Data Governance at the Speed of Business,

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These three steps will help you create and mobilize a program for data governance that can drive enterprise-wide transformation.

Data can be accessed from many sources, including IoT devices and wearables, SaaS apps, social media channels, and SaaS applications. Data from all these channels can be combined and analyzed thoughtfully to unlock new insights and opportunities. Organizations that can institutionalize and scale these insights throughout the enterprise will be able to make better decisions and not have to learn twice.

Data governance is essential for converting siloed data into enterprise-wide insights. It requires more than just a passing commitment. Data governance, at its best, can scale with a company’s strategy, adapt to growing data troves and provide a common terminology that trusts and facilitates communication between business units.

Data governance is what fuses both speed and data. It is the system, policies, procedures, and systems that an organization uses to ensure that teams have the correct data at the right moment to improve and automate products and processes. This is a valuable and exciting function in today’s competitive environment, but it takes significant effort. This article outlines a three-step process for developing and mobilizing a business-friendly data governance program.

Step 1: Establish foundational components

Data governance in many organizations is limited to security, compliance, and privacy. These are binding domains, without a doubt. However, expanding the scope of the oversight and diversifying the members can bring more business value to the organization through quicker, more informed decisions and greater operational efficiency. A data governance program must include four components: a committee to oversee data governance, data owners, data managers, and data stewards.

Take stock of your data governance steering group. If you don’t already have one, create one. If your existing one is not cross-functional, add to it. Each business unit should have a representative. Depending on the committee’s size, it could be a senior executive or someone who is closely involved with the BU’s core IT systems. All business units and functions should have leaders.

First, representatives must articulate the objectives of the committee. This should include both compliance-driven and business-driven objectives. These objectives will help define the data governance goals that the steering committee is most qualified to achieve. Consider a healthcare organization that manages administrative functions for large hospital systems. The purpose of the steering committee was to increase automation in reporting processes. They determined that they needed to establish standard data definitions among their clients to achieve this objective.

After establishing a steering committee and setting its goals, it’s time to assign roles. Each BU or function should have a data owner. This will help establish and maintain the policies and procedures that will reduce data quality issues. Keep in mind the example of healthcare. Each business unit had a different definition of claim denials, which prevented the organization from adopting solutions to automate claims reporting. The steering committee recognized a standard definition for claim-denials to allow data aggregation and automated reporting. The steering committee assigned data owners to align data within their respective business units and functions to this standard definition.

Next, you will need to assign data stewards. The stewards are both tactically and functionally aligned. They assist data owners by ensuring policy compliance, leading domain-specific changes management, and reporting data quality issues. A steward might work with the marketing department of a B2B software firm to encourage the use of common terminology in company CRM tools, such as North, South, and East. This would include teaching the practice to sales reps who use it, monitoring its adoption, and suggesting ways to improve the policies.

It is also essential to create a data management group. This team is typically made up of technical IT personnel and serves as the backbone for your data governance initiative. It monitors and supports established procedures and policies. It conducts audits to verify compliance with privacy and security policies, evaluates data for accuracy and completeness, and guides your data lifecycle strategy from the initial creation of your data through its destruction and expiration.

Step 2: Develop the skills you need to quickly and accurately introduce new data into your ecosystem

After the initial policies have been established and the organization’s structure has been created, it is possible to begin building the skills to make data governance an agile resource that can help you spot opportunities and anticipate problems.

Data governance is responsible for identifying and classifying new data sources. These may result from mergers or the introduction of new technologies within your company. This is done by creating and applying a consistent set of policies, processes, and supporting tools. You can think of it as a gated process. It’s a series of checks that new data must pass to ensure its quality.

The first step is to identify what must be done to bring the new data together. Each organization had its way of managing customer-entity hierarchy relationships. These relationships define relationships between customers and roll up to the same parent company. One example is our B2B software client that purchased a competitor company and wanted to consolidate its customer data. To protect Wall Street metrics, the steering committee decided that the acquired company should inherit acquiring company’s customer-entity hierarchy. The following levers were required to achieve this:

Data Modeling & Design: Map the customer hierarchy of the acquired company to the existing order, and update data modeling artifacts (e.g., entity-relationship diagrams and tools) accordingly.


Data Dictionary: Update the master data management tool and data dictionary with historical context. This will indicate how customer data was mapped from the acquired company to the incumbent customer hierarchy.

Data Compliance and Access: Evaluate the current compliance status to determine if it suits the new customer information and decides whether additional access or security provisions should be implemented.

Data Quality Design and Implementation: Create controls in critical applications to stop salespeople from creating duplicate records or entering text without searching.

Communication and Change Management: Data Stewards are responsible for communicating the changes to affected users and managing any subsequent changes to people and processes.

It is challenging to manage the introduction of new data. However, resist the temptation of pursuing one-off solutions that are quick but not long-term scale or reusability. A thorough analysis and implementation can result in dramatic and scalable long-term gains. You can invest in this process and reap its benefits—the J-curve in the beginning.

Step 3: Formalize operational data management practices for continuous data quality

The final step is to codify data management practices and tools that support your business goals and preserve data quality. The best data management programs can help the following:

Master Data Management: Systems and processes that allow for the creation of one master reference source for all business data (e.g., customer, product) and which in turn reduce errors and redundancies in business operations

Data Quality Auditing and Monitoring: The use of tools and automated processes to identify data that does not conform to business or compliance rules.

Data-Quality reporting: This is the practice of creating data quality metrics, or KPIs, reviewing their progress regularly, and then deciding on actions to improve them.

Data Storage Operations: This is the practice of defining where and what data should be stored throughout the data lifecycle, from creation to destruction. It also accounts for compliance and privacy considerations.

Data Stewardship is the practice of allocating resources across crucial business units or functions to support data quality policies. It also involves managing changes that may result from introducing new information into the environment.

These practices can be codified to improve data quality, including accuracy, completeness, and timeliness. Validity, validity, uniqueness, and consistency are all key factors. A high-quality customer can make the difference between a satisfied and disgruntled customer. One healthcare client found that investing in monitoring and controls technologies has helped to ensure high-quality data for information in motion and rest. This allows them to provide customers with real-time data and consistent experiences across digital and physical channels.

Great data governance programs leverage the company’s data to drive enterprise-wide transformation. A solid data governance program can help improve the performance of specific business units and functions. You will enjoy more incredible speed, agility, and ultimately better business results. Make it a great one.

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