Data Governance

- Why Data Governance Matters

Digital transformation relies on timely and trustworthy data. Data governance policies often protect data but slow down or even prevent access to it. This means slower time-to-market, missed opportunities, and the rise of shadow IT. That's why a data governance strategy that allows for trusted data to be delivered at real-time speeds is essential for anyone who wants to lead their organization into the future. 

Without effective data governance, data inconsistencies in different systems in an organization may go unresolved. For example, customer names may be listed differently in sales, logistics, and customer service systems. This can complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence (BI), enterprise reporting, and analytics applications. Additionally, data errors may not be identified and fixed, further affecting the accuracy of BI and analytics. 

Poor data governance can also hinder regulatory compliance programs. This can create problems for companies that need to comply with a growing number of data privacy and protection laws, such as the EU's GDPR and the California Consumer Privacy Act (CCPA). Enterprise data governance initiatives typically include developing common data definitions and standard data formats for all business systems, thereby improving data consistency for business and compliance purposes.

- Data Governance Policies and Stewards

According to the DGI (Data Governance Institute), a data governance framework is "a logical structure for classifying, organizing, and communicating the complex activities involved in making decisions and taking action on an enterprise's data."

A data governance policy is a document that formally outlines how an organization's data is to be managed and controlled. Some common areas covered by data governance policies are:

• Data quality - ensuring that data is correct, consistent and free from "noise" that could hinder usage and analysis.
• Data Availability - Ensuring that data is available and readily available to the business functions that require it.
• Data Usability - Ensuring that data is clearly structured, documented and labeled for easy search and retrieval, and is compatible with the tools used by business users.
• Data Integrity - Ensuring that data maintains its essential quality even when stored, transformed, transmitted and viewed on different platforms.
• Data Security - Ensuring that data is classified according to its sensitivity and define processes to protect information and prevent data loss and leakage.

Solving all of these issues requires the right mix of people skills, internal processes and the right technology. 

Data stewards are organizational roles responsible for developing data governance policies. Data stewards are typically subject matter experts familiar with the data used by a particular business function or department. They ensure applicability of data elements (content and metadata), manage data and ensure regulatory compliance.

- Objectives of Data and Information Governance

Organizations are embracing digital transformation to change the way they serve customers, analyze the world around them, and manage global risk and operations. The foundation of digital transformation promises is data. It powers new business models, drives customer experience, and accelerates business intelligence. 

Governance encompasses all data assets. Everything from dashboards and code to data science models are data assets. A data governance framework should consider all data assets, i.e. data and analytics governance. 

Data and Information Governance helps organizations achieve the following goals: 

• Compliant with SOX, Basel I/II, HIPAA, GDPR and more
• Maximize the value of data and enable its reuse
• Improve data-driven decision-making
• Reduce data management costs

- Data Governance Policy

A data governance policy informs the content of an organization's data governance framework. It requires you to define for each set of organizational data: 

• Location: The location of physical storage
• Who: who has or should have access to it
• Content: Definitions of significant entities such as "customer", "supplier", "transaction"
• How: what is the current structure of the data
• Quality: current and desired quality of source data and consumable datasets
• Goal: what do we want to do with this data
• Requirements: What needs to happen to the data to reach the goal

[More to come ...]