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What is Data Governance and Why Do You Need It?

What is Data Governance and Why Do You Need It.jpg
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This is where Data Governance comes into play – You need to organize your company, build knowledge on your actual company data, and promote data re-use to deliver the value connected to your data potential.

 

 

Discover the essential topics you need to know to make your data governance program a success!

What is the definition of data governance?

Data governance is a top-level business initiative with specific goals:

  • define a data governance strategy based on the company strategy, business priorities, and critical uses cases and,
  • define and manage how to implement this strategy to deliver value.

Data Governance aims to organize roles between business users (Data users and Data owners), IT department, and support teams (Data Office). Their objective is to create a data practice community and build necessary data knowledge at a business language level – based on the actual data in systems (Data management). This asset must be easy to access and reusable for any Business or Data Scientist to help communicate and promote Data Governance initiatives, its progress, and successes in establishing Data Literacy and Data Culture in the company.

Strategic evolving role of Data Governance.png

 

Roles: who is responsible for the enterprise data governance success?

Data Governance success relies on joint teamwork animated by the CDO with Business data users, data owners, and IT stakeholders (IT Department, Enterprise Architecture and Business Processes managers, Data Protection Officers, R&D, and Innovation leaders).

In a company, the board of directors will define a vision, a strategy, and critical business objectives and use cases. The Chief Data Officer’s mission is then to help the organization leverage data as a strategic asset, and support and animate the data community. Within the organization, even though every company is unique, here are the four most common data governance roles:

Chief Data Officer (CDO)

The Chief Data Officer (CDO) leads the data governance program – with his Data Office Team, and so is responsible for its success. He usually directly reports to the CEO and interacts with the board of Directors as a critical stakeholder.

The CDO’s objective is to master data knowledge, set up compliance with regulations, and provide the ability for data reuse and data innovation such as A.I.

A tale of two CDOs The converging roles of Chief Data Officers and Chief Digital Officers.png

 

Data Officers

Data Owners

The data owner is responsible for the data. Typically, as part of the business team, he is the principal contact for specific functional data. He uses data and benefits from data sharing. His main objectives are to be a data domain expert and to create data reuse projects for his domain and the whole company. A data steward helps him.

Data Stewards

The data steward holds the knowledge of the data and its metadata. He helps organizations set up data governance projects and improve data literacy. His main objectives are to make meaningful data catalogs and business glossaries for data reuse and data innovation, but also to support data reuse projects.

They are the ones filling the gap between IT (Information Technology) and business, and by doing so, improve collaboration and the usage of data.

Partners

Data Architect

Data architects create and model the organization's databases, the datalakes. They can ease data governance by helping CDOs with conceptual models and business terms. Their main objectives are to provide knowledge of applications, business processes, and strategic needs. They provide expertise in concept modeling.

Data Protection Officer

The DPO focuses on regulations and compliance. He ensures that data is relevant and safely used. His main objectives are to ensure compliance with data quality and appropriate use of data in the company. All this to avoid fines from regulators.

Data users for innovation

Data Scientist

Data scientists use data to build disruptive new use cases to create a competitive advantage for a company. His main objectives are to use reliable sources and linked data to provide innovative ideas and go quickly into production.

Key steps to implement an effective data governance program.png

 

What is a data governance framework?

Data governance frameworks are best practice referentials and norms used by CDOs to set up and implement their data governance programs.

Several data governance frameworks exist on the market, but the Data Management Book of Knowledge (DMBok V2) is the reference. The DMBok framework is a set of best practices for Data Management created by the international DAMA association members (Data Management professionals) aiming to enhance data management collaboratively.

The DMBok defines a data management framework in 11 subdomains with best practices for each and data governance maturity level assessments. It defines a data governance certification (Certified Data Management Professional) and is also linked to DMBok knowledge proficiency.

 

What are the strategic accelerators for building a Business Data Governance?

Data governance tools can accelerate data governance when leveraging on synergies with other strategic initiatives.Connected with enterprise architecture features, it can provide IT portfolio and solution architecture knowledge that is needed for data governance. Moreover, knowing IT solutions changes in advance helps to handle data governance maintenance and impact analysis in the long term.

Data governance tools connected with process modelling give a business view on data life cycles: where the data is created or entered, how it is used in which context? Linking process and data governance allows the company to be more customer and performance-oriented, and to adapt to change quicker.

Data Governance must deal with data compliance and quality. When linking with the wider Governance Risk and Controls initiatives – it helps to better assess risks and impacts, data importance, and classification.

 

Why does data governance matter?

Data Governance addresses many critical company topics:

Make decisions based on reliable data, and aligned with the company's objectives.

A data governance program helps implement business strategies based on data. The company defines a strategy with different objectives and in particular business use cases that materialize needs. It could be about enhancing customer service levels and creating new disruptive customer offers. It could be working on operational or financial performance. These use cases must be defined and prioritized with boards and with different business directions.

 

Comply with regulations and therefore avoid heavy fines and ensure Data Security

Mastering data knowledge and usage is usually one of the most common regulatory requirements companies have to face (BCBS 239, Basel III, Solvency II, GDPR, CCPA, …). These rules ensure the proper, fair, and ethical use of data for all contributors. Furthermore – Data Governance deep knowledge (where the data is stored, who is using it?... ) provides valuable information to enforce Data Security initiatives.

 

Allow Innovation based on Data

The higher operational performance or data innovation cannot be done without accurate and efficient data management. For example, Artificial Intelligence innovations cannot be built without good quality data: Predictions about customer behavior in this case aren’t stable enough to be used in real-life conditions. Therefore, the data governance program's role is to organize works and use case implementation. To be used or re-used, data must be known, and data quality must be ensured or enhanced. Data must be compliant with external regulations or internal policies.

 

Implementing a data governance program

A data governance program is a strategic initiative that follows a continuously improving cycle. Four main steps are needed to implement a data governance program:

 

Step 1 – Definition of the data governance strategy

A Data Governance program is a virtuous cycle starting from companies’ business needs. Once the strategy is defined at the board level, business needs and use cases are associated with data needs by the CDO. Business needs will help the CDO define the data governance strategy.

Key steps to implement an effective data governance program.png

 

Step 2 - Data governance assets knowledge management

Create a new organization with internal partners and users

The Chief Data Officer or Data governance manager is here to create a new organization to support Data Management and Data usage. The Data office is usually composed of data experts like Data Stewards, Data Custodians, or Data Architects. They are here to first create Data Catalogs and Business glossaries to allow data reuse. Besides, their aim is also to support business data users and implement projects. They typically work with Data Owners that are on the business side.

Focus first on the data needed to implement critical use cases

For each selected business use case, Data Stewards must define which data is needed. For example, have financial processes and data been compliant? Have customer offers been improved (based on customer data)? Depending on Uses Cases priorities, data governance planning can be defined to create Data Catalog and Business glossary in a step-by-step and interactive way.

Build Data asset knowledge and communicate progress with the community

Communicating data is an important duty for CDOs to set up a data culture (Data Literacy). Data work progress, data quality, and use case successes based on data must be promoted to motivate Data Re-use. Without users knowing and using them, Business Glossaries and Data Catalogs cannot deliver their full potential as expected.

 

Step 3 - Data innovation with R&D

To build data innovation, business, and data scientists will start from critical use cases and will perform Data Shopping to feed A.I. algorithms. They will then use Business Glossaries to search and select Business Data that could be used together – and will choose related actual data from the Data Catalog (with the right quality, and the right freshness). This starting point ensures that A.I. Is built on stable assets to secure A.I. stability and usability in the real world.

 

Step 4 - Business Value creation from real Data

Once data innovation is validated in the R&D Department, it can be made available to Business users by the IT department. This will be leveraging Data Governance knowledge and current data in real-life systems. In this final stage – thanks to the CDO and his Data Office's help on Data Governance for the innovation and R&D teams – Actual and actionable solutions to answer the initial Strategic goals and business uses cases identified in the first step.

Key steps to implement an effective data governance program.png

 

Benefits of data governance tools and software

Data governance software helps CDOs get the best out of the data and resolves those governance challenges:

  • Enable data community joint work

The first benefit is to create a shared environment between people who are building and validating Data Knowledge assets (Data Office for creating and Managing Data and Data Owners from the business side to validate Data Definition).

  • Accelerate data management, allow data innovation and performance

A data governance tool can provide several accelerators (data discovery, automatic Data Catalog, and Business Glossary creation) to ease Data Governance setup and management. As soon as catalogs and glossaries are available to business users and maintained, data knowledge can be re-used to allow data innovation with A.I. on wider data sets. Or it can be used to improve a company's operational performance.

 

With the GAFAMs' rise (Google, Amazon, Facebook, Apple, and Microsoft or startups from the new economy) and disrupting successes, leveraging on a fully implemented data culture: data is everywhere. Data can make a difference in the market and, to compete, companies need to manage and use data as a critical asset.

 

Strategic evolving role of Data Governance.png

 

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This is where Data Governance comes into play – You need to organize your company, build knowledge on your actual company data, and promote data re-use to deliver the value connected to your data potential.

 

 

Discover the essential topics you need to know to make your data governance program a success!

What is the definition of data governance?

Data governance is a top-level business initiative with specific goals:

  • define a data governance strategy based on the company strategy, business priorities, and critical uses cases and,
  • define and manage how to implement this strategy to deliver value.

Data Governance aims to organize roles between business users (Data users and Data owners), IT department, and support teams (Data Office). Their objective is to create a data practice community and build necessary data knowledge at a business language level – based on the actual data in systems (Data management). This asset must be easy to access and reusable for any Business or Data Scientist to help communicate and promote Data Governance initiatives, its progress, and successes in establishing Data Literacy and Data Culture in the company.

Strategic evolving role of Data Governance.png

 

Roles: who is responsible for the enterprise data governance success?

Data Governance success relies on joint teamwork animated by the CDO with Business data users, data owners, and IT stakeholders (IT Department, Enterprise Architecture and Business Processes managers, Data Protection Officers, R&D, and Innovation leaders).

In a company, the board of directors will define a vision, a strategy, and critical business objectives and use cases. The Chief Data Officer’s mission is then to help the organization leverage data as a strategic asset, and support and animate the data community. Within the organization, even though every company is unique, here are the four most common data governance roles:

Chief Data Officer (CDO)

The Chief Data Officer (CDO) leads the data governance program – with his Data Office Team, and so is responsible for its success. He usually directly reports to the CEO and interacts with the board of Directors as a critical stakeholder.

The CDO’s objective is to master data knowledge, set up compliance with regulations, and provide the ability for data reuse and data innovation such as A.I.

A tale of two CDOs The converging roles of Chief Data Officers and Chief Digital Officers.png

 

Data Officers

Data Owners

The data owner is responsible for the data. Typically, as part of the business team, he is the principal contact for specific functional data. He uses data and benefits from data sharing. His main objectives are to be a data domain expert and to create data reuse projects for his domain and the whole company. A data steward helps him.

Data Stewards

The data steward holds the knowledge of the data and its metadata. He helps organizations set up data governance projects and improve data literacy. His main objectives are to make meaningful data catalogs and business glossaries for data reuse and data innovation, but also to support data reuse projects.

They are the ones filling the gap between IT (Information Technology) and business, and by doing so, improve collaboration and the usage of data.

Partners

Data Architect

Data architects create and model the organization's databases, the datalakes. They can ease data governance by helping CDOs with conceptual models and business terms. Their main objectives are to provide knowledge of applications, business processes, and strategic needs. They provide expertise in concept modeling.

Data Protection Officer

The DPO focuses on regulations and compliance. He ensures that data is relevant and safely used. His main objectives are to ensure compliance with data quality and appropriate use of data in the company. All this to avoid fines from regulators.

Data users for innovation

Data Scientist

Data scientists use data to build disruptive new use cases to create a competitive advantage for a company. His main objectives are to use reliable sources and linked data to provide innovative ideas and go quickly into production.

Key steps to implement an effective data governance program.png

 

What is a data governance framework?

Data governance frameworks are best practice referentials and norms used by CDOs to set up and implement their data governance programs.

Several data governance frameworks exist on the market, but the Data Management Book of Knowledge (DMBok V2) is the reference. The DMBok framework is a set of best practices for Data Management created by the international DAMA association members (Data Management professionals) aiming to enhance data management collaboratively.

The DMBok defines a data management framework in 11 subdomains with best practices for each and data governance maturity level assessments. It defines a data governance certification (Certified Data Management Professional) and is also linked to DMBok knowledge proficiency.

 

What are the strategic accelerators for building a Business Data Governance?

Data governance tools can accelerate data governance when leveraging on synergies with other strategic initiatives.Connected with enterprise architecture features, it can provide IT portfolio and solution architecture knowledge that is needed for data governance. Moreover, knowing IT solutions changes in advance helps to handle data governance maintenance and impact analysis in the long term.

Data governance tools connected with process modelling give a business view on data life cycles: where the data is created or entered, how it is used in which context? Linking process and data governance allows the company to be more customer and performance-oriented, and to adapt to change quicker.

Data Governance must deal with data compliance and quality. When linking with the wider Governance Risk and Controls initiatives – it helps to better assess risks and impacts, data importance, and classification.

 

Why does data governance matter?

Data Governance addresses many critical company topics:

Make decisions based on reliable data, and aligned with the company's objectives.

A data governance program helps implement business strategies based on data. The company defines a strategy with different objectives and in particular business use cases that materialize needs. It could be about enhancing customer service levels and creating new disruptive customer offers. It could be working on operational or financial performance. These use cases must be defined and prioritized with boards and with different business directions.

 

Comply with regulations and therefore avoid heavy fines and ensure Data Security

Mastering data knowledge and usage is usually one of the most common regulatory requirements companies have to face (BCBS 239, Basel III, Solvency II, GDPR, CCPA, …). These rules ensure the proper, fair, and ethical use of data for all contributors. Furthermore – Data Governance deep knowledge (where the data is stored, who is using it?... ) provides valuable information to enforce Data Security initiatives.

 

Allow Innovation based on Data

The higher operational performance or data innovation cannot be done without accurate and efficient data management. For example, Artificial Intelligence innovations cannot be built without good quality data: Predictions about customer behavior in this case aren’t stable enough to be used in real-life conditions. Therefore, the data governance program's role is to organize works and use case implementation. To be used or re-used, data must be known, and data quality must be ensured or enhanced. Data must be compliant with external regulations or internal policies.

 

Implementing a data governance program

A data governance program is a strategic initiative that follows a continuously improving cycle. Four main steps are needed to implement a data governance program:

 

Step 1 – Definition of the data governance strategy

A Data Governance program is a virtuous cycle starting from companies’ business needs. Once the strategy is defined at the board level, business needs and use cases are associated with data needs by the CDO. Business needs will help the CDO define the data governance strategy.

Key steps to implement an effective data governance program.png

 

Step 2 - Data governance assets knowledge management

Create a new organization with internal partners and users

The Chief Data Officer or Data governance manager is here to create a new organization to support Data Management and Data usage. The Data office is usually composed of data experts like Data Stewards, Data Custodians, or Data Architects. They are here to first create Data Catalogs and Business glossaries to allow data reuse. Besides, their aim is also to support business data users and implement projects. They typically work with Data Owners that are on the business side.

Focus first on the data needed to implement critical use cases

For each selected business use case, Data Stewards must define which data is needed. For example, have financial processes and data been compliant? Have customer offers been improved (based on customer data)? Depending on Uses Cases priorities, data governance planning can be defined to create Data Catalog and Business glossary in a step-by-step and interactive way.

Build Data asset knowledge and communicate progress with the community

Communicating data is an important duty for CDOs to set up a data culture (Data Literacy). Data work progress, data quality, and use case successes based on data must be promoted to motivate Data Re-use. Without users knowing and using them, Business Glossaries and Data Catalogs cannot deliver their full potential as expected.

 

Step 3 - Data innovation with R&D

To build data innovation, business, and data scientists will start from critical use cases and will perform Data Shopping to feed A.I. algorithms. They will then use Business Glossaries to search and select Business Data that could be used together – and will choose related actual data from the Data Catalog (with the right quality, and the right freshness). This starting point ensures that A.I. Is built on stable assets to secure A.I. stability and usability in the real world.

 

Step 4 - Business Value creation from real Data

Once data innovation is validated in the R&D Department, it can be made available to Business users by the IT department. This will be leveraging Data Governance knowledge and current data in real-life systems. In this final stage – thanks to the CDO and his Data Office's help on Data Governance for the innovation and R&D teams – Actual and actionable solutions to answer the initial Strategic goals and business uses cases identified in the first step.

Key steps to implement an effective data governance program.png

 

Benefits of data governance tools and software

Data governance software helps CDOs get the best out of the data and resolves those governance challenges:

  • Enable data community joint work

The first benefit is to create a shared environment between people who are building and validating Data Knowledge assets (Data Office for creating and Managing Data and Data Owners from the business side to validate Data Definition).

  • Accelerate data management, allow data innovation and performance

A data governance tool can provide several accelerators (data discovery, automatic Data Catalog, and Business Glossary creation) to ease Data Governance setup and management. As soon as catalogs and glossaries are available to business users and maintained, data knowledge can be re-used to allow data innovation with A.I. on wider data sets. Or it can be used to improve a company's operational performance.

 

With the GAFAMs' rise (Google, Amazon, Facebook, Apple, and Microsoft or startups from the new economy) and disrupting successes, leveraging on a fully implemented data culture: data is everywhere. Data can make a difference in the market and, to compete, companies need to manage and use data as a critical asset.

 

Strategic evolving role of Data Governance.png