Data governance framework

Framework of Data Governance

By Shilpi Agarwal, Posted August 30, 2019 In Data Governance

If there’s anything that’s defining thriving businesses today, it’s a strong understanding and strategizing the use of a company’s data.

However, it brings up a whole range of questions, from both users and stakeholders- What data exists in my company? Where is it stored? What is the best data for my problem? When you have figured that out, more questions arise- How do I access it? Can I trust it?

Providing and controlling data access, ensuring data quality and data protection – all come under data governance.

A data catalog takes care of finding and understanding your data part effectively. Now it is also combining the capabilities of a data governance toolset. The merger of data cataloging and data governance is very opportune. That is because their functions are so intertwined.

Here, I will discuss the framework of effective data governance. First, I will layout the functions of data governance and then the features required to support those functions.

Read here about what is data governance and why it matters.

Functions in a Data Governance Framework

Amongst all the functions which are performed under data governance – policy making, change management, aligning data strategy with business strategy, principles and ethics are carried out by the company leadership and management.

data governance tasks for company leadership

Policy Making 

For an organization to govern a variety of data, it needs to have specific policies in the following area.

  • Data transfer policy, (e.g. PII information can’t be shared until encrypted)
  • Naming convention policy
  • Access control policy
  • Data quality policy

Change Management

A traditional company can move towards self-service, or in a reverse scenario, a fast-moving start-up can start having more controls. In both cases, we need a change management program. Data Governance team should be equipped to provide various training to adapt to these changes.

Aligning Data Strategy with Business Strategy

A company has to align overall data strategy with the business strategy of the company. Only then the data governance programs are successful.

Principle & Ethics

Ethical data handling can increase the trustworthiness of an organization and the organization’s data and process outcomes. Like W. Edward Deming’s statement on quality, ethics means “doing it right when no one is looking.”

data governance tasks carried out through tool

Data Classification

Data classification helps the teams find, organize, and secure relevant data. We can classify data as per various categories:

  • Functional categories- Customers, Suppliers, Items, Inventory
  • Security categories- PII, Sensitive, Protected and more

Building Business Glossary

A business glossary helps to solving communication problems by creating a common vocabulary across the entire organization. It additionally ensures the consistency of these terms by synthesizing all of the information of the organization’s data assets through an array of data dictionaries. It then rearranges it into a more understandable and straightforward format.

To create a useful business glossary, organizations should implement a data governance solution that can connect data quality, data lineage, and data definitions.

Data Quality Monitoring and Control

Data quality must be tracked, managed, and monitored if that data is to drive better business decisions. Therefore, being able to measure and monitor data quality throughout the lifecycle and compare the results over time is an essential ingredient in the proactive management of ongoing data quality improvement and data governance. 

Data Access Request and Control

Managing Roles and Responsibilities

Roles and responsibilities are a crucial part of data governance, which comes down to identifying and managing the roles of owners and stewards of data.

Responsibilities of a Data Steward:

A data steward is a role to ensure the fitness of data elements – both the content and metadata. The tasks they do:

  • Make sure the data is classified correctly
  • Ensure that authors describe data accurately, so others can easily find and understand it
  • Ensuring the quality of the data

Responsibilities of a Data Owner:

A data owner is an individual accountable for a data asset. The tasks they do:

  • Ensuring the budget for the data cleanup and rationalization
  • Controlling data access through workflow

Data Maturity Assessment

  • Level 1

    Initial Adhoc

    • Little or no governance
    • Limited tool set
    • Roles defined within silos
    • Controls applied inconsistently, if at all
    • Data quality issues not addressed
  • Level 2

    Repeatable

    • Emerging governance
    • Introduction of a consistent tool set
    • Some roles and processes defined
    • Growing awareness of impact of data quality issues
  • Level 3

    Defined

    • Data viewed as an organizational enabler
    • Scalable processes and tools; reduction in manual processes Process outcomes, including data quality, are more predictable
  • Level 4

    Managed

    • Centralized planning and governance
    • Management of risks related to data
    • Data Management performance metrics
    • Measurable improvements in data quality
  • Level 5

    Optimized

    • Highly predictable processes
    • Reduced risk
    • Well understood metrics to manage data quality and process quality

Source: DAMA International

Data Valuation

Since each organization’s data is unique, a plan to data valuation needs to begin. That includes articulating general cost and benefit categories that can be applied consistently within an organization.

Features of a Data Governance Toolset

Policy Implementation Module

A module that ensures that the data policies are implemented.

Role Assignment Module

A module through which roles like those of data steward, data owner, and data custodian can be assigned.

Automated Data Lineage

Data Lineage is a visual representation of where the data is coming from, where it moves and what transformations it undergoes over time. It provides the ability to track, manage, and view the data transformation along its path from source to destination. This a key feature in maintaining data quality.

Business Glossary

Business glossary is a document which enables data stewards to build and manage a common business vocabulary. This vocabulary can be linked to the underlying technical metadata to provide a direct association between business terms and objects.

Workflows for Permissions

A workflow by which users can request for data and data owners can grant access for a specific time period.

Understanding the framework of data governance is the vital first step in data governance.

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