Data Governance and Protection

Data Governance is:

..the overall process which brings together organizational resources to manage critical data assets to ensure a level of trust and quality aligned with overall business strategy"

A. Drivers for Data Governance:

There are many drivers for establishing a robust Data Governance practice in your organization. Following are some of the many reasons why the importance of Data Governance is growing steadily (figure 1):

1. organizational data remains siloed and fragmented ,

2. organizational data is moving from on-site to cloud platforms,

3. organizations are modernizing legacy data infrastructures,

4. APIs are becoming commonplace to exchange data with private, partner and public consumers,

5. growing incidence of data breaches with severe business impact,

6. regulators are mandating effective data governance, and

7. connected devices are becoming commonplace.

figure 1: drivers for data governance

B. Business Outcomes related to Data Governance

1. Facilitate data-driven and fact-based decision making to improve the bottomline

2. Enhance trust in data by improving data quality across all data quality dimensions.

3. Establish controls to secure data in line with the confidentiality level of data

4. Eliminate or reduce data breaches

5. Enhance data understanding across the organization

6. Comply with regulations

The above outcomes should be defined in an a SMART way (specific, measurable, attainable, relevant and time-bound). KPIs should be linked to outcomes.

C. Resources to enhance Data Governance

In response to these disruptions organizations are enhancing their data governance practices despite challenges along the way. To develop a successful data governance program an organization must:

1. Identify disruptions (figure 1) and define clear business outcomes

2. Establish clear roles and responsibilities in Data Governance domain e.g. Data Custodian, Data Steward, Business or Data Analyst, Data Engineer, Data Scientist, Data Administrator, Data Protection Officer (figure 2)

3. Establish Metadata Management functions (see section C below)

4. Define data governance principles and dimensions of data quality and establish a mechanism to rate data quality of key data entities (figure 3)

5. Assess the impact of regulations such as EU GDPR or California Consumer Privacy Act.

6. Catalog and Categorize data into different levels of confidentiality such as public, internal, confidential and restricted (figure 4)

figure 2: data governance roles

figure 3: data governance principles and data quality dimensions

figure 4: data classification

The organizational resources to implement data governance include people, process and technologies such as roles, responsibilities, processes, functions, policies and applications.

D. Metadata Management

Metadata management is an important capability in Data Governance. It involves managing metadata ("data about data") about organizational data. Metadata includes definition of business terms, their relationship with each other, conceptual and logical data models, rules governing the data and related information such as origin, flow and impact of data. The following diagram describes the metadata capabilities

Data flow: organizational data flows through a series of steps beginning with sources, ingestion into storage, processing, analysis and serving the data to consumers

Along the way the data flows are supported by metadata capabilities which are described below:

1. Metadata Glossary: definition of business terms and their relationship

2. Metadata Semantics Management: conceptual and logical data models, mapping, design patterns

3. Metadata Ingestion and Translation

4. Metadata Repository

5. Rules Management

6. Managing Impact

7. Managing Lineage

D. Data Quality Management

E. Building Data Governance Theme:

The above information should be used to define the Data Governance theme for your organizations. Following information may be captured to develop Theme Dashboard:

• Business: disruptions, outcomes, KPIs, risks, strategy actions, stakeholders, roles. concerns, functions and processes

• IT: Data Entities, Applications, Stack

• Finance: IT Towers, IT Cost Pools and TCO analysis