What is the difference between a Data Owner and a Data Steward?

What is the difference between a Data Owner and a Data Steward?


What is the difference between a data steward and a data owner? This question comes up over and over again!

There are many different definitions associated with data management and data governance on the internet. Moreover, depending on the company, their definitions and responsibilities can vary significantly. To try and clarify the situation, we’ve written this article to shed light on these two profiles and establish a potential complementarity.

Above all, we firmly believe that there is no idyllic or standard framework. These definitions are specific to each company because of their organization, culture, and their “legacy”.

Data owners and data stewards: two roles with different maturities

The recent appointment of CDOs was largely driven by the digital transformations undertaken in recent years: mastering the data life cycle from its collection to its value creation. To try to achieve this, a simple – yet complex – objective has emerged: first and foremost, to know the company’s information assets, which are all too often siloed. 

Thus, the first step for many CDOs was to reference these assets. Their mission was to document them from a business perspective as well as the processes that have transformed them, and the technical resources to exploit them. 

This founding principle of data governance was also evoked by Christina Poirson, CDO of Société Générale during a roundtable discussion at Big Data Paris 2020. She explained the importance of knowing your data environment and the associated risks to ultimately create value. During her presentation, Christina Poirson developed the role of the Data Owner and the challenge of sharing data knowledge. Part of the business roles, they are responsible for defining their datasets as well as their uses and their quality level, without questioning the Data Owner:

“The data in our company belongs either to the customer or to the whole company, but not to a particular BU or department. We manage to create value from the moment the data is shared”.  

It is evident that the role of “Data Owner” has been present in organizations longer than the “Data Steward” has. They are stakeholders in the collection, accessibility and quality of datasets. We qualify a Data Owner as being the person in charge of the final data. For example, a marketing manager can undertake this role in the management of customer data. They will thus have the responsibility and duty to control its collection, protection and uses.

More recently, the democratization of data stewards has led to the creation of dedicated positions in organizations. Unlike a Data Owner and manager, the Data Steward is more widely involved in a challenge that has been regaining popularity for some time now: data governance.

In our articles, “Who are data stewards” and “The Data Steward’s multiple facets“, we go further into explaining about this profile, who are involved in the referencing and documenting phases of enterprise assets (we are talking about data of course!) to simplify their comprehension and use.

Data steward and data owners: two complementary roles?

In reality, companies do not always have the means to open new positions for Data Stewards. In an ideal organization, the complementarity of these profiles could tend towards :  

A data owner is responsible for the data within their perimeter in terms of its collection, protection and quality. The data steward would then be responsible for referencing and aggregating the information, definitions and any other business needs to simplify the discovery and understanding of these assets.

Let’s take the example of the level of quality of a dataset. If a data quality problem occurs, you would expect the Data Steward to point out the problems encountered by its customers to the Data Owner, who is then responsible for investigating and offering corrective measures.

To illustrate this complementarity, Chafika Chettaoui, CDO at Suez – also present at the Big Data Paris 2020 roundtable – confirms that they added another role in their organization: the Data Steward. According to her and Suez, the Data Steward is the person who makes sure that the data flows work. She explains:

“The Data Steward is the person who will lead the so-called Data Producers (the people who collect the data in the systems), make sure they are well trained and understand the quality and context of the data to create their reporting and analysis dashboards. In short, it’s a business profile, but with real data valence and an understanding of data and its value”. 

To conclude, there are two notions regarding the differentiation of the two roles: the Data Owner is “accountable for data” while the Data Steward is “responsible for” the day-to-day data activity.

How to deploy effective data governance, adopted by everyone

How to deploy effective data governance, adopted by everyone


It is no secret that the recent global pandemic has completely changed the way people do business. In March 2020, France was placed in total lockdown, and many companies had to adapt to new ways of working, whether that be by introducing remote working, changing the production agenda, or even shutting down the organization’s operations completely. This health crisis had companies ask themselves: how are we going to deal with the financial, technological and compliance risks following COVID-19?

At Big Data Paris 2020, we had the pleasure to attend the roundtable “How to deploy effective data governance that is adopted by everyone” led by Christina Poirson, CDO of Société Générale, Chafika Chettaoui, CDO of the Suez Group and Elias Baltassis, Partner & Director, Data & Analytics of the Boston Consulting Group. In this roundtable of approximately 35 minutes, the three data experts explain the importance and the best practices of implementing data governance. 


First steps to implementing data governance

The impact of Covid-19 has not been without underlining the essential challenge of knowing, collecting, preserving and transmitting quality data. So, has the lockdown pushed companies to want to put in place a data governance strategy? This first question, answered by Elias Baltassis, confirms the strong increase in demand for implementing data governance in France:

“The lockdown certainly accelerated the demand for implementing data governance! It was already a topic for the majority of these companies long before the lockdown, but the health crisis has of course pushed companies to strengthen the security and reliability of their data assets”.

So what is the objective of data governance? And where do you start? Elias explains that the first thing to do is to diagnose the data assets in the enterprise, and identify the sticking points: “Identify the places in the enterprise where there is a loss of value because of poor data quality. This is important because data governance can easily drift into a bureaucratic exercise, which is why you should always keep as a “guide” the value created for the organization, which translates into better data accessibility, better quality, etc”. 

Once the diagnosis is done and the sources of value are identified, Elias explains that there are four methodological steps to follow:

  1. Know your company’s data, its structure, and who owns it (via a data glossary for example),
  2. Set up a data policy targeted at the points of friction,
  3. Choose the right tool to deploy these policies across the enterprise
  4. Establish a data culture within the organization, starting with hiring data-driven people, such as Chief Data Officers. 

The above methodology is therefore essential before starting any data governance project which, according to Elias, can be implemented fairly quickly: “Data governance can be implemented quickly, but increasing data quality will take more or less time, depending on the complexity of the business; a company working with one country will take less time than a company working with several countries in Europe for example”. 


The role of the Chief Data Officer in the implementation of data governance

Christina Poirson, explains that for her and Société Générale, data governance played a very important role during this exceptional period: “Fortunately, we had data governance in place that ensured the quality and protection of data during lockdown to our professional and private customers. We realized the importance of the couple digitization and data, which has been vital not only for our work during the crisis, but also for tomorrow’s business.

So how did a company as large, old and with thousands of data records as Société Générale implement a new data governance strategy? Christina explains that data at Société Générale is not a recent topic. Indeed, since the very beginnings, the firm has been asking for information about the client in order to be able to advise them on what type of loan to put in place, for example. 

However, Société Générale’s CDO tells us that today, with digitization, there are new types, formats and volumes of data. It confirms what Elias Baltassis said just before: “The implementation of a data office and Chief Data Officers was one of the first steps in the company’s data strategy. Our role is to maximize the value of data while respecting the protection of sensitive data, which is very important in the banking world!”.

To do this, Christina explains that Société Générale supports this strategy throughout the data’s life cycle: from its creation to its end, including its qualification, protection, use, anonymization and destruction.

On the other hand, Chafika Chettaoui, CDO of the Suez group, explains that she sees herself as a conductor:

“What Suez lacked was a conductor who had to organize how IT can meet the business objectives. Today, with the increasing amount of data, the CDO has to be the conductor for the IT, business, and even HR and communication departments, because data and digital transformation is above all a human transformation. They have to be the organizer to ensure the quality and accessibility of the data as well as its analysis”.

But above all, the two speakers agreed that a CDO has two main missions:

  • The implementation of different standards on data quality and protection,
  • Must break down data silos by creating a common language around data, or data fluency, in all parts of the enterprise.

Data acculturation in the enterprise

We don’t need to remind you that building a data culture within the company is essential to create value with its data. Christina Poirson explains that data acculturation was quite a long process for Société Générale: 

“To implement data culture, we went through what we call “data mapping” at all levels of the managerial structure, from top management to employees. We also had to set up coaching sessions, coding training and other dedicated awareness sessions. We have also made available all the SG Group’s use cases in a catalog of ideas so that every company in the group can be inspired: it’s a library of use cases that is there to inspire people”. 

She goes on to explain that they have other ways of acculturating employees at Société Générale:

  • Setting up a library of algorithms to reuse what has already been set up
  • Implementing specific tools to assess whether the data complies with the regulations.
  • Making data accessible through a group data catalog

Data acculturation was therefore not an easy task for Société Générale. But, Christina remains positive and tells us a little analogy: “Data is like water, CIOs are the pipes, and businesses make demands related to water. There must therefore be a symbiosis between the IT, CIO and the business departments”. 

Chafika Chettaoui adds: “Indeed, it is imperative to work with and for the business. Our job is to appoint people in the business units who will be responsible for their data.  We have to give the responsibility back to everyone: the IT for building the house, and the business for what we put inside. By putting this balance in place, there are back and forth exchanges and it is not just the IT’s responsibility”.


Roles in Data Governance

Although roles and responsibilities vary from company to company, in this roundtable discussion, the two Chief Data Officers explain how role allocation works within their data strategy. 

At Société Générale they have fairly strong convictions. First of all, they set up “Data Owners”, who are part of the business, who are responsible for :

  • the definition of their data
  • their main uses
  • their associated quality level

On the other hand, if a data user wants to use that data, they don’t have to ask permission from the Data Owner, otherwise the whole system is corrupt. As a result, Société Générale has put in place measures to ensure that they check compliance rules and regulations, without calling the Data Owner into question: “the data at Société Générale belongs either to the customer or to the whole company, but not to a particular BU or department. We manage to create value from the moment the data is shared”.  

At Suez, Chafika Chettaoui confirms that they have the same definition of Data Owner, but she adds another role, that of the Data Steward. At Suez, the Data Steward is the one who is on site, making sure that the data flows work.

She explains: “The Data Steward is someone who will animate the so-called Data Producers (the people who collect the data in the systems), make sure they are well trained and understand the quality of the data, as well as be the one who will hold the dashboards and analyze if there are any inconsistencies. It’s someone in the business, but with a real data valency and an understanding of the data and its value”. 

What are the key best practices for implementing data governance?

What should never be forgotten in implementing data governance is to remember that data does not belong to one part of the organization but must be shared to all. It is therefore imperative to standardize the data. To do this, Christina Poirson explains the importance of a data dictionary: “by adding a data dictionary that includes the name, definition, data owner, and quality level of the data, you already have a first brick in your governance”. 

As mentioned above, the second good practice in data governance is to define roles and responsibilities around data. In addition to a Data Owner or Data Steward, it is essential to define a series of roles to accompany each key stage in the use of the data. Some of these roles can be :

  • Data Quality Manager
  • Data Protection Analyst
  • Data Usages Analyst 
  • Data Analyst
  • Data Scientist
  • Data Protection Officer
  • etc

As a final best practice recommendation for successful data governance, Christina Poirson explains the importance of knowing your data environment, as well as your risk appetency, the rules of each business unit, industry and service to truly facilitate data accessibility and compliance. 

…and the mistakes to avoid?

To end the roundtable, Chafika Chettaoui talks about the mistakes to avoid in order to succeed in governance. According to her, we must not start with technology. Even if, of course, technology and expertise are essential to implementing data governance, it is very important to focus first on the culture of the company. 

She states: “Establishing a data culture with training is essential. On the one hand we have to break the myth that data and AI are “magical”, and on the other break the myth of “intuition” of some experts, by explaining the importance of data in the enterprise. The cultural aspect is key, and at any level of the organization. ” 

Data Governance Tool: Lean Data Governance Canvas

Data Governance Tool: Lean Data Governance Canvas

lean data governance canvas

Inspired by Ash Maurya’s “Lean Canvas” business model, Zeenea’s Lean Data Governance Canvas is intended for Data Managers whose missions are to clarify and orchestrate data governance within their organizations. From a methodological point of view, the Lean Data Governance Canvas is composed of two main parts:

  • The elements on the left that represent those of a governance system
  • The elements on the right that are inherent to an organization

It’s important to know that this Lean Data Governance Canvas is a toolkit for implementing data governance. The intervenors will have to iterate in order to conduct a LDGC to have the least number of assumptions as possible over time.

However, be aware! There must not be one unique canvas that represents the entirety of the enterprise but rather, there must be separate ones for strategic and operational levels.


The insights highlighted in the Lean Canvas will have to be consistent and respect the company’s strategic objectives.

Method: Lean Data Governance Canvas


0- Strategic objectives

Before beginning your journey with the Lean Data Governance Canvas, it is important to highlight the enterprise’s strategic expectations and ask yourself:

What are the enterprise’s and the board’s strategic objectives? How does it apply to the data and IT department?


1 & 2 – Segment data citizens & problems

Start by thinking about a type of persona. After this, you can take the time to come up with up to 3 challenges that this group faces:

Who are the data citizens you wish to address?
What are the top 3 problems/ risks that data governance seeks to solve for the defined data citizen segment?

Your data citizens are either the ones in charge of your data governance (Data Owners, Data Managers, IT Custodians, etc.) or the producers/consumers of data (Management, Supply-chain, CRM, Data Science, Marketing, etc). Your risks may concern one or more of these personas.


3 – Regulatory Compliance

Digital transformation brings about more regulatory compliances (like the GDPR for example). To keep your constraints in mind, write down your regulatory requirements and ask yourself this question:

What are the risks stemming from regulatory (including supervisory) requirements?


4 – Value Proposition

This part of the LDGC focuses on the value that data governance will bring to the segmented data citizens.

Why push back data governance implementation for the defined data citizen segments?

The value proposition is unique, congruent and engaging for concerned data citizens. Communication or marketing support can sometimes be a valuable aid in formalizing a value proposition. Do not hesitate to get closer to the relevant internal teams.


5 – Solutions

In this section, the means and principles are defined, ones that will make it possible to overcome the problems of your data citizen segments and veer towards the value proposition. Without going into too much detail:

What are the 3 main principles that will answer the data citizen segments problems?

In this Canvas, a solution must not take into account what already exists and is not determined according to time or budget. The Canvas is not a project of timing, but an upcoming project that must be considered as an MVP (minimum viable product) for a first milestone.


6.1 – Targeted metrics

These indicators define the performance of the established data governance in the data citizen segment. They will measure the resolution of the problem and the value of your governance rules.

What key indicators should be measured to validate the progress of the sought-out value proposition?

6.2 – Connectivity metrics

These metrics are indicators that define the implemented data governance performance on the sources of information that your previously listed.

What key indicators should be measured to validate the performance of the data governance rules on a source?

7 – Data sources

What are the “absolutely necessary” data sources that will bring the most value at the start of your defined data citizen segments?

Data sources are valuable assets for data-centric teams. The goal therefore, is to find the value. Mass-production and exhaustiveness induce an immediate complexity that can not be easily controlled. The choice must be made based on the data’s value according to the uses of business.


8 – Technological needs

Identify the technological needs that must be acquired to measure governance metrics and/or achieve the value proposition.

What are the technologies and tools needed to measure the associated metrics?

9 – People needs

Identify the skills and resources to bring data governance to life, animate and measure it within the targeted data segment.

Who are the people concerned and what tips and interactions are necessary to strive for the value proposition and its maintenance?

The evolution of the Lean Data Governance Canvas over time

After focusing on these first steps, it is important to test it! We encourage Lean Data Governance Canvas users to rework the canvas as much as possible – through iteration – and testing them, after which a winning data governance model should appear. Despite the difficulty of these workshops, we are convinced that this work will save you time, energy and money. Think about it, with thee Lean Data Governance Canvas, it is possible to build something everyone in the enterprise wants and respects.

Download our data governance toolkit

At Zeenea, we use this tool, among others, to set up data governance programs. Thanks to our metadata management platform, connect to all your data sources by automatically importing and updating your data into a central repository. Our tool allows anyone – with the necessary capabilities – to discover, understand and trust in the enterprise’s data assets.

Companies rely on Zeenea to meet the challenges of setting up effective data governance in a lean startup environment: to promote the use of data internally while limiting the risks!

Data Governance and Data from ERP/CRM packages: a Must Have

Data Governance and Data from ERP/CRM packages: a Must Have

For the last 3 decades, companies have been relying on ERP and CRM packages to run their operations.

In response to the need to comply with regulations, reduce risk, improve profitability, competitiveness and customer engagement, they have to become Data Driven.

In addition to the need of leveraging a wide variety of new data assets produced heavily by new means, strategic data from those historical systems have to be involved in any Data Initiative.


Challenges faced by companies trying to leverage data from ERP/CRM to feed their digital initiatives

In the gold rush that companies are pursuing with Artificial Intelligence, Advance Analytics and in any Digital Transformation program, understanding and leveraging Data from ERP/CRM packages is in the critical path in any Data Governance journey.

First, they have large, complex, hard to understand and customized database models. Understanding the descriptions, the relationship definitions and more means to serve Data Citizens is almost impossible without an appropriate Data Catalog like Zeenea with ad hoc ERP/CRM connectors.

As an example, SAP has more than 90.000 table sets. As a consequence, a Data Scientist will hardly understand the so called TF120 table in SAP or the F060116 in JD Edwards.

Secondly, identifying a comprehensive subset of accurate datasets to serve a specific Data initiative is an obstacle course.

Indeed, a big percentage of the tables in those systems are empty, may appear redundant or have complex links for those who are not experts of the ERP/CRM domain.

Thirdly, the demand for fast, agile and ROI focused Data Driven initiatives put the ERP/CRM knowledgeable personnel in the middle of the game.

ERP/CRM experts are rare, busy and expensive workers and companies cannot afford increasing those team or having them losing their focus.

And finally, if a Data Catalog is not able to properly store Metadata information for those systems, in a smooth, comprehensive and effective way, any data initiative will be deprived of a large part of its capabilities.

The need for financial data, manufacturing data and customer data to take a few examples is obvious and therefore put ERP/CRM systems as mandatory data sources of any Meta Data Management program.

Zeenea value proposition

An agile and easy way

At Zeenea, we believe in a Data Democracy world, where, any employee of a company can discover, understand and trust any dataset that is useful.

This is only possible with a reality proof data catalog easily and straightforwardly connecting to any data source, including the ones from ERP/CRMP packages.

But mostly, a Data Catalog has to be smart, easy to use, easy to implement and easy to scale in a complex IT Landscape.

A wide connectivity

Zeenea provides Premium ERP/CRM connectors for the following packages:

  • SAP and SAP/4HANA
  • SAP BW
  • Salesforce
  • Oracle E Business Suite
  • JD Edwards
  • Siebel
  • Peoplesoft
  • MS Dynamics EX
  • MS Dynamics CRM

Zeenea “Premium ERP/CRM connectors help companies in various aspects.

Discovering and assessing

Zeenea connectors help companies to build an automatic translation layer, hiding the complexity of the underneath database tables and automatically feeds the Metadata registry with accurate and useful information, saving time and money of the Data Governance Team.


Scoping useful metadata information for specific cases.

In a world with thousands of datasets, Zeenea provides a mean to build accurate and self-sufficient models to serve focused business needs by extracting in a comprehensive way: 

  • Business and technical names for tables
  • Business and technical names for columns in tables
  • Relationships between tables
  • Data Elements
  • Domains
  • Views
  • Indexes
  • Table row count
  • Application hierarchy (where available from the package) 


Zeenea’s “Premium ERP/CRM connectors” are able to identify and tag any personal data or Personal Identifiable Information coming from its supported CRM/ERP packages in its Data Catalog to stick with GDPR/CCPA regulation.


What is metadata management?

What is metadata management?

“By 2021, organizations will spend twice as much effort in managing metadata compared with 2018 in order to assess the value and risks associated with the data and its use.”

*Gartner, The State of Metadata Management

The definition of metadata management

As mentioned in our previous article “The difference between data and metadata”, metadata provides context to your data. And to trust your data’s context, you must understand it. Knowing the who, what, when, where, and why of your data means knowing your metadata, otherwise known as metadata management.

With the arrival of Big Data and the various regulations, data leaders must look further into their data through metadata. Metadata is created whenever data is created, added, deleted from, updated or acquired. For example, metadata in an Excel spreadsheet includes the date of creation, the name, the associated authors, the file size, etc. In addition, metadata could also include titles and comments made in the document.

In the past, a form of metadata management would be look up a book’s call number in a catalog to find its location in a library. Today, metadata management is used in software solutions to comply with data regulations, set up data governance as well as understand the data’s value. Thus, this discipline becomes essential for enterprises!

Why should you implement a metadata management strategy?

The first use case regarding metadata management is to facilitate the discovery and understanding of a person’s or program’s specific data asset.

This requires setting up a metadata repository, populating and generating easy to use information in it.

Here are, among others, benefits of metadata management:

    • A better understanding of the meaning of enterprise’s data assets,
    • More communication on a data’s semantics via a data catalog,
    • Data leaders are more efficient, leading to faster project delivery,
    • The use of data dictionaries and business glossaries allow the identification of synergies and the verification of coherent information,
    • Reinforcement of data documentation (deletions, archives, quality, etc.),
    • Generate audit and information tracks (risk and security for compliance).

Manage your metadata with Zeenea’s metadata management platform

With Zeenea, transform your metadata into exploitable knowledge! Our metadata management platform automatically curates and updates your information from your storage systems. It becomes a unique, up-to-date source of knowledge for any data explorer in the enterprise.

[Infographic] What is a Data Democracy?

[Infographic] What is a Data Democracy?

“What distinguishes Web Giants is not the structure of their governance but the culture that irrigates and animates this organization.”

A democracy, simply put, is a form of government where all people have the authority to choose their legislation. Well when referring to data, data democracy refers to all people having the authority to access and understand their enterprise’s information.

Data Democracy is not a form of governance

Contrarily as the name suggests, Data Democracy is not a governance model.

It is definitely not a model in which the rules governing data distribution would be put to vote and defined according to a majority approach. Nor is it an organization in which Data Stewards are elected as “data representatives” into an electoral body of the enterprise.

A Data Democracy refers to a corporate culture, an open model where liberty goes hand in hand with responsibility. Its main objective is to make a company’s data widely accessible to the greatest number of people, if not to all. In practice, any employee is able to pull data values at any level.


How did Data Democracy come about?

To understand what Data Democracy is, it is important to know about the other data cultures. These are:

  • Data Anarchy: a system where operational professions develop their own clandestine base (“shadow IT”) which serves their immediate interests.
  • Data Monarchy: a system that has a very strong asymmetry in data access depending on hierarchical position.
  • Data Aristocracy: A system characterized by a more significant degree of freedom than in Data Monarchy, but which is solely reserved to a very select subset of the population.
  • and finally Data Democracy.

Learn more about the different data cultures

In this case, the mantra of Data Democracy is to open up the potential offered up by data to as many people as possible. This freedom of access offers a maximum of opportunities to create value for the company; it provides every employee with the ability, at their level, to use all accessible and compatible resources within their needs in order to produce value locally.

This freedom only works if the regulations and the basic tools are implemented, and each employee is responsible for how they use their data. Therefore, the distribution of necessary and sufficient information is required to allow employees to male proper use of it while adhering to regulations.


The rights & duties of Data Democracy : infographic

The democratic approach presents an interesting challenge to balance: on the one hand, you must ensure that the right to use data can truly be exercised, and on the other hand you must counterbalance this right with a certain number of duties.

The right granted to every employee to use the organization’s data for their own activities is only tangible from the moment that this employee has the necessary information to identify and localize the data they may need.

In return, employees must also be made aware of the responsibilities that they must assume when they wish to make use of the data. They can also be adapted to the context of the company and to the nature of the data offered.


Here a list of some of the rights & duties in infographic form.

Download our white paper on Data Democracy

The adoption of such a culture can only work if everyone benefits. To learn more about how Data Democracy is constructed and its benefits, download our white paper:

“How does Data Democracy strengthen Agile Data Governance?”

What Data Governance strategy is best suited for your sector?

What Data Governance strategy is best suited for your sector?

In our previous article we explained why data governance is critical for enterprises. We also talked about the differences between defensive and offensive data governance to achieve your data strategy.

In this new article, we want to focus on what kind of data governance you need and its trend according your business sector.

When it comes to implementing data governance, the approach an enterprise will take on and where they fit on the data strategy spectrum is solely based on their business environment. Indeed, a data governance strategy isn’t set up in a unique way: it’ll change from one sector to another.

Here we’ve taken a look at three different sectors: the healthcare/hospital sector, the retail/eCommerce sector and the banking/insurance sector. We’ve identified which approach each of these industries takes on today, and how it will differ from tomorrow.

Healthcare & Data Governance

As hospitals have a highly regulated environment, their Data Governance strategy will take on a very defensive approach.

Indeed, today in the healthcare department, data quality and protection are essential. This sector has numerous amounts of sensitive information regarding their patients. Their data is used to save lives, treat specific symptoms, find new cures…So, bad data can lead to big risks!

However, with the rise of “Healthtech” companies, the industry is becoming more and more competitive. Hospitals and other healthcare businesses will have to merge towards a more offensive strategy in the years to come. This sector will have to balance between keeping their defensive approach to their data, and continuously innovating on their services in order to keep up with competition.

Retailers / eCommerce & Data Governance

On the other end of the spectrum, retailers and eCommerce actors take on an offensive data governance strategy.

This industry sees new actors come into the market every year, even every month! With all of this competition, it is crucial for retailers and online businesses to produce value with their data in order to offer personalized services, innovative products, etc.

With that said, consumers are now more and more protective with their data. With the rise of Machine Learning and AI techniques, people are questioning the ethics around their data’s uses. Also, the increasing rules and regulations regarding data privacy and security are forcing enterprises in this sector to change their data management strategies to comply with these new laws.

In the future, retailers will have to take on a defensive approach while still continuously producing value and innovating.

Banks / Insurance & Data Governance

The banking and insurance industries are right in the middle.

Both require strong defensive data governance because of the regulatory pressures they face. These sectors also work with very personal data, so it is essential to implement a strong defensive strategy.

Regardless, the banking and insurance industries are seeing new actors come in the market. With these new online infrastructures, digital services, and fully personalized options, this sector is seeing a rapid shift in data governance strategies.

Banks and insurance companies will have to not only keep focusing on their data quality and security but also take on a more offensive approach to their data in order to face these new web giants and offer innovative products and services.

How to start implementing a data governance strategy?

Getting started with implementing Data Governance can be a very overwhelming task!

At Zeenea, we’ve created a Lean Data Governance Canvas to help you ask and answer the right questions!

Just like the famous “Lean Canvas”, we’ve re-arranged elements to help enterprises set up “Lean” Data Governance and help your business find the right approach to take on data governance.

Why is Data Governance critical for your enterprise?

Why is Data Governance critical for your enterprise?

>> Re-watch our webinar <<

Data Governance was a trending topic in 2019! Enterprises dealing with their data are realizing how important it is to implement this discipline in order to effectively and efficiently manage their data assets.

To fully understand what Data Governance is, many definitions exist:

“Data governance is a quality control discipline for adding new rigor and discipline to the process of managing, using, improving and protecting organizational information.”
IBM Data Governance Council

“Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.”
Dama DMBok

“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
The Data Governance Institute

“Data governance is the formal orchestration of people, processes, and technology to enable an organization to leverage their data as an enterprise asset.”
MDM Institute

What is certain is that these definitions are far from being fun! But before we get into defining what data governance is, let’s see the reasons why it has become a strategic subject for enterprises.

What drives enterprises to implement Data Governance?


In our experience, we’ve learned that enterprises tend have one or several of these issues within their organizations:

Tribal knowledge: usually enterprises have a person or select group of people who produce, work with and understand their data assets. However, the rest of the organization has no knowledge on their data (where it comes from, its value, its quality, etc.). This results in enterprises having siloed information that is difficult to use and share.

Big mess: In the last decade, many complicated information systems have appeared resulting in enterprises having massive amounts of unorganized data. Data users are therefore subjected to data where their quality, uses or even location is unknown.

Compliance: All enterprises are subjected to some form of compliance may it be data privacy, general data usage or ethics. Those that do not have governance in their organizations will suffer from these rules and regulations.

Implementing Data Governance therefore helps enterprises resolve these problems!

With Data governance, enterprises are able to create a data fluent organization, organize their data and comply to the increasing regulatory demands.

Why Data Governance fails in enterprises

However, enterprises often look past implementing Data Governance. These organizations believe that:

  • Governance implies control and not value
  • It slows down business
  • It is more of an IT concern than a business concern
  • Governance projects implemented in the past have failed too many times before
  • It is too big, there are no resources available
  • It’s nice to have, but not a priority


What strategy for your Data Governance?

With all of that said, it is essential for enterprises to build the right sized Data Governance. There is not a unique way of implementing data governance: enterprises must know what kind of governance they need, the right style, and where they stand in the governance landscape.

We’ve identified two types of strategies when to comes to implementing Data Governance.

Defensive Data Governance

This dimension of Data Governance is more focused on risk control and risk management. Here, enterprises are making sure they respect data compliance (such as GDPR), privacy, and security. This framework goes hand in hand with some of the definitions seen above where it is more about “control”.

Offensive Data Governance

This dimension is more focused on producing value with data. With an offensive approach to Data Governance, enterprises are prioritizing value production and innovation.

What is the difference between Metadata and Data?

What is the difference between Metadata and Data?

“Data is content, and metadata is context. Metadata can be much more revealing than data, especially when collected in the aggregate.” 

— Bruce Schneier, Data and Goliath

Definitions of Data and Metadata

For the majority of people, the concepts of Metadata and Data are unclear. Even though both are a form of data, their uses and specifications are completely different.

Data is a collection of information such as observations, measurements, facts, and descriptions of certain things. It gives you the ability to discover patterns and trends in all of an enterprise’s data assets.

On the other hand, Metadata, often defined as “data on data”, refers to specific details on these dataIt provides granular information on one specific data such as file type, format, origin, date, etc.

Key differences between data and metadata

The main difference between Data and Metadata is that data is simply the content that can provide a description, measurement, or even a report on anything relative to an enterprise’s data assets. On the other hand, metadata describes the relevant information on said data, giving them more context for data users.

Some data is informative and some may not be; such as “raw” data (numbers, or non-informative characters). However, metadata is always informative as it is a reference to other data.

Finally, data can or cannot be processed, as raw data is always considered unprocessed data. The difference with metadata is that metadata is always considered to be processed information.

Why is metadata important for Data Management?

When data is created, so is metadata (its origin, format, type, etc.). However, this type of information is not enough to properly manage data in this expanding digital era; data managers must invest time in making sure this business asset is properly named, tagged, stored, and archived in a taxonomy that is consistent with all of the other assets in the enterprise. This is what we call, “metadata management.”

With better metadata management comes better data value. This metadata allows for enterprises to assure greater Data quality and discovery, allowing data teams to better understand their data. Without metadata, enterprise find themselves with datasets without context, and data without context has little value.

This is why having a proper metadata management solution is critical for enterprises dealing with data. By implementing a metadata management platform, data users are able to discover, understand, and trust in their enterprise’s data assets.

Are you looking for a metadata management solution?

The Chief Data Officer’s evolution to a Data Democracy Sponsor

The Chief Data Officer’s evolution to a Data Democracy Sponsor

Under the pressure of digital transformation, Chief Data Officers (CDO) have appeared within large companies. According to Gartner, 90% of large companies will have a CDO by the end of 2019.

The thousands of CDOs appointed in the course of the past few years were in charge of improving efficiency and capacity to create value for their organization’s information ecosystem. That is to say, they were invited to direct their organization in processing and exploiting information with the same discipline as the other, more traditional, assets.

Companies who valorize their information assets surpass their rivals in using them to reinvent, digitize, or eliminate existing processes or products.

The CDO’s missions can be summarized as exploiting and finding uses for corporate data as well as being in charge of developing the use of and trust of employees regarding internal enterprise data. As we have seen, these missions often collide with the powerful cultural restraints within organizations.

How have the Chief Data Officer’s missions evolved?

The CDO has many responsibilities. Gartner identified the main responsibilities of a CDO during their 2019 Data & Analytics event in London. These are, among others:

  • Defining a data and analytics strategy in their organization
  • Supervising operational initiatives in response to the established upstream strategy
  • Ensuring information made available on data is trustworthy and valuable
  • Constructing data governance
  • Creating business value in data analytics
  • Managing efforts regarding data science
  • Operating and maintaining efforts in infrastructure in response to data analysis needs
  • Etc.

We believe that this impressive list of responsibilities is complemented by another, which could serve as a common thread for all the others and facilitate them: promoting Data Democracy and supporting cultural changes.

At first, CDOs had to lead a mission to convince interest organizations to exploit data. The first few years of this mission were often supported by the construction of a data universe adapted to new uses, often in the form of a Data Lake or Data Mart. The investments agreed upon to construct these data universes were significant but often reserved to specialists. In brief, organizations had more so implemented Data Aristocracies rather than Data Democracies.

The CDO towards a new role

 With the exponential development of data, the role of the CDO took a new scope. From now on CDOs must reconsider the organization in a cross-functional and globalizing way. They must become the new leaders in Data Democracy within companies and respond to the call of numerous data citizens who have understood that the way in which data is processed must change radically. The new CDOs must break the bonds of data silos.

In order to obtain the support for data initiatives from all employees, they must not only support them in understanding data (original context, production, etc.) but also help them to invest in the production strategy and the exploitation of data.

From now on, the involvement of stakeholders in the exploitation of data must extend to all levels of the enterprise. It is by facilitating understanding, exchanges, and access around data that organizations will become data-driven.

Download our white paper “How does Data Democracy strengthen Agile Data Governance?”

In order not to commit every employee to a level which is above them, and to respect their desires and limitations, a participatory approach will lead to the implementation of multi-disciplinary teams that will welcome the necessary skills and adequate positions for the deployment of agile data governance. Read more about the Chief Data Officer new role in governance in our white paper.

    Understanding the different Data Cultures

    Understanding the different Data Cultures

    Just like corporate or organizational culture, each enterprise that deals with data has its own data culture. We believe that what distinguishes Web Giants isn’t the structure of their governance, but the culture that irrigates and animates this organization.

    At Zeenea, we believe in putting in place a Data Democracy. It refers to corporate culture, an open model where freedom rhymes with responsibility.

    To better understand Data Democracy, it is necessary to compare it to other data cultures. Here are the main data cultures:


    Data Anarchy

    In this system, operational professions feel poorly served by their IT departments, and each one develops its own clandestine base (shadow IT) which serves their immediate interests while freeing them from all control regulations and conformity to standards. In 2019, this culture brings sizeable risks: data leaks, contravention of ethical regulations, service quality degradation, reinforcement of silos, etc.


    Data Monarchy

    This system translates to a very strong asymmetry in data access depending on the hierarchical position. Data, here, is very strictly controlled; its consolidation level is carefully aligned with the organizational structure, and its distribution is very selective.

    This monarchical culture prevailed for a long time in Business Intelligence (BI) projects: data collected in data warehouses were carefully controlled, then consolidated in reports where access was reserved to a few select people who were close to decision-making bodies. This method promotes a “top-down” approach and willingly encourages a defensive strategy, where rules, restrictions, and regulations insulate data. Its main theoretical benefit is the almost infallible control over corporate data, but that translates into very limited access to data, only reserved to certain privileged groups.


    Data Aristocracy

    A Data Aristocracy is characterized by a more significant degree of freedom than in Data Monarchy, but which is solely reserved to a very select subset of the population, mainly expert profiles such as Data Engineers, Data Analysts, Data Scientists, etc. This aristocratic approach is often the one that brings the most successful data governance projects to the surface.

    Such a culture can be favorable to more offensive strategies, as well as to heterogeneous one, combining top-down and bottom-up. However, it deprives the majority of employees access to data and thus, a certain number of possible innovations and valorizations.

    Data Democracy

    Data Democracy’s main objective is to make a company’s data widely accessible to the greatest number of people, if not to all. In practice, every employee is able to pull data values at any level. This freedom of access offers maximum opportunities to create value for the company; it provides each employee with the ability, at their level, to use all accessible and compatible resources within their needs in order to produce locally, and through a trickle effect, it will benefit the entire company.

    This freedom only works if the regulations and the basic tools are implemented, and each employee is responsible for how they use their data. Therefore, the distribution of necessary and sufficient information is required to allow employees to make proper use of it while adhering to regulations.t

      Download our white paper “How does Data Democracy strengthen Agile Data Governance?”

      The democratic data culture presents an interesting challenge to balance: on one hand, you must ensure that the right to use data can truly be exercised, and on the other hand you must counterbalance this right with a certain number of duties. Find out how to construct a democratic data culture in our white paper, “How does Data Democracy strengthen Agile Data Governance?”.

        Data Stewardship and Governance: The Data Steward’s Multiple Facets

        Data Stewardship and Governance: The Data Steward’s Multiple Facets

        Where Stewardship refers to the taking care of and the supervision of a specific property or organization, Data Stewardship refers to data supervision. Initially, the idea was that a domain expert would be in charge with qualifying and documenting data from their professional standpoint. In fact, Data Stewards are those who work closest to where the data is collected; they are often those who best understand the different aspects of data and the standards to which they must adhere to.


        Data stewardship and governance: the responsibilities

        In practice, Data Stewardship covers a wide range of responsibilities, depending on the maturity level of the organization. We can organize these responsibilities in four broad categories:

        Operational supervision and quality

        This refers to monitoring and supervising the complete life cycle of a dataset. 

        More specifically, Data Stewards must define, and therefore implement, the necessary processes for the acquisition, storage, and distribution of datasets.

        They must also ensure that the data produced fulfills the quality criteria that were defined (values, gaps, completeness, freshness, etc.) and that the procedures are put into place to evaluate and correct potential quality problems.


        A Data Steward is in charge of defining and documenting data and creating a glossary of industry-specific terms.

        They must ensure that each element of a dataset possesses a clear definition and a specific use.

        The documentation constitutes a collection of technical and functional metadata according to a meta model in common principle.

        Conformity and risk management

        Data protection and the management of regulatory risks or ethics is one of the most challenging aspects of the Data Steward’s role.

        The regulatory environment around data is more restrictive and shifting. It’s up to them to ensure that the proliferation of data is framed by a collection of protocols ensuring conformity with the applicable standards – especially regarding privacy protection.

        Security and access control

        Finally, Data Stewards must define the rules governing data access.

        These include the different levels of confidentiality and procedures, allowing the authorization of a person or group to access data.

        Download our white paper “How does Data Democracy strengthen Agile Data Governance?” 

        Orchestrated by a Data Management division, implemented by different types of Data Stewards, data governance must be deployed in an organization. To ensure this deployment, several operational models are conceivable in theory – decentralized, federated, centralized, etc. We think what distinguishes organizations is not the structure of their governance but the underlying culture of this organization. This culture has a name: Data Democracy.

        What is a Chief Data Officer

        What is a Chief Data Officer

        According to a Gartner study presented at the Data & Analytics conference in London 2019, 90% of large companies will have a CDO by 2020!

        With the arrival of Big Data, many companies find themselves with colossal amounts of data without knowing how to exploit them. In response to this challenge, a new function is emerging within these large companies: the Chief Data Officer.

        The Chief Data Officer’s role

        Considered as data “gurus”, Chief Data Officers (CDO) play a key role in an enterprise’s data strategy. They are in charge of improving the organization’s overall efficiency and the capacity to create value around their data.

        In order for CDOs to fulfill their missions, they must reflect on providing high-quality, managed, and secure data assets. In other words, they must find the right balance between an offensive and defensive data governance strategy that matches the enterprise’s needs.

        According to the Gartner study, presented at their annual Data & Analytics event in London in March 2019, the CDO, among other things, has several important responsibilities within a company:

        Define a data & analytics strategy

        What are the short, medium, and long-term data objectives? How can I implement a data culture within my enterprise? How can I democratize data access? How can I measure my data assets quality? How can I attain internal and/or legal regulatory compliance? How can I empower my data users?

        There are so many questions that CDOs must ask themselves in order to implement a data & analytics strategy in their organization.

        Once the issues have been identified, it is time for operational initiatives. A CDO acts as a supervisor so that the efforts made in providing data information are trustworthy and valuable.

        Their role takes shape over time. They must become the new “Data Democracy” leaders within their companies and maintain the investment provided for its infrastructure and organization.

        Build Data Governance

        Implementing data governance must successfully combine compliance with increasingly demanding regulatory requirements and the exploitation of as much data as possible in all areas of an enterprise. To achieve this goal, a CDO must first ask themselves a few questions:

        • What data do I have in my organization?
        • Are these data sufficiently documented to be understood and managed by my collaborators?
        • Where do they come from?
        • Are they secure?
        • What rules or restrictions apply to my data?
        • Who is responsible for them?
        • Who uses my data? And how?
        • How can my collaborators access them?

        It’s by building agile data governance in the most offensive way possible that CDOs will be able to facilitate data access and ensure their quality in order to add value to them.

        Evangelize a “Data Democracy” culture

        Data Democracy refers to the idea that if each employee, with full awareness, can easily access as much data as possible, an enterprise as a whole will reap the benefits. This right to access data comes with duties and responsibilities, including contributing to maintaining the highest level of data quality and documentation. Therefore, governance is no longer the sole preserve of a few, but becomes everyone’s business.

        To achieve this mission, Zeenea connects and federates teams around data through a common language. Our data catalog allows anyone – with the allotted allowances – to discover and trust in an enterprise’s data assets.

        Are you a Chief Data Officer looking for a Data Governance tool?

        In order for Chief Data Officers achieve their objectives, they need to be equipped with the right tools. With Zeenea’s data catalog, CDOs can identify their data assets, make them accessible and usable by their collaborators in order to be valorized.

        Easy to use and intuitive, our data catalog is the CDO’s indispensable tool for implementing agile data governance. Contact us for more information.

        How does Data Democracy strengthen Agile Data Governance?

        How does Data Democracy strengthen Agile Data Governance?

        In 2018, we published our first white paper “Why start an agile data governance?”. Our goal was to present a pragmatic approach on the attributes of such data governance, one that is capable of rising to the challenges of this new age of information:

        We advocate for it to be bottom-up, non-invasive, automated and iterative. In a word, agile.

        In this second edition, we decided to tackle the organization of this new agile data governance and its scaling process using the same mindset.

        We believe that what distinguishes Web Giants in their approach to their data isn’t the structure of their governance but the culture that irrigates and animates their organization.

        This culture has a name: Data Democracy.

        Our white paper will address the following themes:

        Assessing data governance

        Our white paper assess the different governance bodies that we come across in traditional organizations today. The latter often takes on a defensive approach, usually inherited from Master Data Management or from larger initiatives for implementing information systems governance. Very centralized, sometimes bureaucratic, they focus on data control and conformity, often resulting in limiting data access among all company employees.

        The concept of a data democracy

        In order to understand what Data Democracy is, it is important to know that it is not a governance model. Data Democracy refers to a corporate culture, an open model where liberty goes hand in hand with responsibility.

        Data Democracy’s main objective is to make a company’s data widely accessible to the greatest number of people, if not to all. In practice, every employee is able to pull data values at any level.

        A democratic approach presents an interesting challenge to balance: on the one hand, you must ensure that the right to use data can truly be exercised, and on the other hand you must counterbalance this right with a certain number of duties.

        Building a data democracy

        The adoption of a data culture can only work if everyone benefits, hence the importance of communication previously mentioned when discussing rights and responsibilities. The balance between the two must be positive in the end, and governance must not introduce more restrictions than gains. Finally, the results must be made easier.

        To enable everyone to find the necessary information. That is the main objective of a data catalog, which must, even more so than its basic function (referencing data and associated metadata), offer simplicity of use in order to navigate through an ocean of information.

        The new roles of agile data governance

        Under the pressure of digital transformation, new roles appear within large companies.

        The Chief Data Officer: the data democracy sponsor

        Among them, there is the Chief Data Officer, or CDO. They are in charge of improving efficiency and the capacity to create value for the information ecosystem of their organization.

        With the exponential development of data, the role of the CDO took on a new scope.

        From now on, CDOs must reconsider the organization in a cross-functional and globalizing way, and governance and corporate data management technology in enterprises.

        They must become the new leaders in “Data Democracy” within companies and must respond to the call of numerous “data citizens” who have understood that the way in which data is processed must change radically. The new CDOs must break the bonds of data silos.

        Are we all Data Stewards?

        The concept of Data Stewardship stems from a much more traditional model. The organizations that already have Data Stewards tend to be quite large and established.

        Everyone who uses sensitive data engages their responsibility regarding the way they use it. The regulations for the protection of sensitive data – regulatory or internal – must be applied in the same manner for all those who enter contact with it.

        This dedication to involving everyone helps distribute responsibility for data, giving a broader sense of ownership, which encourages users to explore data themselves, and lastly decompartmentalizes data.

        Download our white paper: “How does Data Democracy strengthen Agile Data Governance?

        To know more about the organization of agile data governance, the definition of Data Democracy, and its new roles, download our second edition “How does Data Democracy strengthen agile data governance?”

        Iterative Governance – Agile Data Governance attribute (5/5)

        Iterative Governance – Agile Data Governance attribute (5/5)

        The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.

        The following articles explain the characteristics of a data governance labeled as agile in order to:

        1. Be as close as possible to your enterprise’s operational reality.
        2. Adapt to your enterprise’s context and not the other way around.
        3. Accurately reflect your data assets.
        4. Unify and involve your collaborators.
        5. Respond to changes quickly.

        The implementation of data governance must not take the form of a five-year plan where deliverables hardly see the day. It must avoid the Big Bang effect and adopt an approach influenced by “agile” methods used in the software development sector.

        The enterprise must adopt an iterative approach concerning the implementation of data governance. 

        This approach rests on the concept of validity verification, experimentation, and iterative design.

        We think that a data governance project must start by curating of data assets, in a cross-functional way. By adopting the Pareto principle, collect, document, and manage the 20% of data that will generate 80% of business value within your organization.

        By gradually increasing its reach across your different data segments, by redefining the roles and responsibilities within your organization, and the rules of data management, you will begin to seek a satisfying governance.

        This flexibility also encourages the emergence of a strong data culture within your organization.

        Download our white paper

        Why start an agile data governance?

        Collaborative Governance – Agile Data Governance attribute (4/5)

        Collaborative Governance – Agile Data Governance attribute (4/5)

        The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.

        The following articles explain the characteristics of a data governance labeled as agile in order to:

        1. Be as close as possible to your enterprise’s operational reality.
        2. Adapt to your enterprise’s context and not the other way around.
        3. Accurately reflect your data assets.
        4. Unify and involve your collaborators.
        5. Respond to changes quickly.

        The consistent practice to have a single person or a single group to arbitrate data governance has become obsolete.

        Data governance must not be the IT’s guarded territory.

        Data circulates in the hierarchy from senior managers to entry-level employees in all departments. Information on how data should be managed and what rules to follow can come from anywhere.

        To create a democracy of data, where all the employees can access the enterprise’s data on a large scale, like how Facebook has done, signifies that employees don’t have to wait to execute projects that can add value.

        This also signifies that data problems are more likely to be discovered and corrected. This is all the more important in an environment where 85% of organizations’ information are redundant, obsolete and trivial, and that 41% of all the stored data are not touched for the past 3 years.

        We believe that the sustainability of data governance must include the creation of communities around the different areas of activity related to data within your organization. This approach aims to put individuals and their interactions in front of processes and tools.

        As a shared asset, it will be necessary to define ownership rules and areas of responsibility around an enterprise’s data. In a number of organizations, the responsible parties have official roles to play such as « data owners », « data stewards » or « data custodians ». If the formal designation of responsibilities remains essential, we think that it is important to involve as many people as possible in the implementation of data governance, capable of contributing to the knowledge, control and management of data.

        Otherwise known as: « Everyone is a Data Steward ».

        Download our white paper

        Why start an agile data governance?

        Automated Governance – Agile Data Governance attribute (3/5)

        Automated Governance – Agile Data Governance attribute (3/5)

        The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.

        The following articles explain the characteristics of a data governance labeled as agile in order to:

        1. Be as close as possible to your enterprise’s operational reality.
        2. Adapt to your enterprise’s context and not the other way around.
        3. Accurately reflect your data assets.
        4. Unify and involve your collaborators.
        5. Respond to changes quickly.

        Implementing data governance must begin by referencing, indexing, and evaluating your organization’s data assets. Building such an artifact by only using human intelligence is rarely successful given the resource constraints. Therefore, it is necessary to maximise automated processes regarding the extracting and collecting of information related to the data in your organization.

        With the help of artificial intelligence algorithms or machine learning, it becomes possible to interpret, contextualize, and give more meaning to your data assets.

        With the help of artificial intelligence algorithms or machine learning, it becomes possible to interpret, to contextualize, and to deduce a more precise meaning to your data asset. This automation allows your data managers avoid the blank paper syndrome and, freeing them from tedious and repetitive tasks, increase the support from as many people as possible of your initiative to implement data governance within your company.

        Finally, this automation became a necessity in the new era where the volume and the variety of systems exploded. The maintenance and the updating of this information repository are crucial to reflect the reality of your IS data. With the help of incremental analysis, which frequently and automatically processes your data, you will gain a high-value metadata repository added for your data consumers.

        Download our white paper

        Why start an agile data governance?

        Non intrusive Governance – Agile Data Governance attribute (2/5)

        Non intrusive Governance – Agile Data Governance attribute (2/5)

        The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.

        The following articles explain the characteristics of a data governance labeled as agile in order to:

        1. Be as close as possible to your enterprise’s operational reality.
        2. Adapt to your enterprise’s context and not the other way around.
        3. Accurately reflect your data assets.
        4. Unify and involve your collaborators.
        5. Respond to changes quickly.

        Enterprises consider that the classic approaches to Enterprise Data Management (EDM) require all parties involved to adopt a certain number of tools and procedures that can burden the processes of data discovery, thus becoming an obstacle to innovation. In a world where the variety as well as the volume of data is exploding, where new tools for data storage and processing ceaselessly pop up, a much more reasonable approach exists.

        It is a means to give freedom to your collaborators to use tools more adaptable to their uses, whether it is to generate or to access datasets, of course, according to their authorized level!

        This approach aims to centralize the knowledge that your collaborators have acquired from their datasets in a “data catalog.” The objective is to collect and to aggregate the metadata of your created or updated datasets from your tools and storage systems. It is from these platforms, unrelated to operations, that data governance can be executed without interfering with the daily work of your collaborators.

        This non-intrusive method of addressing data governance calls for the enterprise move forward little by little. Experiment with and adjust your management rules on data and its metadata gradually so that you can establish a curation of your data assets.

        Download our white paper

        Why start an agile data governance?

        Bottom-up Governance – Agile Data Governance attributes (1/5)

        Bottom-up Governance – Agile Data Governance attributes (1/5)

        The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.

        The following articles explain the characteristics of a data governance labeled as agile in order to:

        1. Be as close as possible to your enterprise’s operational reality.
        2. Adapt to your enterprise’s context and not the other way around.
        3. Accurately reflect your data assets.
        4. Unify and involve your collaborators.
        5. Respond to changes quickly.

        The implementation of data governance must avoid pitfalls, all too often seen in the past via a top-down approach.

        This type of descending approach wants for objectives and instructions be set by management and then implemented.

        This project leadership, like software development in recent years, has proven to be too hierarchical and bureaucratic, uncorrelated to the realities on the ground and therefore, data held by the company.

        We recommend a bottom-up approach of the field, in the operational sense, to progressively consolidate a synthesis and to maintain a data governance management that corresponds to the real context of your enterprise.

        We define a bottom-up data governance by:

        • A democratic approach rather than a hierarchical one.
        • A willingness to solve problems created fluidly rather than by imposing more structure.
        • A “bureaucracy” reduced to a minimum to facilitate its implementation and its maintenance (a prevailing work principle at Spotify called, “Minimum Viable Bureaucracy”).
        • An active collaboration amongst stakeholders in favor of ownership and the collection of information on the organization’s data.
        • An autonomy of collaborators in the choice of tools and the manner in which they organize themselves.

        Download our white paper

        Why start an agile data governance?

        The role of metadata in a data-driven strategy

        The role of metadata in a data-driven strategy

        Our conviction requires a company to make compromises between control and flexibility in the use of data. In short, companies must be able to adopt a data strategy both encouraging and easy- to-use, all while minimizing risks.

        We are convinced that such governance is achievable if your collaborators are likewise able to answer these few questions:

        • What data are present in our organization?
        • Are these data sufficiently documented to be understood and mastered by the collaborators in my organization?
        • Where do they come from?
        • Are they secure?
        • What rules or restrictions apply to my data?
        • Who are the people in charge? Who are the “knowers”?
        • Who uses these data? How?
        • How can your collaborators access it?

        These metadata (information about data) become strategic information within enterprises. They describe various technical, operational or business aspects of the data you have.

        By constituting a unified metadata repository, both centralized and accessible, you are guaranteed precise data, which are consistent and understood by the entire enterprise.

        The benefits of a metadata repository

        We bring our experiences to enhance a well-founded governance on metadata management. We are firmly convinced that we cannot govern what we do not know! Thus, to build a metadata repository constitutes a solid working base to start a governance of your data.

        It will allow, among others, to :

        • Curate your asset;
        • Assign roles and responsibilities on your referenced data;
        • Be completed by your employees in a collaborative manner;
        • Strengthen your regulatory compliance.

        The concentration of efforts on metadata and the creation of such a frame of reference is one key characteristic of a data governance with an agile approach.

        Download our white paper
        Why start an agile data governance?

        What is Data Governance?

        What is Data Governance?

        The notion of governance associated with information systems first appeared in the 90’s. It refers to the IT management resources implemented in an enterprise to achieve its strategic objectives. With the explosion of new technology for the purpose of collecting and sharing information, new security rules to guarantee data protection have been imposed.

        In the context of fundamental digital changes, enterprises bear witness to the massive volume of data generated for their race to innovation. This influx of information is leading enterprises to initiate a data-driven governance in a global and transversal way like a strategy-focused priority.

        We like to define data governance as an exercise of authority over decision-making power (planning, surveillance, and enforcement of rules) and the controls on data management.

        In other words, it allows the clear documentation of the different roles and responsibilities around data as well as determining the procedures and the tools supporting data management within an organization.

        An enterprise’s data is a « shared asset » and must be treated as such. It is for this reason that data governance is essential. This set of practices, policies, standards, and guides will supply a solid foundation to ensure that data is properly managed, creating value within an organization.

        Data governance: between control and facilitation?


        In the past, implementations of data governance within organizations have rarely been successful. Data managers are often too focused on technical management or the strict control of data.

        For data users who strive to experiment and innovate around data, governance can evoke a set of restrictions, limitations, and useless bureaucracy. They have frightening visions of data locked in dark catacombs, only accessible after months of fighting against administrative hassles. For others, they remember the painful amount of energy wasted at meetings, updating spreadsheets and maintaining wikis, only to find that no one was even benefiting from the fruits of their labor.

        It’s no wonder that data governance has a bad reputation!

        Despite the fact that it brings real value, organizations avoid carrying out a governance due to past experiences, ill-advised.

        It is clear that enterprises are conditioned by regulatory compliance: to guarantee data privacy, its security, and to ensure risk management. Nevertheless, to undertake an offensive strategy, which tends to improve an enterprise’s data usage – guaranteeing useful, useable, and used data – becomes the next crucial step in enhancing this asset.

        Data governance therefore is based on two approaches: offensive or defense. It is a matter of orienting the enterprise’s strategy towards IT requirements in terms of data security or a capitalization strategy and analysis in order to generate business value.

        Here are, among others, the objectives of a data governance strategy:

        Defensive approach


        • Undertake compliance with the authorities of other countries to avoid sanctions, such as the General Data Protection Regulation (RGPD) enforced in May 2018.

        • Comply with the internal obligations and rules to which the organization’s data is subject.

        • Ensure data security, its integrity and its quality for proper use.

        Offensive approach


        • Increase a company’s profitability and competitive position with the help of data.

        • Optimize data analysis, modeling, visualization, transformations and data enrichment.

        • Increase the company’s flexibility in the use of its data.

        Whether your sector of activity is highly regulated or immensely competitive, you will have to invest in having perfect control over your data in order to create innovative products and to keep your head above water.

        Data Governance: a competitive advantage

        Data Governance: a competitive advantage

        For the past few years, on the trails of GAFA (Google, Apple, Facebook, and Amazon), data is perceived as a crucial asset for enterprises. This asset is enhanced by digital services and new uses that disrupt our daily lives and weaken more traditional businesses.

        This transformation, whether we like it or not, concerns all structures and all sectors. Enterprises have understood that in order to face up to innovative startups and powerful web giants, they must capitalize on their data. This awareness brings the great – likewise the small – enterprises to start a digital transformation to become what we call, Data-Driven.

        In order to be data-driven, data should be considered like an asset in business, which must be mastered in order to be enhanced.

        It is a means to collect, safeguard, and ensure data assets of the highest quality and security. In other words, users must have access to accurate, intelligible, complete, and consistent data in order to detect proven business opportunities, to minimize time-to-market, and also to undertake regulatory compliance.

        The road to reach the Promised Land of data innovation is full of obstacles. Between siloed data on both sides in the enterprise and tribal knowledge, this legacy does not contribute anything to the overall quality of data.

        The advent of Big Data has also reinforced the sentiment that the life cycle of one data must be mastered in order to find your way through the influx and the massive volume of the enterprise’s stored data. Talk about a challenge encompassing roles and responsibilities, processes and tools!

        The implementation of such data governance is a chapter that a data-driven company must write

        However, in our experience, exchanges with and lectures by major players of data confirmed our observation that the approaches to data governance from recent years have not kept their promises.

        Through our white papers, we hope to shed light on this subject and be a starting point in the construction of an “agile” data governance, where more traditional approaches have not been able to organize and adapt in a constantly changing environment.

        Download our white paper

        Why start and agile data governance?

        Metadata management : a trending topic in the data community

        Metadata management : a trending topic in the data community

        On the 4th, 5th and 6th of March, Zeenea had the opportunity to attend the famous Data & Analytics Summit in London organized by Gartner. This is an indispensable and inspiring event for Chief Data Officers and their teams in the implementation of their data strategy.

        This article outlines many concepts from the conference: “Metadata Management is a Must-Have Discipline” by Alan Dayley, Gartner Analyst. This subject has attracted the attention of many C-Levels, confirming that metadata management is a top priority for the years, even months, to come.


        The concept of metadata applied to our daily lives

        To introduce the concept of metadata, the speaker made an analogy to a situation that is known to all of us and that is becoming more and more important in our daily lives: to identify and select what we eat.

        Take the example of a meal composed of many different ingredients that have been significantly modified. It’s thanks to the different labels, pricing schemes, and descriptions on a product’s packaging that consumers are able to identify what they have on their plates.

        This information is what we call, metadata!

        How do metadata bring value to an enterprise?

        Applying metadata on data allows the enterprise to contextualize its data assets. Metadata addresses different subjects, gathered within four different categories: Data Trust, Regulations & Privacy, Data Security, and Data Quality.

        The implementation of a metadata management strategy depends on finding the balance between the identified business needs within the company and the regulations associated with data risks.

        In other words, where should you invest your time and money? Should you democratize data access to your data teams (data scientists, data engineers, data analysts or data experts) to increase in productivity or to concentrate on the demands of regulatory bodies such as the GDPR, to avoid a hefty fine?

        The answer to these questions is specific to each enterprise. Nevertheless, Alan Dayley highlights four use cases, identified as top priority cases by CDOs, where metadata management should be the key:


        1. Data governance

        In this particular use case, the speaker confirms that data governance can no longer be thought of in a “top-down” manner. Data cross-references different teams and profiles with distinct roles and responsibilities. In light of this, everyone must work together to inform and complete their data’s information (its uses, its origin, its process, etc.). Contextualizing data is a fundamental element to establishing effective and easy data governance!


        2. Risk management and compliance

        The information requested below have been enforced since the arrival of the GDPR. Enterprises and their CDOs must:

        • Define the responsibilities linked to their data sets.
        • Map their data sets.
        • Understand and identify the processing operations on the data and associated risks.
        • Have a processing and/or a data lineage register.

        3. Data analysis

        By addressing data governance in a more collaborative way and by favoring interactions between data users, the enterprise will benefit from collective intelligence and continuous improvement on the understanding and analysis of a data set. In other words, it’s extracting previous discoveries and experimentations from pertinent information for the next data users.


        4. Data value

        In the quest for data monetization, data will have no value, so to speak, unless the information around it is:

        • measured: by its quality, its economic characteristics, etc.
        • managed: the persons in charge, documentation provided, its updates, etc.


        How to establish metadata management?

        No matter your enterprise’s objectives, you can not reach them without metadata management. Therefore, the answer to those questions is indeed metadata!

        Our recommendations to be able to undertake this exercise would be to:

        • Hire the right sponsor that values a metadata-centric approach in the enterprise.
        • Identify the main use case that you want to treat first (as defined above).
        • Check that the efforts made in terms of metadata are not isolated but are centralized and unified.
        • Select a key metadata management solution on the market, such as a data catalog.
        • Define where, who, and how you will start.

        To conclude this article, not having metadata management is like driving on a road with no signs. Be careful not to get lost!

        How Big Data & Machine Learning contributed to Zalando’s success

        How Big Data & Machine Learning contributed to Zalando’s success

        For the second year in a row, Zeenea participated at Big Data Paris as a sponsor this past 11th and 12th of March to present its’ data catalog.

        During the event, we were able to attend to many different conferences presented by professionals in the data field : chief data officers, business analysts, data science managers, etc…

        Among those conferences, we had the opportunity to attend the Zalando conference, presented by Kshitij Kumar, VP Data Infrastructure.


        Zalando: the biggest eCommerce plateform in Europe

        With more than 2,000 different brands and 300,000 items available, the German online fashion platform conquered 24 million active users in 17 European countries since its’ creation in 2008 [1].

        In 2018, Zalando earned about € 5,4 billion : a 20% increase since the year 2017 [2]!

        With these positive results, Zalando has a lot of hope for the future. Their objective is to become the fashion reference :

        We want to become an essential element to the lives of our customers. Only a handful of apps make it to being part of a customer’s life such as Netflix for television or Spotify for music. We aim to be this one fashion destination where the customer can fulfil all of their fashion needs. [3]”

        explains David Schneider, co-CEO of Zalando.

        But how was Zalando able to become so successful in such little time? According to Kshitij Kumar, it is a question of data.

        Zalando on the importance of being a data-driven enterprise

        Everything is based on data.” states Kshitij Kumar during his conference Big Data Paris this past March. For 20 minutes, he explains that everything must revolve around data : business intelligence and machine learning are built based on the company’s data.

        With more than 2,000 technical employees, Zalando claims a Big Data infrastructure in different categories :


        Data Governance

        In response to the GDPR, the VP Data Infrastructure explains the importance of establishing data governance with the help of a data catalog: “It is essential to an organization in order to have safe and secure data.


        A machine learning platform

        It’s by exploring, working, curating and observing your data that a machine learning platform can be efficient.


        Business intelligence

        It’s by putting into place visual KPIs and trusted datasets that BI can be proactive.


        Zalando’s Machine Learning evolution

        Kshitjif reminds us that with Machine Learning, it is possible to collect data in real time.

        In the online fashion industry, there are many use-cases: size recommendation, search experience, discounts, delivery time, etc…

        Interesting questions were then brought up: How can you know exactly what a customer’s taste is? How to know exactly what he could want?

        Kumar answers by telling us that it’s by repeatedly testing your data:

        Data needs to be first explored, then trained, deployed and monitored in order for it to be qualified. The most important step is the monitoring process. If it is not successful, then you must start the machine learning process again until it is.

        Another benefit in Zalando’s data strategy is their return policy. Customers have 100 days to send their items back. Thanks to these returns, Zalando can gather data and therefore, better target their clients.


        Zalando’s future

        Kshitij Kumar tells us that by 2020, he hopes to have an evolved data structure. “

        In 2020, I envision Zalando to have a software or program that allows any user to be able to search, identify and understand data. The first step in being able to centralize your data is by having a data catalog for example. With this, our data community can grow through internal and external (vendors) communication.



        [1] “L’allemand Zalando veut habiller l’Europe – JDD.” 18 oct.. 2018, https://www.lejdd.fr/Economie/lallemand-zalando-veuthabiller-leurope-3779498.

        [2] “Zalando veut devenir la référence dans le domaine de la mode ….” 1 mars. 2019, http://www.gondola.be/fr/news/non-food/zalando-veut-devenir-la-reference-dans-le-domaine-de-la-mode.

        [3] “Zalando Back in Style as It Bids to Be Netflix of Fashion – The New ….” 28 févr.. 2019, https://www.nytimes.com/reuters/2019/02/28/business/28reuters-zalando-results.html.

        Who are Data Stewards?

        Who are Data Stewards?

        Digital transformations bring about new challenges in the data industry. We are increasingly talking about data stewardship;  an activity focused around data management and documentation of an organization. In this article, we would like to present the data stewards, the enterprise’s true guardians of data, take a closer look at their role, their missions, and their tools.

        This article is a summary of the interviews conducted with more than 25 data stewards in medium-sized and large French enterprises. The goal was to understand their tasks and their hardships in metadata management, providing solutions within our data catalog.

        The Data Steward’s role in the enterprise

        Enterprises are reorganizing themselves around their data to produce value and finally innovate from this raw material. Data stewards are here to orchestrate data systems’ data of the enterprise. They must ensure the proper documentation of data and facilitate their availability to their users, such as data scientists or project managers for example. Their communication skills enable them to identify the data managers and knowers, as well as to collect associated information in order to centralize them and perpetuate this knowledge within the enterprise. In short, data stewards provide metadata; a structured set of information describing datasets. They transform these abstract data into concrete assets for the profession.

        The profession is on the rise! It deals with trending topics and its social role allows data stewards to work with both technical and professional people. Data stewards are the first point reference for data in the enterprise and serve as the entry point to access data.

        They have the technical and business knowledge of data, which is why they are called “masters of data” within an organization!

        Data Steward’s missions

        Their objective is quite clear; a data steward must take part in the data governance of enterprises. To find and to understand these data, to impose a certain discipline in metadata management and to facilitate their availability to their users.

        These are, among other things, quite a few subjects that data stewards must address. To achieve this, data stewards must ensure that data documentation that they manage are well maintained. They are free to suggest the method and format of technical and professional data documentation of their choice. Their days are punctuated by the search for data managers and knowers to enrich the knowledge they have gathered in an exploitable tool for technical and professional users. Thus, they want the actors of data projects to be able to connect and collaborate in order to improve information sharing and productivity for all.


        Equip Data Stewards

        The data steward is, therefore, a new profession where their missions are still in need of clarification, its tools to be identified, and its necessity within the enterprise to be evangelized. As a result, enterprises still have difficulty in allotting a clear budget. It is therefore difficult for them to be properly equipped to ensure the proper control and management of their data.

        Yet, when well equipped, it will allow them to:


        • become autonomous in data management activities,

        • centralize information collected on the data,

        • manage obsolescence of documentation,

        • report errors and/or changes to data,

        • identify relevant data to send to their users,

        • expose data to their users from a collaborative tool.

        Such an approach can be successful where many larger “data governance” initiatives have failed.


        In conclusion

        To this day, we are convinced that the data steward role is indispensable to construct and orchestrate efficient data governance in the enterprise. This is the direction Zeenea is taking by offering dynamic and connected documentation of the enterprise’s data. Otherwise known as data catalogs, their ambition is to become the reference tool for data stewards. To manage data in a user-friendly way. To centralize all collected metadata. To open data to its users, depending on the level of sensitivity. To manage data quality. All this in one click. Etc.

        In a virtuous circle, the data catalog will bring increased value to data users once the data steward industrializes the addition of metadata and the contribution of collaborators in the tool.

        Data Governance: Towards an agile approach

        Data Governance: Towards an agile approach

        These last few months, it has become more and more difficult to attend a meeting without hearing the expression data governance. However, this subject is nothing new! Be that as it may, with the arrival of Big Data technologies: data and its use become the cornerstone of approaches to innovation. An old subject evolving in a very new context…

        Data and governance: one can not be without the other

        The data craze over the last years is such that enterprises invest a lot of time and money to try to break down data silos and to unify their asset thanks to new, ever more efficient, and less costly storage infrastructures.

        Nevertheless, enterprises understood rather quickly that the promise – to innovate through data – was going to be much more complicated than previously expected. Despite the latest technological advancements, data are still scattered on both sides in the enterprise with a militant legacy. New storage systems implemented are, ultimately, “only” additional technical stacks in the enterprise’s IS landscape and don’t allow, on their own, to manage data’s life cycle, guarantee rules allowing the best data usage and thus, maximize the creation of data value. We are talking about data governance here.

        The objectives of data governance

        In the pursuit of innovation, enterprises are rethinking their organizations to move towards a “data-driven” culture. Information systems must become the profession’s strong arm by placing refined, secured and quality data at the center of strategic decisions.

        To achieve this transformation, organizations construct what we call data governance. This project pursues quite clear objectives, among others:

        • Ensure metadata management (technical, operational, or even business) and data documentation.
        • Simplify data access and facilitate their use by as many collaborators as possible.
        • Ensure data quality and integrity.
        • Manage data security: Supervise data collection and their use, especially when it comes to personal data.

        An agile strategy to data governance

        The way to approach the subject of data governance is evolving. Our experiences have brought us to promote data governance based on the following four pillars:

        • Non-invasive and post hoc: Data governance should not be an obstacle to innovation in your enterprise. Metadata collection and aggregation of an enterprise’s datasets, after their creation or update through various pipelines, allows you not to interfere with the owners of datasets or their users.

        • Automatic and connected: The automation of metadata collection and governance KPIs allows your tools to accurately reflect the reality. On the other hand, this automation guarantees that such governance is up-to-date and ensures upscaling.

        • Bottom-up et collaborative: A strategy of bottom-up data governance wants to put individuals and their interactions in front of processes and tools. An approach to data governance cannot be successful, which involves all the collaborators in an organization, thus benefiting from collective intelligence.

        • Iterative: Construct data governance in stages to correspond as close as possible to the company’s expectations and to its operations. The adaptation to change must be at the heart of an enterprise’s data governance strategy.

        Such an approach can be successful where many larger “data governance” initiatives have failed.

        Agile data governance conclusion

        The same as how software developments have gradually shifted away from traditional methods (V-model, Waterfall, etc.) to agile methods, data governance must be rethought.

        Such an approach is not only iterative but also applied incrementally to your data governance strategy allowing greater flexibility, necessary to take into consideration the ever-increasing complexity of your IS.

        White paper: “Why start an agile data governance?”

        The question is not about moving towards becoming a data-driven organization, but how.  We recommend the implementation of an agile, collaborative and pragmatic data governance.


        Data mapping: The challenges in an organization

        Data mapping: The challenges in an organization

        The arrival of Big Data did not simplify how enterprises work with data. The volume, the variety, and the various data storage systems are exploding.

        To prove this, Matt Turck published what we call the Big Data Landscape. Updated every year, this infographic shows the different key players in various sub-domains of the Big Data landscape.

        Thus, with the Big Data revolution, it is even more difficult to answer “primary” questions related to data mapping:

        • What are the most pertinent datasets and tables for my use cases and my organization?

        • Do I have sensitive data? How are they used?

        • Where does my data come from? How have they been transformed?

        • What will be the impacts on my datasets if they are transformed?

        >> Download our toolkit: Metamodel template <<

        So many questions that information systems managers, Data Lab managers, Data Analysts or even Data Scientists ask themselves to be able to deliver efficient and pertinent data analysis.

        Among others, these questions allow enterprises to:

        • Improve data quality: Providing as much information as possible allows users to know if the data is suitable for use.

        • Comply with European regulations (GDPR): mark personal data and the carried out processes.

        • Make employees more efficient and autonomous in understanding data through graphical and ergonomic data mapping.

        To put these into action, companies must build what is called data lineage.