To meet the challenges of your business, are you searching for a solution that enables a more available, scalable infrastructure at controlled costs? Would you like to increase your capacity to innovate? Then you need to get into Platform Engineering!

In this article, discover what Platform Engineering is and how it differs from adjacent concepts – including DevOps and SRE – as well as its benefits for your organization.

Designated by Gartner as one of the key trends of 2023, Platform Engineering is a little-known discipline. Yet, it is a crucial solution as companies increasingly move to the cloud. Platform Engineering aims to improve software development and delivery by streamlining and optimizing the process of planning and implementing tool chains such as CI/CD pipelines, test environment deployment, and infrastructure-as-code (IaC) configuration to automate cloud resource provisioning.

What is Platform Engineering?

Platform Engineering is a discipline that focuses on the design, development, and management of various technical platforms. It delivers a set of services and tools that enable developers to build, deploy, and manage applications and services efficiently and cost-effectively. Its mission? To build a robust, flexible, and automated IT infrastructure capable of meeting the needs of a wide range of applications and services.

The Platform Engineers in charge of building these infrastructures have the objective of delivering a high level of availability, scalability, and resilience, in order to absorb the ever-increasing traffic and data flows. There is a fine line between the teams in charge of platform engineering and the development and operational (DevOps) teams. They often work closely together to provide tools and services designed to accelerate development cycles, improve application quality, and facilitate continuous deployment.

What do Platform Engineering teams do and how does Platform Engineering work?

Most commonly, Platform Engineering teams are responsible for the design, implementation, and management of the technical platforms that support an organization’s applications and services. To do this, they ensure in particular:

• Developing and maintaining the platform infrastructure by managing the installation, and configuration of servers, storage, networks, and other components.
• Automating the processes of deployment, configuration management, and system monitoring.
• Platform security, identity, and access management, as well as certificate management, security audits, etc.
• Technical support to the development and operations teams to solve platform-related issues.
• Optimizing platform performance by monitoring performance metrics, identifying bottlenecks, and making improvements.
• Platform capacity management by monitoring resource utilization trends and forecasting future needs.

What are the benefits of Platform Engineering?

Platform Engineering improves the productivity of the development teams by providing tools and services that accelerate development and deployment cycles. This optimized productivity also contributes to cost control through more efficient use of IT resources. If Platform Engineering improves the availability of the infrastructure, it also enables scalability and adaptability for the current (and future!) needs of the company.

Finally, Platform Engineering helps to strengthen the security of the IT infrastructure by providing tools for identity and access management, security monitoring, and security incident response.

What are the differences between Platform Engineering and DevOps?

Platform Engineering and DevOps are two different but complementary approaches. To fully understand the differences between the two disciplines, note that DevOps encourages close collaboration between development and operations teams (Dev and Ops) to accelerate development cycles, improve code quality, and reduce deployment times.

So while DevOps aims to create a culture of collaboration and shared responsibility between Dev and Ops teams, Platform Engineering focuses on the design, construction, and management of technical platforms. While the two approaches share common company objectives, they focus on different aspects of managing an organization’s IT infrastructure.

What are the differences between Platform Engineering and SRE

Platform Engineering and Site Reliability Engineering (SRE) are two related fields. Both focus on the management of an organization’s IT infrastructure. SRE relies on engineering practices to maintain the availability, resiliency, scalability, and performance of services and applications. The mission of SRE teams is to ensure the availability of IT systems, monitor and measure the quality of service, resolve incidents, and provide long-term solutions for recurring problems. They, therefore, work hand in hand with the DevOps teams and the Platform Engineer.

The main difference between SRE and Platform Engineering is that SRE focuses on managing software products to ensure availability and quality of service, while Platform Engineering focuses on creating and managing a robust, flexible, and scalable IT infrastructure for applications and services.

The Council of the European Union has just approved the Data Governance Act, a document that aims to facilitate the re-use of certain protected public sector data and encourage data sharing throughout the European Union, while ensuring strict respect of data privacy. It should be executed by 2023. 

When it comes to data, the European Union plays a major role. While the GDPR celebrated its fourth anniversary on May 25th, the European Parliament and the Council of the European Union continue to work for a reasonable and responsible use of data. The Data Governance Act (also known as the DGA) has been in the works since 2019 – the result of a broad consultation covering the private and public sectors. Based on 11 workshops, the DGA is devoted to the strengthening of the control offered to individuals and legal entities on the use and dissemination of their data. 

After an initial agreement on April 6th, 2022 to define the scope of the Data Governance Act, the Council of the European Union officially approved the DGA on May 16th, 2022. The Act is expected to be fully implemented by the summer of 2023. Beyond its main ambition, which is to define a unique and homogeneous framework for all European countries, the Data Governance Act is conceived as a legal instrument that should facilitate, fluidify, and rationalize the exploitation of data. 

Unlike the GDPR, the Data Governance Act is not limited to personal data, but has much broader ambitions. Ambitions that not only frame good practices related to data governance, but also encourage the exploitation of data from the public sector. Behind the DGA, there is a double aspiration: to preserve the freedom of the business while protecting data privacy.

What exactly is the DGA?

To fulfill its mission of protecting data while creating the conditions for freeing up innovation and creativity, the Data Governance Act is based on four key principles. 

The first principle concerns public actors. Like private sector companies, public agencies generate and use large amounts of data. This data falls under the scope of the GDPR and is subject to strict protection and oversight – whether it is personal data, privacy rights, or intellectual property. The DGA sets out a legal and technical framework that defines the rules for the re-use of this protected data, with essential levers such as anonymization or pseudonymization.

The second major component of the Data Governance Act is devoted to the sharing of data (personal or corporate) with non-profit organizations. The regulator’s ambition is to encourage innovation in the public interest in key sectors such as the environment and health sectors. A specific status called ‘altruistic organization’ will thus be created. To benefit from this status, it will be necessary to register officially via a European form and to respect a demanding framework, placed under the line of transparency.

The third principle of the DGA concerns the sharing of data between companies and private actors. These actors use intermediaries whose missions are redefined by the DGA. The principle is clear: to avoid that these intermediaries can exploit the information for their own purposes by sharing it. Once again, the DGA sets out the principle of total transparency, combined with an ambition for sovereignty. For example, these intermediaries must be located in the European Union or the European Economic Area.

Finally, the DGA institutes the creation of a European Data Innovation Council that will compile and share best practices related to data governance, with ongoing reflections on standardization at the European level.

What is the impact of the Data Governance Act for your company?

While the DGA may seem restrictive in developing a precise framework, particularly for data intermediation, it is nevertheless a major step forward. Indeed, behind the native requirement inscribed in the spirit of the Data Governance Act, you will find above all, the ground of trust with your customers as well as your partner ecosystems. A trust that is essential to legitimize all data projects in the service of the efficiency and productivity of your company.

Data Privacy is a priority for organizations that wish to fully exploit their data. Considered the foundation of trust between a company and its customers, Data Privacy is the pillar of successful data governance. Understand why in this article.

Whatever the sector of activity or the size of a company, data now plays a key role in the ability for organizations to adapt to their customers, ecosystem, and even competitors. The numbers speak for themselves! Indeed, in a study by Stock Apps, the global Big Data market was worth $215.7 billion in 2021 and is expected to grow 27% in 2022 to exceed $274 billion.

Companies are generating such large volumes of data that data governance has become a priority. Indeed, a company’s data is vital to identify its target audiences, create buyer personas, provide personalized responses to its customers or optimize the performance of its marketing campaigns. However, this is not the only issue. If data governance provides the possibility to create value with enterprise data assets, it also ensures the proper administration of data confidentiality, also known as Data Privacy.

Data Privacy vs. Data Security: two not so very different notions 

Data Privacy is one of the key aspects of Data Security. Although different, they take part in the same mission: building trust between a company and its customers who want to entrust their personal data. 

On the one hand, Data Security is the set of means implemented to protect data from internal or external threats, whether malicious or accidental (strong authentication, information system security, etc.).

Data Privacy, on the other hand, is a discipline that concerns the treatment of sensitive data – not only personal data (also called PII for Personally Identifiable Information) but also other confidential data (certain financial data, intellectual property, etc.). Data Privacy is furthermore clearly defined in the General Data Protection Regulation (GDPR) which came into place in Europe in 2018 and has since helped companies redefine responsible and efficient data governance.

Data confidentiality has two main aspects. The first is controlling access to the data – who is allowed to access it and under what conditions. The second aspect of data confidentiality is to put in place mechanisms that will prevent unauthorized access to data.

Why is Data Privacy so important?

While data protection is essential to preserve this valuable asset and to create the conditions for rapid data recovery in the event of a technical problem or malicious attack, data privacy addresses another equally important issue. 

Consumers are suspicious of how companies collect and use their personal information. In a world full of options, customers who lose trust in one company can easily buy elsewhere. To cultivate trust and loyalty, organizations must make data privacy a priority. Indeed, consumers are becoming increasingly aware of data privacy. The GDPR has played a key role in the development of this sensitivity: customers are now very vigilant about the way their personal data is collected and used. 

Because digital services are constantly developing, companies gravitate in a world of hyper-competition where customers will not hesitate to switch to a competitor if said company has not done everything possible to preserve the confidentiality of their data. This is the main reason why Data Privacy is so crucial!

Why is data privacy a pillar of data governance?

Data governance is about ensuring that data is of sufficient quality and that access is managed appropriately. The company’s objectives are to reduce the risk of misuse, theft, or loss. As such, data privacy should be understood as one of the foundations of sound and effective data governance. 

Even if data governance embraces the data issue in a much broader way, it cannot be done without a perfect understanding of the levers to be used to ensure optimized data confidentiality…

In a world where companies aspire to become data-driven, data management and data governance are concepts that must be mastered at all costs. Too often perceived as related or even interchangeable disciplines, the differences are important.

A company wanting to become data-driven must master the disciplines, concepts, and methodologies that govern the collection and use of data. Among those that are most often misunderstood are data governance and data management. 

On the one hand, data governance consists of defining the organizational structures of data – who owns it, who manages it, who exploits it, etc. On the other hand, data governance is about policies, rules, processes, and monitoring of indicators that allow for a sound administration of data throughout its life cycle (from collection to deletion).

Data management can therefore be defined as the technical application of the recommendations and measures defined by data governance.

Data governance vs. data management: their different missions

The main difference between data governance and data management is that the former has a strategic dimension, while the latter is rather operational.

Without data governance, data management cannot be efficient, rational, or sustainable. Indeed, data governance that is not restated into appropriate data management will remain a theoretical document or a letter of intent that will not allow you to actively and effectively engage in data-driven decision-making. 

In order to understand what is at stake, it is important to understand that all the disciplines related to data are permanently overlapping and interdependent. Data governance is a conductor who orchestrates the entire system. It is based on a certain number of questions such as:

• What can we do with our data?
• How do we ensure data quality?
• Who is responsible for the processes, standards, and policies defined to exploit the data? 

Data management is the pragmatic way to answer these questions and make the data strategy a reality. Data management and data governance can and should work in tandem. However, data governance is mainly concerned with the monitoring and processing of all the company’s data, while data management is mainly concerned with the storage and retrieval of certain types of information.

Who are the actors of data governance and management?

At the top management level, the CEO is naturally the main actor in data governance, as they are its legal guarantor. But they are not the only one who must get involved.

The CIO (Chief Information Officer) plays a key role in securing and ensuring the availability of the infrastructure. However, constant access to data is crucial for the business (marketing teams, field salespeople) but also for all the data teams who are in charge of the daily reality of data management. 

It is then up to the Chief Data Officer (CDO) to create the bridge between these two entities and break down the data silos in order to build agile data governance. He or she facilitates access to data and ensures its quality in order to add value to it.

And while the Data Architect will be more involved in data governance, the Data Engineer will be more involved in data management. As for the Data Steward, he or she is at the confluence of the two disciplines.

How combining the two roles helps companies become data-driven

Despite their differences in scope and means, the concepts of data governance and data management should not be opposed. In order for a company to adopt a data-driven strategy, it is imperative to reconcile these two axes within a common action. To achieve this, an organization’s director/CEO must be the first sponsor of data governance and the first actor in data management.  

It is by communicating internally with all the teams and by continuously developing the data culture among all employees that data governance serves the business challenges while preserving a relationship of trust that unites the company with its customers.

This is the last episode of our third and final season of the “The Zeenea Effective Data Governance Framework”.

Divided into two episodes, this final season will focus on the implementation of metadata management with a data catalog.

In this final episode, we will help you start a 3-6 weeks data journey with Zeenea and then deliver the first iteration of your Data Catalog.

1. Identify the goal

2. Deploy / Connect

3. Conceive and configure

4. Import the items

5. Open and test

6. Measure the gains

This is the first episode of our third and final season of the “The Zeenea Effective Data Governance Framework”.

Divided into two episodes, this final season will focus on the implementation of metadata management with a data catalog.

For this first episode, we will give you the right questions to ask yourself in order to build a metamodel for your metadata.

The Who

• Who created this data?
• Who is responsible for this data?
• Who does this data belong to?
• Who uses this data?
• Who controls or audits this data?
• Who is accountable on the quality of this data?
• Who gives access to this data?

The What

• What is the “business” definition for this data?
• What are the associated business rules of this data?
• What is the security/confidentiality level of this data?
• What are the acronyms or aliases associated with this data?
• What are the security/confidentiality rules associated with this data?
• What is the reliability level (quality, velocity, etc.) of this data?
• What are the authorized contexts of use (related to confidentiality for example)?
• What are the (technical) contexts of use possible (or not) for this data?
• Is this data considered a “Golden Source”?

The Where

• Where is this data located?
• Where does this data come from? (a partner, open data, internally, etc.)
• Where is this data used/shared?
• Where is this data saved?

The Why

• Why are we storing this data? (rather than treating its flow)?
• What is this data’s current purpose/usage?
• What are the possible usages for this data? (in the future)

The When

• When was the data created?
• When was this data last updated?
• What is this data’s life cycle? (update frequency)?
• How long are we stocking this data for?
• When does this data need to be deleted?

The How

• How is this data structured? (diagram)?
• How do your systems consume this data?
• How do you access this data?

This is the final episode of the second season of the Zeenea Effective Data Governance Framework series.

Divided into three parts, this second part will focus on Adaptation. This consists of : 

• Organizing your Data Office
• Building a data community  
• Creating Data Awareness

For this third and final episode of the season, we will help you use awareness support techniques that reduce the efforts needed to realize communicative tasks to make anyone aware of what the Data Governance Team is doing, get buy-in, and alignment at all levels.

At Zeenea, we advise to use the SMART framework to plan and execute the Data Awareness program.

What are SMART goals?

• Specific:  What do you want to accomplish?  Why is this goal important?  Who is involved?  What resources are involved?
• Measurable:  Are you able to track your progress?  How will you know when it’s accomplished?
• Achievable:  Is achieving this goal realistic with effort and commitment?  Do you have the resources to achieve this goal?  If not, how will you get them?
• Relevant:  Why is this goal important?  Does it seem worthwhile?  Is this the right time?  Does this match efforts/needs? 
• Timely:  When will you achieve this goal?

The “SMART” method for your data teams

If you think about the level of reach a team has, you can summarize them in 3 categories:

• The Control sphere is the one your Data Team can reach directly and interacts 
• The Influence sphere is the level where you can find sponsors and get help from
• The Concern sphere consists of the C levels who need to be informed on how things are progressing from a high level perspective.

In other words, you will have to touch all the stakeholders involved but with different means, timing and interactions.

Spend time creating nice formats, and pay attention to the form of all your artifacts.

Examples of SMART tasks 

You fill find below examples of SMART tasks:

For the Control sphere, we advise you to do the following:

• Deliver trainings (for both Data Governance teams as well as End users)
• Deliver presentations dedicated to teams (Strategy, OKRs, Roadmap, etc).
• Keep your burn-down charts and all visual management tools displayed at any time.

For the Influence sphere, we advise you to:

• Celebrate your first milestones
• Organize sprint demos
• Display OKRs teams constantly

And for the Concern sphere, we advise you to

• Celebrate the end of a project
• Organise product demos
• Record videos and make them available

This is the second episode of the second season of the Zeenea Effective Data Governance Framework series.

Divided into three parts, this second part will focus on Adaptation. This consists of : 

• Organizing your Data Office
• Building a data community  
• Creating Data Awareness

For this second episode, we will give you the keys to organizing an efficient and effective data community in your company.

Guilds

Guilds should be organized differently and in a self organized way.

The reason for Guilds to exist is passion and the teams are only built on a voluntary base.

In order to avoid the syndrome of too many useless meetings, we advise to allow only Guilds to meet in certain circumstances like:

• Trainings, workshops but in short formats like in BBLs (Brown Bag Lunch) for the topics they built the Guild for
• Q&A sessions with top executives to emphasize the Why of the Data Strategy
• Hack days to crack a topic 
• Post mortem meetings after a major issue has occurred.

This is the first episode of the second season of the Zeenea Effective Data Governance Framework series.

Divided into three parts, this second part will focus on Adaptation. This consists of : 

• Organizing your Data Office
• Building a data community  
• Creating Data Awareness

For this first episode, we will give you the keys to build your data personas in order to set up a clear and well defined Data Office. 

Some useful tips:

• Personas should be displayed in the office of all Data Office team members.
• Make it fun, choose an avatar or a photo for each team member, write a small personal and professional bio, list their intrinsic values and work on the look and feel.
• Build one persona for each person, don’t build personas for teams
• Be very precise in the personas definition interviews, rephrase if necessary.
• Treat people with respect and consider all ideas equally.
• Print them and put them on the office walls for all team members to see.

Building cross functional teams

In order to get rid of Data and organisational silos, we recommend you organise your Data Office in Feature Teams (see literature on the Spotify feature teams framework on the internet).

The idea is to build cross functional teams to address a specific feature expected by your company.

The Spotify model defines the following teams:

Squads

Squads are cross-functional, autonomous teams  that focus on one feature area. Each Squad has a unique mission that guides the work they do. 

In season 1, episode 2, in our OKRs example, the CEO has 3 OKRs and the first OKR (Increase online sales by 2%) has generated 2 OKRs:

• Get the Data Lake ready for growth, handled by the CIO
• Get the data governed for growth, handled by the CDO.

There would then be 2 squads:

• Feature 1: get the Data Lake ready for growth
• Feature 2: get data governed for growth.

Tribes

At the level below, multiple Squads coordinate within each other on the same feature area. They form a Tribe. Tribes help build alignment across Squads. Each Tribe has a Tribe Leader who is responsible for helping coordinate across Squads and encouraging collaboration. 

In our example, for the Squad in charge of the feature “Get Data Governed for growth”, our OKRs map tells us that there is a Tribe in charge of “Get the Data Catalog ready”.

Chapter

Even though Squads are autonomous, it’s important that specialists (Data Stewards, Analysts) align on best practices. Chapters are the family that each specialist has, helping to keep standards in place across a discipline.

Guild

Team members who are passionate about a topic can form a Guild, which essentially is a community of interest (for example: data quality). Anyone can join a Guild and they are completely voluntary. Whereas Chapters belong to a Tribe, Guilds can span different Tribes. There is no formal leader of a Guild. Rather, someone raises their hand to be the Guild Coordinator and help bring people together.

Here is an example of a Feature Team organization:

This is the fourth episode of our series “The Zeenea Effective Data Governance Framework”.

Split into three seasons, this first part will focus on Alignment: understanding the context, finding the right people, and preparing an action plan in your data-driven journey. 

[SEASON FINALE] This episode will give you the keys to build a concrete and actionable SWOT analysis.

How to start building a smart SWOT analysis?

Building a good SWOT analysis means adopting a democratic approach that will ensure you don’t miss important topics.

There are 3 principles you should follow:

Gather the right people

Invite different parts of your Data Governance Team stakeholders from Business to IT, CDO and CPO representatives. You’ll find that different groups within your company will have entirely different perspectives that will be critical to making your SWOT analysis successful.

Throw your ideas against the wall

Doing a SWOT analysis consists, in part, in brainstorming meetings. We suggest giving out sticky-notes and encouraging the team to generate ideas on their own to start things off. This prevents group thinking and ensures that all voices are heard.

This first ceremony should be no more than 15 minutes of individual brainstorming, Put all the sticky-notes up on the wall and group similar ideas together. 

You can allot additional time to enable anyone to add notes at this point if someone else’s idea sparks a new thought.

Rank the ideas

It is now  time to rank the ideas. We suggest giving a certain number of points to each participant. Each participant will rate the ideas by assigning points to the ones they consider most relevant. You will then be able to prioritize them with accuracy.

Toolkits for your SWOT analysis

In our first episode, we helped you analyze your Data Maturity.

We suggested you build a SWOT analysis for each aspect. It is interesting to focus on those for which your company score was low and spend more time on them and draft an improvement plan as described below:

This is the third episode of our series “The Zeenea Effective Data Governance Framework”.

Split into three seasons, this first part will focus on Alignment: understanding the context, finding the right people, and preparing an action plan in your data-driven journey. 

This third episode will give you the keys on how to get good sponsorship for your data projects.

Step 1: Identify potential sponsors

The first step consists in identifying all the potential sponsors and setting up one to one (or one to many if you involve many colleagues) meetings to ensure endorsements and move forward on the Data Governance you want to put in place. You have learned a lot from the OKR meetings and now have the substance to ensure their support. 

Step 2: Prepare your storytelling

The second step is to prepare a story for each sponsor. Again, based on the workshops you were involved in on the company Data Strategy, you should be able to draft a personalized story.

You have 3 forms of storytelling which can be combined if needed:

• Use a testimony and real story to strengthen yours,
• Use a metaphor to illustrate the data concepts when they feel too complex,
• Use a “springboard” story from a specific characteristic to give the big picture.

Step 3: Present yourself

The third step consists in getting ready to describe who you are, what you do and why you do it through the prism of every sponsor.

Step 4: Asking for money

The fourth step consists in getting ready to ask for the money. Asking for money involves proposing different scenarios with different outcomes, a detailed analysis on the costs, a quantitative view on the financial benefits and then a ROI analysis.

Step 5: Commit to deliverables

The fifth step is to commit to deliverables. There won’t be endorsement if you don’t commit to tangible deliverables, results as well as a time frame.

Some tips and tricks to maximize your chances for getting  the sponsors aligned:

Ask for more than you need. Don’t sell yourself short and be prepared for a cut in your funding expectations and prepare accordingly.

Get a champion. In the list of sponsors, try to build a good relationship with one in particular and ask for help and insights to maximize your chances of winning.

Be impeccable in all aspects. When you’re courting a sponsor, always keep to your word, always be on time or early for an appointment. Let him or her know you are a person of integrity. Don’t forget to share the OKRs Map in which the sponsor is involved down to your own OKR.

Be brief and sharp. Ask for what you want, but don’t take up a lot of potential sponsors’ time doing it.

Get commitments. At the end of the sponsorship process, you should be able to get the following outcomes:

• Get understanding and alignment
• Get funding (means and resources)
• Get help in removing impediments (and build a fast track in the organisation hurdles)
• Get a schedule to organize feedback ceremonies

This is the second episode of our series “The Zeenea Effective Data Governance Framework”.

Split into three seasons, this first part will focus on Alignment: understanding the context, finding the right people, and preparing an action plan in your data-driven journey. 

This second episode will give you the keys on how to put in place an efficient enterprise data strategy through the setting up of Objective Key Results. 

This is the first episode of our series “The Zeenea Effective Data Governance Framework”.

Split into three seasons, this first part will focus on Alignment: understanding the context, finding the right people, and preparing an action plan in your data-driven journey. 

Our first episode will give you the keys on how to evaluate the maturity of your company’s data strategy in order for you to visualize where your efforts should lie in your data governance implementation.

Data is the petrol of the 21st century

With GAFA paving the way (Google, Apple, Facebook, and Amazon), data has, in recent years, become a crucial enterprise asset and has taken a substantial place in the minds of key data and business people alike.

The importance of data has been amplified by new digital services and uses that disrupt our daily lives. Traditional businesses who lag behind in this data revolution are inevitably put at a serious competitive disadvantage.

To be sure, all organizations and all sectors of activity are now impacted by the new role data represents as a strategic asset. Most companies now understand that in order to keep up with innovative startups and powerful web giants, they must capitalize on their data.

This shift in the digital landscape has led to widespread digital transformations the world over with everybody now wanting to become “Data-Driven”. 

The road to becoming data-driven

In order to become data-driven, one has to look at data as a business asset that needs to be mastered first and foremost, and then exploited.

The data-driven approach is a means to collect, safeguard and maintain data assets of the highest quality whilst also tackling the new data security issues that come with the territory. Today, data consumers must have access to accurate, intelligible, complete, and consistent data in order to detect potential business opportunities, minimize time-to-market, and undertake regulatory compliance.

The road to the promised land of data innovation is full of obstacles.

Data legacy, with its heavy silos and the all too often tribal nature of data knowledge, rarely bodes well for the overall quality of data. The advent of Big Data has also reinforced the perception that the life cycle of any given data must be mastered in order for you to find your way through the massive volume of the enterprise’s stored data.

It’s a challenge that encompasses numerous roles and responsibilities, processes and tools.

The implementation of a data governance is therefore, a chapter that any data-driven company must write.

However, our belief that the approaches to data governance from recent years have not kept their promises is borne out by our own field experience along with numerous and ongoing discussions with key data players.

At Zeenea, we strongly believe in adopting a different approach to maximize the chances of success. Our Professional Services and Customer Success teams provide our customers with the expertise they need to build effective data governance, through a more pragmatic and iterative approach that can adapt to a constantly changing environment.

We call it the Zeenea Effective Data Governance Framework.

Our beliefs on data 

Awareness of the importance of data is a long journey that every company has to make. But each journey is different: company data maturity varies a lot ; expectations and obligations can also vary widely.

Overall success will come about with a litany of small victories over time.

We have organized our framework in 3 steps.

With both the increasing need for digital transformation and power of IT solutions, the place of data in corporate strategies is exploding. A reality that makes the notion of data governance an unavoidable priority. Here’s a look back at a challenge that will remain crucial in 2021!

With the growing importance of new technologies, companies are at a crossroads. On the one hand, they are collecting and producing huge volumes of data. On the other hand, they must be able to harness the full richness of this data to adapt to their markets in real time.

The challenge? Implement robust data governance strategies to ensure not only the accuracy and relevance of data, but also its reliability and security. 

But the challenge does not stop there! They must also provide their teams, internally, with the information they need to fulfill their missions.  According to estimates published in Statista’s Digital Economy Compass 2019, the annual volume of data created worldwide has increased more than twenty-fold between 2010 and 2020, and reached 50 zettabytes this year! 50 zettabytes, that’s 500 million 100TB hard disks. A dizzying figure, which only goes to illustrate the importance of defining a real data governance policy. 

The question is not limited to a simple concern for storage or security, but also and above all for the exploitation of the data. An exploitation that allows the company to develop a precious asset to facilitate the daily life of its teams and the satisfaction of its customers! 

In fact, Gartner stated: “The uncertainty ushered in by 2020 will stay with us for multiple years to come. But with disruption comes enormous opportunity to not just restart what we used to do, but forge new paths. Data and analytics leaders that thrive will design and execute on a strategy that accelerates change, builds resilience and optimizes business impact.”

Starting Data Governance

No one doubts the importance of a data governance policy anymore. The COVID-19 crisis is a clear illustration of this. Health data are critical to controlling the epidemic and when governance is not properly in place, the consequences can be disastrous.

Strictly speaking, data governance is the overall management of the availability, usability, integrity and security of the data used in an organization. But behind this principle, there are the facts… and organizational or technical difficulties. Within a company, the definition of an appropriate data governance policy must rely on the right people. The team in charge of the data governance policy guarantees the determination of standards, the use and integration of data between projects, domains and business sectors… A demanding mission that requires taking up complex challenges.

Meeting today’s data governance challenges

Since the place of data is central to the life of a company, it is, more than ever, essential to abolish the silos that too often hinder the optimal use of data. This is the very heart of a data governance project: ensuring that data becomes valuable information. A challenge that involves democratizing data access to non-IT profiles.

All business departments must be able to manipulate, exploit and interrogate data. 

To achieve this, the solutions deployed in organizations must offer an intuitive and ergonomic experience. But behind the sharing of information, which brings with it the notion of data quality, there is the constant challenge of securing data… especially when your employees are not physically present in the company and access this strategic asset from home, for example. Identity management and compliance with “best practices” in terms of IT security must be the subject of constant support. This support must be the immediate counterpart to the development of an internal culture of data governance. 

Developing policies, procedures and practices that enable effective control and protection of data, while at the same time strengthening the way it is handled and used, is the DNA of a Data Governance policy! 

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!

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) 

Compliance

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.

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.