01/10/19

Four Elements to a Successful Data Governance Journey

By: Eric Truntz, Senior Principal

Without a doubt, one of the most consistent IT trends across the insurance and financial services industries is the increasing deployment of and dependency on data governance. The momentum behind this trend can be attributed to several influences. A few of the most compelling include business strategies founded on the premise of the elusive “single source of truth”, ever-increasing compliance requirements and the accountability of knowing where all business data comes from and what happens to that data at every step through a business process. Considering the depth of these influences, it’s easy to see that implementing successful data governance is not merely a project with a definitive start and end; it is an operational capability and requires an effort geared towards the operating culture of an organization. If your company has decided to start a data governance program or is considering one, there are four key elements which will help you design success: sponsorship and engagement of key stakeholders, establishing purpose, metrics, and timelines, selecting and implementing appropriate tools, and empowered data discovery. Include these elements in your project planning to improve data quality, ensure data alignment, and further leverage your data to boost your bottom line.

1. Sponsorship and Engagement of Key Stakeholders

Ensure key members of your organization, from both the business and the IT sides of the house, are involved and have bought into the effort to achieve success. Enterprise-wide data governance is founded on the notion of collaboration, meaning that data used across business units and IT groups must be linked together as a whole. Including both sides of the house will make for better data, but it will also facilitate a general understanding of business issues by IT and IT issues by the business. Feedback and concerns from IT and business units should then be combined in a tool with metrics, rules, and procedures to identify, remediate and prevent business rule failures, data duplication, and other organizational issues that impact the bottom line.

2. Establish Program Purpose, Metrics, and Timeline

The tactics to implement data governance and to determine the scope of a data governance program can vary widely – from reducing compliance risk to improving data quality. No matter your organization’s focus, establishing clear goals is crucial to the success of the program. Today, most stakeholders have an understanding that data is an asset and a company-wide responsibility. What is not as widespread a belief is that data governance should not be just an IT initiative or just a business initiative; without well governed, high-quality data, data-driven business decisions become challenging to make. Set specific goals for the program that align and grow with organizational strategy and define intermediate metrics for success so that you can track progress and course-correct as needed. Allocate adequate time to implement, test, and monitor the solution as well. Lastly, select a place where data governance and overall data management should live in your organization. Traditionally placed in the hands of the IT organization, data is really a business asset. Strong program management in the form of purpose, metrics, and a timeline will set you up for success.

3. Selecting and Implementing Appropriate Tools

Not all data governance tools are created equal and not every tool is appropriate for every job. The underlying philosophy of data governance is to strengthen business goals with enterprise-wide data, thereby unlocking additional utility of that data. Whichever tool you select, make sure it supports this collaborative nature without becoming overly burdensome for your organization to operate. Some of the key functionality a data governance tool should include:

• The ability to capture overall business goals and enforce business and technical rules surrounding the creation, use, and destruction of data assets.

• Techniques for monitoring data within the systems of the organization, including occurrences where data fails to comply with business rules.

• The creation of documentation by the repository as you populate it, such as data flow, data impact and lineage, and data dictionaries. This is crucial as it will save you time down the road.

• Built-in metrics to track things like data quality, quantity, and timeliness.

• The ability to be implemented and operated at the appropriate level for your organization’s needs at each point in your data governance journey.

4. Empowered Data Discovery

Over the years, there have been several pushes for companies around data; to think of data as an asset, encourage business involvement in data projects and use data for decision making. Of course, these are all great operating principles, but what is data an asset for? Companies must first determine what they are looking to implement through their data governance projects. Data governance should go well beyond determining common definitions of data elements, ensuring data standards, and reusing key references or master data. Internally, you must identify additional information about your data to understand what your governance strategy is achieving. This must include:

• Data lineage: where did it come from and how did it get here?

• Data linkage: what is the relevance of the most important data and what contextual meaning can you derive from it?

• Usage and data context: who is using the data, how are they using it, and what different contexts can the data be used in?

This is not a comprehensive list, as there is limited space in a blog post. However, if you are interested in other key information to collect during data discovery, shoot us a note here.

It is important to remind stakeholders not to get in the way of data discovery. In-depth data knowledge is gained through direction and clarity, but discovery phases will be unsuccessful if there is a suffocated data flow or experimentation with constrictive processes. Consider data governance as an enablement and empowerment core capability and you will find greater success in its implementation.

Kicking off a successful data governance program requires knowledge of existing data, cross-sector program management, stakeholder engagement, and the right tools. Most importantly, make sure that accountability and ownership are bestowed to the correct organization at your company. Data governance is not always best owned by the technology group.

Keeping these four elements in mind can help you plan and execute your program. Have some thoughts you would like to share? We’d love to hear from you!

 

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