Data literacy has been a trending topic for a few years, and it is known that it is a vital skill for enterprises seeking to fully transform their organizations and become data-driven. 

As technology can be a point of failure if not handled properly, it is often not the most important roadblock to progress. In fact, in Gartner’s annual Chief Data Officer survey, the top roadblocks for success were cultural factors – human, skills, and data literacy. 

However many of these enterprises still struggle to understand what data literacy truly is, or know how to reshape their cultural organization into a data literate one. 

In its 2020 survey, New Vantage Partners observed that:

“Companies continue to focus on the supply side for data and technology, instead of increasing demand for them by business executives and employees. It’s a technology push rather than a pull from humans who want to make more data-based decisions, develop more intelligent business processes, or embed data and analytics into more products and services.”

In this article, we’d like to shed light on what data literacy is, why it is important for your enterprise, and tips on how to become a data literate organization.  

The definition of data literacy

Just as literacy means to have “the ability to read for knowledge, write coherently and think critically about printed material” data literacy is the ability to consume for knowledge, produce coherently and think critically about data.

In 2019, Gartner defined data literacy as: “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.”

So, based on these definitions, we can conclude that data literate people can, among other things:

• make analyses using data, 
• use data to communicate ideas for new services, products, workflows or even strategies,
• understand dashboards (visualizations for example),
• make data-based decisions rather than based on intuition

In summary, being data literate signifies having the set of skills to be able to effectively use data individually and collaboratively. 

Why is data literacy important?

Gartner expects that, by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies. By 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value.

The increasing volume and variety of data that businesses are flooded with on a daily basis require employees to employ higher order skills such as critical thinking, problem-solving, computational, and analytical thinking using data. And as organizations become more data-driven, poor data literacy will become an inhibitor to growth. In fact, in their survey “The Human Impact of Data Literacy”, Accenture found that: 

• 75% of employees are uncomfortable when working with data.
• 1/3 of employees have taken a sick day from work due to headaches working with data.
• A lack of data literacy costs employers 5 days of productivity translating to billions of dollars in lost productivity per employee each year.

 Furthermore, a Deloitte survey conducted in 2019 found that 67% of executives are not comfortable accessing or using data resources.

Data uplifts the success of organizations in creating both physical and digital business opportunities—improving accuracy, increasing efficiency and augmenting the ability of the workforce to deliver greater value. It is therefore important and essential to be able to interpret, analyze and communicate findings on data to be able to uncover the secrets to successful business and competitive advantage. 

Tips on how to become data literate

In order to build a successful data literacy program, here are some tips to help your organization on your data fluency journey:

Tip #1 – Develop a data literacy vision and associated goals

Any organization investing in data and AI capabilities should have already undertaken the creation of a data vision and  roadmap. In the process of doing so, data and IT leaders will have identified and prioritized the areas of business where data can produce value.

These steps are critical to creating a data-literate organization and reducing the friction around understanding and using data.

Management and HR need to communicate across the entire enterprise that data is a strategic asset that creates value. Using the data vision and roadmap as context, they should be able to explain to all employees why data matters, how it creates value, and how it impacts the business.

The absence of a clear vision for data and a plan to create value out of it, will create frustration and, as a consequence, employees will lack understanding of why they are being asked to make efforts and therefore, not have the motivation to do so.

In addition, a data literacy vision should detail desirable skills, abilities, and the level of literacy required for different business units and roles.

Business, IT, and HR leaders need to create a framework to achieve literacy goals, measure progress, and create a way to maintain data literacy.  This includes deciding what skills are required, how to measure & track skills development, and to what degree different parts of the organization should use data in achieving their strategic objectives.

Tip #2 – Asses workforce skills

Data literacy skills should ideally be assessed during the recruitment process for new hires.  In this way, HR will already know what kind of data literacy learning should be offered to the new hire over time.

However, for already existing employees, HR can map current employee data skills based on the roles and responsibilities provided in the above steps, and determine where there are gaps. 

Tip #3 – Create data literacy modules

According to Qlik, only 34% of firms provide data literacy training.

In most cases, the HR department is responsible for helping business managers identify and track areas of improvement and development opportunities for employees. They are also in charge of organizing the procedures for learning specific organizational skills as well as the time it takes. It’s no different when it comes to becoming data literate.

Once HR and managers have a general idea of an employee’s or a business unit’s strengths and weaknesses in data skills, HR can begin to construct personalized and efficient learning programs that allow employees to upskill in data and analytics responsibilities.

Tips #4 – Track, measure, and repeat

A successful data literacy program takes time to put in place. Business leaders must allow their employees to invest the time required to become data literate and improve their skills.  Over time, data thinking will become part of the corporate culture.

Finally, it’s important to communicate data literacy progress across the enterprise and on an individual basis. Tracking and communicating on the progress is key to continuing the evaluation of your organization’s data roadmap, vision and literacy.

This type of long-term planning and investment in educating the entire organization about how to access, understand and analyze data on the job will accelerate the efforts and investment that data science, machine learning and AI teams are making.

The results of data literacy efforts will allow organizations to finally be able to embrace and leverage data across the enterprise and for maximum value! 

When talking about data management, we often speak of the term “data preparation”.

According to SearchBusinessAnalytics, data preparation is the process of gathering, combining, structuring and organizing data so it can be analyzed as part of data visualization, analytics and machine learning applications. In other words, it is the process of cleaning and transforming raw data prior to analysis.

Data preparation is often a lengthy process for data and business users, but nevertheless essential in order to give context to data and turn it into valuable business insights. In 2016, Forbes said that 76% of data scientists stated that data preparation is the worst part of their jobs! However, accurate business decisions can only be made through the analysis of clean data.

How data preparation works

Data preparation is an essential part of many enterprise applications maintained by IT, such as data warehousing or business intelligence. It is also a practice conducted by the business for ad hoc reporting and analytics, with IT and tech-savvy business users, such as data scientists, routinely burdened by requests for customized data preparation. 

These days there’s growing interest in empowering business users with self-service tools for data preparation – so they can access and manipulate data sources on their own, without technical proficiency. 

The steps for data preparation are the following:

Step 1: Access and gather data

The first step in data preparation is to be able to access data from any source, no matter the origin, narrative or format. The optimal solution for giving enterprise-wide access to data is by implementing a data catalog solution. This essential tool is the key to starting your data preparation journey. 

>> For more information on Zeenea Data Catalog <<

Step 2: Discover data

After accessing and gathering data, the next step is to discovery data. Data discovery allows enterprises to adequately assess the full data picture. It helps all employees understand their data and their context through metadata. It is also very useful for enterprises seeking better compliance management. It allows organizations to know what data is personal/sensitive and where it can be found. In addition, data discovery can bolster innovation, as it unblocks essential information for satisfying customers and gaining competitive advantage.

Step 3: Cleanse data

Traditionally the most time-consuming part of data preparation, cleaning up data is nevertheless one of the most important tasks for removing bad data. Bad data can include outdated data, duplicate data, unreliable data, etc. Cleansing data therefore includes tedious tasks such as filling in missing information, making data private or sensitive, adding descriptions, and standardizing data patterns. 

Step 4: Enrich data

After cleansing all the data, it is time to start transforming and enriching the data. This step includes connecting your data with other related data sources to provide deeper insights. A data catalog is also an important part of this step in data preparation. 

>> More information on Zeenea’s connectors <<

Step 5: Store data

The last step in data preparation is to store data. By correctly storing your enterprise data, this enables data teams to be able to use fresh, clean data for their analysis. 

The Future of Data Preparation

Initially focused on analytics, data preparation has evolved to address a much broader set of uses cases and can be used by a larger range of users.

Although it improves the personal productivity of whoever uses it, it has evolved into an enterprise tool that fosters collaboration between IT professionals, data experts, and business users.

In this age where data is all around us, organizations have increasingly been investing in data management strategies in order to create value and gain competitive advantage. However, according to a study conducted by Gemalto in 2018, it was found that 65% of organizations can’t analyze or categorize all the consumer data they store.

It is therefore crucial for enterprises to look for solutions that allow them to seek out the value of their data from the metrics, insights and information by facilitating their data discovery journey.

Data discovery definition

Data discovery problems are everywhere in the enterprise, whether it’s in the IT, Business Intelligence or Innovation department. By integrating data discovery solutions, enterprises provide data access to all employees, enabling Data teams and Business analysts to understand and thus, collaborate on data related topics.

It is also very useful for enterprises seeking better compliance management. It allows organizations to know what data is personal/sensitive and where it can be found. In addition, data discovery can bolster innovation, as it unblocks essential information for satisfying customers and gaining competitive advantage.

From Manual to Smart Data Discovery

For 20 years, before advanced machine learning techniques, data specialists mapped their data using the sole brain power of humans! They critically thought out about what data they had, where it was stored, and what are the needs to be provided to the end customer. Data Stewards usually took care of data assets documentation rules and standards that guided the data discovery process. In these manual approaches, usually done using Excel sheets, people conceptualized and drew out maps to comprehend their data’s organization.

Nowadays, with the advancement of technology, the definition of data discovery includes automated ways of presenting data. Smart Data Discovery represents a new wave of data technologies that use augmented analytics, Machine Learning and Artificial Intelligence. It not only prepares, conceptualizes and integrates data, but also presents it through intelligent dashboards to reveal hidden patterns and business insights.

The benefits of data discovery

Enterprise data moves from one location to another in the speed of light, and is being stored in various data sources and storage applications. Employees and partners are accessing this data from anywhere and anytime, so identifying, locating and classifying your data in order to protect it and gain insights from it should be the priority!

The benefits of data discovery include:

• A better understanding of enterprise data, where it is, who can access it and where, and how it will be transmitted,
• Automatic data classification based on context,
• Risk management and regulatory compliance,
• Complete data visibility,
• Identification, classification, and tracking of sensitive data,
• The ability to apply protective controls to data in real time based on predefined policies and contextual factors

Data discovery enables enterprises to adequately assess the full data picture.

On one hand it helps implement the appropriate security measures to prevent the loss of sensitive data and avoid devastating financial and reputational consequences for the enterprise. On the other, it enables teams to dig deeper into the data to identify the specific items that reveal the answers and find ways to show answers. It’s a win-win situation!

Learn more about data discovery in our white paper: “Data Discovery through the eyes of Tech Giants”

Discover the various data discovery solutions developed by large Tech companies, some belonging to the famous “Big Five” or “GAFAM”, and how they helped them become data-driven.

”There aren’t enough expert data scientists to meet data science and machine learning demands, hence the emergence of citizen data scientists. Data and analytics leaders must empower “citizens” to scale efforts, or risk failure to secure data science as a core competency”. – Gartner 2019

As data science provides competitive advantages for organizations, the demand for expert data scientists is at an all-time high. However, supply remains pretty scarce for that demand! This limitation is a threat for enterprises’ competitiveness, and in some cases, their survival in the market.

In response to this challenge, an important analytical role providing a bridge between data scientists and business functions was born: the citizen data scientist.

What is a citizen data scientist?

Gartner defines the citizen data scientist as “an emerging set of capabilities and practices that allows users to extract predictive and prescriptive insights from data while not requiring them to be as skilled and technically sophisticated as expert data scientists”. A “Citizen Data Scientist” is not a job title. They are “power users” who can perform both simple and sophisticated analytical tasks.

Typically, citizen data scientists don’t have coding expertise but can nevertheless build models using drag-and-drop tools and run prebuilt data pipelines and models using tools such as Dataiku. Be aware: citizen data scientists do NOT replace expert data scientists! They bring their own expertise but do not have the specialized expertise for advanced data science.

The citizen data scientist is a role that has evolved as an “extension” from other roles within the organization! This means that organizations must develop a citizen data scientist persona. Potential citizen data scientists will vary based on their skills and interest in data science and machine learning. Roles that filter into the citizen data scientist category include:

• Business analysts
• BI Analysts / Developers
• Data Analysts
• Data Engineers
• Application Developers
• Business line manager

How to empower citizen data scientists?

As expert skills for data science initiatives tend to be quite expensive and difficult to come by, utilizing a citizen data scientist can be an effective way to close the current gap.

Here are ways you can empower your data science teams:

Break enterprise silos

As I’m sure you’ve heard this many times before, many organizations tend to operate independently in silos. Mentioned above, all of roles are important in an organization’s data management strategy, and they all have expressed interest in learning about data science and machine learning skills. However, most data science and machine learning knowledge is siloed in the data science department or specific roles. As a result, data science efforts are often invalidated and unleveraged. Lack of collaboration between data roles makes it difficult for citizens data scientists to access and understand enterprise data!

By establishing a community of both business and IT roles that provides detailed guidelines and/or resources allows enterprises to empower citizens data scientists. It is important for organizations to encourage the sharing of data science efforts throughout the organization and thus, break silos!

Provide augmented data analytics technology

Technology is fueling the rise of the citizen data scientist. Traditional BI vendors such as SAP, Microsoft and Tableau Software, provide advanced statistical and predictive analytics as part of their offerings. Meanwhile, data science and machine learning platforms such as SAS, H2O.ai and TIBCO Software, provide users that lack advanced analytics capabilities with “augmented analytics”. Augmented analytics leverages automated machine learning to transform how analytics content is developed, consumed and shared. It includes:

Augmented data preparation: machine learning automation to augment data profiling and quality, modeling, enrichment and data cataloguing.

Augmented data discovery: enables business and IT users to automatically find, visualize and analyse relevant information, such as correlations, clusters, segments, and predictions, without having to build models or write algorithms

Augmented data science and machine learning: automates key aspects of advanced analytics modeling such as feature selection, algorithm selection and time-consuming step processes.

By incorporating the necessary tools and solutions and extending resources and efforts, enterprises can empower citizen data scientists!

Empower citizen data scientists with a metadata management platform

Metadata management is an essential discipline for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets. By implementing a metadata management strategy, where metadata is well-managed and correctly documented, citizen data scientists are able to easily find and retrieve relevant information from an intuitive platform.

Discover our tips for starting metadata management in only 6 weeks by downloading our new white paper “The effective guide to start metadata management”!

In 2019, the Data Scientist was named most promising job by LinkedIn. From Fortune 500 companies to small enterprises all around the world, building a team of data science professionals was a priority in their business strategies. To support this claim, the year 2019 broke all records of AI & data science investment!

Despite all of these positive trends, Data Scientists are quitting and changing companies at a rapid pace. How come? Let’s analyze the situation.

They don’t spend their time doing what they were hired for

Unfortunately, many companies that hire data scientists do not have a suitable AI infrastructure in place. Surveys still suggest that roughly 80% of data scientists’ time is spent on cleaning, organizing and finding data (instead of analyzing it), which is one of the last things they want to spend their time doing! In their article “How We Improved Data Discovery for Data Scientists at Spotify”, Spotify explains how in the beginning their “datasets lacked clear ownership or documentation making it difficult for data scientists to find them.” Even data scientists working for Web Giants have felt frustration in their data journey!

Most data scientists end up leaving their companies because they end up filtering the trash in their data environments. Having clean and well documented data is key for your data scientists to not only better find, discover and understand the company’s data but also save time on fastidious tasks and produce actionable insights!

Business and Data Science goals are not aligned

With all the hype around AI and Machine Learning, executives and investors want to showcase their data science projects at the forefront of the latest technological advances. They often hire AI and data experts thinking that they will reach their business objectives in double the time. However, this is rarely the case! Data science projects typically involve a lot of experimentation, trial & error methods and iterations of the same process before reaching the final outcome.

A lot of companies increase their hiring of data specialists in order to increase the research and insight production across their company. However, this research often only has a “local impact” in specific parts of the enterprise, going unseen by other departments that might find it useful in their decision making!

It is therefore important for both parties to effectively & efficiently work together by establishing solid communication. Aligning business objectives with data science objectives is the key to not lose your data scientists. By using a Data Ops approach, data scientists are able to work in an agile, collaborative and change-friendly environment that promotes communication between both the business and IT departments.

They struggle to understand & contextualize data at enterprise level

Most organizations have in place numerous complex solutions, usually misunderstood by the majority of the enterprise, making it difficult to train new data science employees. Without a unique centralized solution, data scientists find themselves going through a various different technologies, losing sight of what data is useful, up-to-date, and of quality for their usages.

This lack of visibility on data is frustrating to data scientists whom, as mentioned above, spend the majority of their time looking for data in multiple tools and sources.

By putting in place a single source of truth, data science experts are able to view their enterprise data in and produce data-driven insights.

Accelerate your data scientists work with a metadata management solution

Metadata management is an essential discipline for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets. By implementing a metadata management strategy, where metadata is well-managed and correctly documented, data scientists are able to easily find and retrieve relevant information from an intuitive platform. Empower your data science teams by providing them with the right tools that enables them to create new machine learning algorithms for their data projects and thus, value for your enterprise.

Discover our tips for starting metadata management in only 6 weeks by downloading our new white paper “The effective guide to start metadata management”!

In the data world, a business glossary is a sacred text that represents long hours of hard work and collaboration between the IT & business departments. In metadata management, it is a crucial part of delivering business value from data. According to Gartner, It is one of the most important solutions to put in place in an enterprise to support business objectives.

To help your data scientists with their machine learning algorithms and their data initiatives, a business glossary provides clear meanings and context to any data or business term in the company.

Back to basics: what is a business glossary?

A business glossary is a place where business and/or data terms are defined and accessible within the entire organization. As simple as it may sound, it is actually a common problem; not all employees agree or share a common understanding of even basic terms such as “contact” or “customer”.

Its main objectives, among others, are to:

• Use the same definitions and create a common language between all employees,
• Have a better understanding and collaboration between business and IT teams,
• Associate business terms to other assets in the enterprise and offer an overview of their different connections,
• Elaborate and share a set of rules regarding data governance.

Organizations are therefore able to have information as a second language.

How does a business glossary benefit your data scientists?

Centralized business information allows to share what is essentially tribal knowledge, around an enterprise’s data. In fact, it allows Data Scientists to make better decisions when choosing which datasets to use. It also allows:

A data literate organization

Gartner predicts that by 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs. Increasingly, organizations are realizing this and beginning to look at data and analytics in a new way.

As part of the Chief Data Officer job description, it is essential that all parts of the organization can understand data and business jargons. It helps all parts of the organization to better understand a data’s meaning, context, and usages. So by putting in place a business glossary, data scientists are able to efficiently collaborate with all departments in the company, whether IT or business. There are less communication errors and thus they participate in the construction and improvement of knowledge of the enterprise’s data assets.

The implementation of a data culture

Closely related to data literacy, data culture refers to a workplace environment where decisions are made through emphatic and empirical data proof. In other words, executives make decisions based on data evidence, and not just on instinct.

A business glossary promotes data quality awareness and overall understanding of data in the first place. As a result, the environment becomes more data-driven. Furthermore, business glossaries can help data scientists gain better visibility into their data.

An increase in trusting data

A business glossary ensures that the right definitions are used effectively for the right data. It will assist with general problem solving when data misunderstandings are identified. When all datasets are correctly documented with the correct terminology that is understood by all, it increases overall trust in enterprise data, allowing data scientists to efficiently work on their data projects.

Their time is less spent on cleaning and organizing data, but rather on bringing valuable insights to maximize business value!

Implement a Business Glossary with Zeenea

Zeenea provides a business glossary within our data catalog. Our business glossary automatically connects and imports your glossaries and dictionaries in our tool with our APIs. You can also manually create a glossary within Zeenea’s interface!

Check our business glossary benefits for your data scientists!

Exploding quantities of data have the potential to fuel innovation and produce more value for organizations. Stimulated by the hopes of satisfying customers, enterprises have, for the past decade or so, invested in technologies and paid handsomely for analytical talent. Yet, for many, data-driven culture remains elusive, and data is rarely used as the basis for decision making.

The reason is because the challenges of becoming data-driven aren’t technical, but rather cultural. Describing how to inject data into decision-making processes is far more easier than to shift an entire organization’s mindset! In this article, we describe five ways to help enterprises create and sustain data culture at its core.

By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.

What is data culture?

”Data culture” is a relatively new concept that is becoming increasingly important to put in place, especially for organizations developing their digital and data management strategies. Just like organizational or corporate culture, data culture refers to a workplace environment where decisions are made through emphatic and empirical data proof. In other words, executives make decisions based on data evidence, and not just on instinct.

Data culture gives organizations more power to organize, operate, predict, and create value with their data.

>> Check out our webinar: Why does data culture matter <<

Here are our five tips for creating and sustaining data culture:

Step 1: Align with business objectives

The fundamental objective of collecting, analyzing, and deploying data is to make better decisions.” (McKinsley)

Trusting your data is one of the most important tips for building data culture, as distrust in data leads to disastrous organizational culture. And to trust in data, it must align with business objectives. To drive strategic and cultural changes, it is important for the enterprise to agree on common business goals, as well as the relevant metrics to measure achievements or failures across the entire organization.

Ask yourself the right questions: How can we not only get ahead of our competitors, but also maintain the lead? What data would we need to decide what our next product offering should be? How is our product performing in the market? By introducing data into your business decision-making process, your enterprise will have already made the first step to building a data culture.

Step 2: Destroy data silos

In this case, data silos refer to departments, groups, or individuals who are the guardians of data, who don’t share, or don’t know how to share, data knowledge with other parts of the enterprise. When crucial information is locked away and available to only a few connaisseurs, it prevents your company from developing a cross-departmental data culture. It is also problematic on a technical standpoint: multiple data pipelines are harder to monitor and maintain, which leads to data being stale and obsolete by the time anyone uses it for decision making.

To break data silos, enterprises must put in place a single source of truth. Empower employees to make data-driven decisions by relying on a centralized solution. A data catalog enables both technical and non-technical users to understand and trust in the enterprise’s data assets.

>> Check out our blog post: What is a data catalog? <<

Step 3: Hire data-driven people

When building a data culture, it’s important to hire data-driven people. Enterprises are reorganizing themselves, forcing the creation of new roles to support this organizational change:

Data Stewards

Data Stewards are here to orchestrate an enterprise’s data systems. Often called the “masters of data”, they have the technical and business knowledge of data. Their main mission is to ensure the proper documentation of data and facilitate their availability to their users, such as data scientists or project managers for example.

This profession is on the rise! Their social role allows data stewards to work with both technical and business departments. They are the first point reference for data in the enterprise and serve as the entry point to access data.

Chief Data Officers

Chief Data Officers, or CDOs for short, play a key role in the enterprise’s data strategy. They are in charge of improving the organization’s overall efficiency and the capacity to create value around their data. 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. But 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 of Data Democracy!

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.

Step 4: Don’t neglect your metadata

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 metadata allows for enterprises to assure greater Data quality & 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.

Step 5: Respect the various data regulations

If you’re in Europe, this is old news by now. With the GDPR put into place in May 2018 as well as all of the other various regulations slowly seeing the day in the United States, UK, or even Japan, it is important for enterprises to respect and follow the guidelines to conform.

>> If you aren’t sure if you conform, check out our articles on GDPR compliance <<

Implementing data governance is a way to ensure that all personal data privacy, data security, and ensure risk management. It is a set of practices, policies, standards, and guides that will supply a solid foundation to ensure that data is properly managed thus, creating value within an organization.

Step 6 BONUS TIP: Choose the right solutions

Metadata management is the new black: it is an emerging discipline, necessary for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets. A metadata management solution offers enterprises a centralized platform to empower all data users in their data culture implementation.

For more information on metadata management, contact us!

In order to optimize their business strategies and improve productivity, data-driven organizations are seeing a shift in their data management practices; going from managing and maintaining data, to also managing and maintaining metadata. However, Gartner states that only 5% to 20% of enterprises are equipped with metadata management solutions! This enterprise level discipline is therefore, an essential practice that will continue to develop for the next few years:

”By 2023, 80% of organizations will require solutions that respond to the needs and use cases of their business personas.” – Gartner.

The challenges & concerns of metadata management solutions for enterprises

Current metadata management solutions in the market fall short in responding to new organization concerns. They usually face:

• A lack of adoption of current metadata management solutions,
• A lack of confidence in the data being analyzed,
• Inability to find data in an organization’s data ecosystem,
• Solutions that are designed for a technical user and not a business user.

These findings, made by various enterprises with current metadata management solutions, were pretty harsh, and for the right reasons! In the search for creating a data democracy culture, enterprises find themselves stuck with very technical tools aimed for the IT department, usually abandoned by business users or misunderstood by the enterprise in general. This technology-driven approach of metadata management leads to a data literacy gap!

Yet, if enterprises seek implementing metadata management, then they have already realized the value of metadata in the first place. So, the problem here isn’t the discipline itself, but rather the dissatisfaction and the irrelevance of the chosen tool and its characteristics.

The six elements to look out for when choosing a metadata management solution

The original target markets of any metadata management solution were the IT departments searching for a better way to understand data. However, as time passes, other business functions have become more involved with owning or working with data and metadata. Among these personas are data stewards, data analysts, business analysts, data scientists, data architects and data engineers.

Here are six elements that are essential for a adopting a successful long term metadata management strategy:

A personalized user experience

As mentioned above, the data literacy gap is still too wide, and as a response, metadata management solutions must have features and functionalities that support data literacy goals in a business-friendly way. Searching capabilities are very effective in a metadata management solution. Through a Googlesque search engine, data users are able to find relevant information via simple keywords or phrases. Helps menus, drag and drops and wizards are also other common examples.

Metadata management solutions that include artificial intelligence and machine learning features allow for more customizable, user-friendly experiences. As the information requested and viewed by data users varies from person to person, it is important that a metadata management solution offers adaptive & personalized interfaces based on their usages. This information should be displayed with easy and eye-catching visual representations in order to avoid spending too much time trying to understand the data.

Role & Access support

In today’s world, data users often change roles and enterprises find themselves spending most of their time re-configuring who has access to what information. Adopting a successful metadata management solution means being able to easily configure and define roles & edition modes within the platform.

This allows for an overview of all users: who are my data stewards? Who are my data users on X project? Who is the owner of this dataset so I can request permission to access this information? Who last updated this dataset? Role and access management is essential for long term metadata management.

Reporting features

One of the driving factors for organizations seeking metadata management solutions is the need to gain trusted results from the data flow & analytics. These solutions should have dashboards that are relevant and understandable to business users, associated with their business use case.

Supported by a data catalog, these reporting features give information on whether it’s a valuable solution for your enterprise. A data catalog reports the volume of data collected, the number of users it has, the frequency to which users connect to the catalog, how many times a dataset’s been viewed or even its frequently asked questions. It can also provide information regarding data documentation, for example it’s completion level, whether it contains personal information, etc.

A business glossary

As mentioned above, metadata management solutions should enable business users to navigate through content aligned with their use cases. This includes fields and labels that these users can create themselves, rather than adhere to the tool’s taxonomie and semantics.

Business glossary functionalities must provide personalized and modular templates when creating data terms and taxonomies. Wikis and articles within the tool are not enough! A successful business glossary enables data leaders to build and manage a common business vocabulary and make it available across the entire organization.

Compliance with data regulations

Governance and compliance in general are major drivers for acquiring metadata management solutions.

When choosing a metadata management platform, if your main use case is data governance, it is important to seek solutions that offer automated capabilities regarding your enterprise’s personal information. Automated notifications and data fingerprinting technologies provide augmented cataloging and stewardship capabilities for better data governance.

Collaborative capabilities

Social features are a must for companies seeking metadata management. Discussions, ratings, notes, popularity, notifications and messaging capabilities are all important elements to have. For example, social capabilities allow users to easily communicate with data stewards or specific experts linked to a dataset or project.

Moreover, collective intelligence allows enterprises to leverage crowdsourced information and knowledge. With collaborative features enterprises are able to “archive” previous knowledge by stocking it. Companies thus create data communities and a more data literate organization!

Start metadata management in just 6 weeks!

When it comes to metadata management, Zeenea’s got your back. In this white paper, we share our advice and expertise on implementing iterative metadata management optimized for your context.

Data management can be defined as the process of ingesting, storing, organizing and maintaining all data created and collected by an organization in order to help drive operational decision-making and strategic planning.

It won’t be a surprise if we tell you that data topics are constantly evolving and becoming more complex within organizations! As a result, any organization considering these large-scale data and analytics initiatives is increasingly faced with high-volume data of various types, formats, and distributed environments.

In an attempt to maximize its value, metadata is a response that provides knowledge about where the data is located, what attributes it has, or how it is linked (also called a knowledge graph). Yet most organizations do not yet have a formal approach to metadata management. 

Let us convince you of its necessity in this article…

The challenges of metadata in a next-gen data management

In an increasingly dispersed and complex technology environment, Data Managers or, Chief Data Officers, are tasked with providing and simplifying a consistent data environment that can be activated by their teams. 

 Among our clients who have taken the gamble of initiating metadata management, we see a common objective: to ensure the visibility of different data sources and initiatives and to involve new players who do not necessarily have technical profiles.

In short, the need to align semantics across multiple data silos is driving an increased demand for metadata governance capabilities. 

See in this new discipline of data management, a lever to better describe your data, by including information on its location in order to facilitate the use and/or protection in diverse environments and sources.

Here is an excerpt of the questions your metadata will be able to answer:

• Who created this data?
• Who is responsible for this data?
• In what applications is it used?
• What is the level of reliability (quality, speed, etc.) of this data?
• What are the permitted contexts of use (e.g. confidentiality)?
• Where is the data located?
• Where does this data come from (a partner, open data, internally, etc.)?

Our recommendations to data management stakeholders

For those who are today approaching metadata management as part of data management strategies, we advise to :

• Progressively deploy an enterprise data catalog by adopting metadata management practices. The use of data catalogs will allow, among other things, to inventory all forms of metadata – technical, but also increasingly business, operational and social – in order to improve the visibility of data management activities.

• Work with suppliers who are able to accept this diversity in their systems and operate in distributed, independent and increasingly cloud-connected data management infrastructures. 

• Identify metadata management use cases that can be easily activated in order to quickly prove its value. The solution providers selected should be those that automate the discovery, profiling and inventorying of metadata or at least the most tedious of tasks.

Metadata management is emerging discipline and is necessary for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets.

Many of them are trying to establish their convictions on the subject and brainstorm solutions to meet this new challenge. As a result, metadata is increasingly being managed, alongside data, in a partitioned and siloed way that does not allow the full, enterprise-wide potential of this discipline.

1. How to successfully launch a metadata management platform
2. How do manage metadata
3. How to start metadata management

To value this “new” discipline in your organization, you need to demonstrate its ability to deliver value from its outset! At Zeenea, we offer strong data catalog support to produce value in a very short time frame, in most cases a matter of a few weeks.

In this article, we deliver an approach facilitated by a solution like Zeenea: connected, agile and agnostic about the technologies used in enterprises

Set a milestone

For each milestone, you will be asked to identify several elements:

• What are the problems: Increasing and sharing knowledge must solve a problem. It can be of various types: compliance for a scheduled audit, centralization and uniformity of a particular piece of information to satisfy a group of collaborators in a difficult situation, for example.

• What data: It is important to focus efforts on data directly related to the identified problem. Trying to deal with too large a data set will lengthen the time frame and ultimately extend the time at which the achievement of the objective, and therefore the production of value, could be measured.

• Who are my data users: On the first iteration, which may be longer than any other, the mobilized users will be structuring. They must be able to free up enough time to invest themselves in achieving the objective, but they must also have the motivation to set up metadata management in place. These users will be your first ambassadors moving forward.

• What time do I have: This iteration must be completed within a reasonably short period of time. On an enterprise-wide basis, we recommend a timeframe between 4 weeks to 3 months maximum, depending on the bandwidth of the people in charge of the subject. This duration should also help to qualify whether a particular problem is appropriate or should be subdivided, or simply discarded.

Sometimes, even before the first milestone is identified, a preliminary introspective exercise on the enterprise’s data governance maturity is carried out.

We suggest this through workshops during which the company, with the help of our maturity matrix, will be able to define its positioning. This type of exercise is of particular interest when it is carried out regularly (e.g. every year). It allows a global assessment of the benefits of deploying your governance program.

Launch the first milestone

Typically, the chronological sequence for the onboarding phase supported by our metadata management tool is the following:

Our desire is to anchor the launch in a value-producing reflex. Each iteration must bring the company tangible benefits that address your issues. This first iteration includes elements that will not appear anymore, or at least much less, in the following iterations, in particular the technical aspects related to the implementation of the solution.

We suggest by default iterations of 6 weeks. This duration, which is fairly arbitrary, corresponds relatively well to the time generally required to produce significant value while not disrupting too much the activity of the people involved. Indeed, it is necessary to keep in mind that it is rare that the mobilized collaborators have full time to deal with the subject.

Metadata management is emerging discipline and is necessary for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets.

Many of them are trying to establish their convictions on the subject and brainstorm solutions to meet this new challenge. As a result, metadata is increasingly being managed, alongside data, in a partitioned and siloed way that does not allow the full, enterprise-wide potential of this discipline.

1. How to successfully launch a metadata management platform
2. How do manage metadata
3. How to start metadata management

Is your organization in the process, or in the process of reflecting, the development of a metadata management solution? Metadata management is essential to meet the growing demands of data governance, data risk and compliance, as well as data analysis and value generation.

In order to support this discipline, you will have to choose a metadata management platform. Simply put, these solutions must enable data managers to capture, store and aggregate metadata from the enterprise IS on a single platform.

You will soon realize that the market is complex: the solutions are diverse and their scope or capacity more or less limited. Take some time to assess the functional capabilities of your metadata management solution to help you:

Centralize your efforts

Ensure that metadata efforts are not isolated but centralized and unified.

In this way you will avoid reproducing silos of information in the same way that data has been siloed in the past. To do this, check the platform’s operational ability to integrate and manage assets coming from different sources of information.

After all, metadata is everywhere in your IS: applications, relational or non relational databases, the cloud, business glossaries, data dictionaries or even Excel files!

Match your context

We are firmly convinced that it is not up to the company to comply with the solution’s documentation model, but rather the other way around!

By adopting a modular and customizable solution, you will be able to adjust, prioritize and add the missing and necessary elements for your data consumers. From this approach, take a step-by-step approach to incremental and iterative metadata governance that fits your context according to your priorities. So take some time to collect the needs and difficulties of your data users and consumers. Then, create a template of relevant documentation for your consumers that will ultimately guide your metadata collection efforts.

Quicken the pace (and well!)

We consider the automation of the ingestion and the intelligence of the solution as the key factors of success! These capabilities will allow you to automate the most tedious tasks, offer synergies in your data assets in order to document and contextualize your data in an ever increasing and better way. Under the regulatory prism, for example, an intelligent platform will be able to identify through its already existing documentation which data sets are considered «sensitive» and contain personal data.

Explore your metadata

Aggregating IS metadata into a platform only makes sense if it is shared across the enterprise and easily accessible by your data consumers. Data catalogs respond to this first use case:

The easier it is for the user to access the information, the better the data catalog! To enable analysts, data scientists and other data consumers to find and understand an enterprise’s data assets in order to extract value from them

Metadata management is emerging discipline and is necessary for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets.

Many of them are trying to establish their convictions on the subject and brainstorm solutions to meet this new challenge. As a result, metadata is increasingly being managed, alongside data, in a partitioned and siloed way that does not allow the full, enterprise-wide potential of this discipline.

1. How to successfully launch a metadata management platform
2. How do manage metadata
3. How to start metadata management

Before starting a metadata management project, here are some elements to consider:

Accepting failure

As strong as this title may be, fearing failure won’t avoid it. Being aware of risk and knowing how to integrate it into the approach is a crucial part of launching a metadata management platform.

To accept failure is to admit that the road will not be paved with simple and obvious steps

Implementing data governance around relevant metadata management is a complex subject, exacerbated by many factors: size and complexity of the organization, culture or sensitivity concerning data as a subject, awareness of the associated strategic issues, etc. Naturally, a complex subject entails a certain risk during operational execution…

Experimenting with your data environment

Metadata management is built gradually and no revelation will strike the team at its initialization. Only experimentation will make it possible to validate decisions.

In order to control the costs of these various experimentations, the most appropriate approach is to progress step by step. Accepting failure is not to resign oneself, on the contrary, it puts oneself in a position where efforts are not only focused on anticipation, but also on remediation and adaptation. Hypotheses are going to be validated successfully, while modifying a number of parameters as little as possible each time, and the conclusions, through measurement, will allow progress to be made.

This operation is wholly iterative and incremental. One thus finds fundamentals promoted in a rather general, agile way

Aligning with enterprise objectives

The objectives of your metadata governance can be local or global.

The approach may concern only a limited perimeter in the enterprise, and reflect a very local initiative, whereas conversely, it may be intended to apply to the enterprise as a whole. Even more so when they are global to the enterprise, and therefore often expressed in a rather generalist way, it is important to ensure that their implementation remains in line with the original idea

An important element in this equation is the human dimension! On the one hand because responsibilities will be identified, but also because certain processes will either have to evolve or be defined, or because the culture and preconceptions surrounding the data will have to be changed with a great deal of communication.

Nevertheless, adapting is in general, a healthy enterprise practice.

Prioritizing

Among the benefits of such an approach is, as mentioned above, better risk
control.

But there is another obvious benefit: the possibility of a faster return
on investment. The first effects should be noticeable as soon as the first iteration is complete.

The objective must be determined to produce concrete value for the enterprise.

Selecting the useful information

Being selective about the nature of the information characterizing the data helps with identifying your useful information. The temptation of an overly ambitious metamodel could actually be detrimental to the qualitative effort required of Data Stewards profiles.

We therefore recommend a very precise selection of metadata that meets the objectives given by iteration. The organization of knowledge in a metadata catalog will be optimized, both for the contributors and the Stewards, as well as for the users in search of information. Quality must take precedence over quantity, and the iterative approach will meet the expectations of enrichment progressively as deployment progresses.

Capitalize on your metadata!

Last but not least, your experimentation will result in local initiatives that may give rise to reflections on the generalization of all or part of the achievements.

To capitalize is to know how to identify what is in the common interest.