Data Governance: The Foundation

Shot of Corridor in Working Data Center Full of Rack Servers and Supercomputers with High Internet Visualization Projection.

Data is one of the most critical and highly valuable corporate assets that any organization holds. Humans are only increasing the amount of data produced while the ability to consume that data in a structured, meaningful way is lagging.  It’s been shown that each person creates roughly 1.7 MB of data per second, with most of that data being unstructured. Today 95% of companies cite the need to manage unstructured data as a problem in their business—poor data quality comes at a cost.

Though the collection and utilization of data has only grown over time, only in the last few years have organizations recognized the need for better data quality. With the explosion of cloud-based platforms and other technologies that provide the ability for non-technical resources to produce and consume data,  an organizations’ need to better understand and trust their data has been brought to the forefront. While data governance tends to not be the most exciting aspect of business, it’s a critical component to ensure your business is making the best data-driven decisions it can from highly trusted data.

According to research by McKinsey, “leading firms have eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars.” Data governance is the difference between leading firms and the laggards.

What is Data Governance?

As with anything in technology, Data Governance is a term with an evolving definition. The Data Management Association defines data governance as “planning, oversight, and control over the management of data and the use of data-related sources.” It’s NOT, nor should it be, a collection of ad-hoc, tactically-focused data correction projects. Data Governance policies, processes, and procedures need to be embedded into the daily functions of an organization, which in turn improves the quality and accuracy of their data without the end-users feeling like it’s another step in a process. The purpose of data governance is to identify what data and information is important, establish the processes to manage it, and measure the effectiveness of the effort in achieving business objectives.

Have you ever run a report and noticed random spikes, zero values, or even the dreaded “NULL” value? Herein lies the value of Data Governance—it ensures that upstream processes are accurately and properly processed so downstream users can correctly understand, report, and make better, more informed business decisions.

Data Governance is achieved through the establishment of a focused team and team of teams, composed of technology and business stakeholders responsible for building and delivering an organizational culture that sees data as an asset. This team oversees data by documenting policies and controlling how pieces of data are captured, defined, stored, and distributed across the enterprise.

There are a couple of foundational components that help Data Governance programs achieve success:

Executive Sponsorship

Woman in corporate setting

The need for Data Governance starts from either a bottom-up or top-down communication and should be an integrated part of an organization’s culture and discipline. Organizations strive to be more innovative and adaptable to economic realities, and with this change, the need to embed data governance into their overall strategies is critical to minimize disruption during change. As business needs change, so does data and therefore, it’s critical that executive leadership understand and project the importance of being data-driven. Embedding governance processes into daily routine will help improve the overall quality and accuracy of reporting, which in turn leads to more innovative insights and decision-making capabilities.

Downstream consumers of data, such as your data analysts and business stakeholders, are the key benefactors to these data governance initiatives. Their daily work efforts will only succeed if the executive leadership team is directly involved and supportive. Unfortunately, organizations often wait to address data governance issues until errors are discovered, or when there is a need to adhere to regulatory, compliance, or M&A situations. These data governance initiatives are then reduced to one-off data integration or correction initiatives that are assigned to an IT group, and they fail to understand the true organizational management needed to continue defining, delivering, and maintaining data.

Embedding governance processes into daily routine will help improve the overall quality and accuracy of reporting, which in turn leads to more innovative insights and decision-making capabilities.

To motivate executive understanding and involvement in building a data-driven culture, it’s necessary to start with a business case. Leadership must see the strategic value and benefit of doing business through data-driven initiatives. For example: how does improved data quality provide more accurate and reliable sales information? More defined customer data to better serve marketing outreach and campaign management? And so much more. The fundamental value proposition of data governance is to provide the operational foundations and processes that can directly correlate to improved business decision-making through trustworthy and compliant data, increased data visibility, and greater analytical insights. These types of decisions can help an organization increase sales, stay competitive in the market, and help drive lower overall costs.

When leadership is on board with the value of data governance, they play a key role in the design and implementation of a  strategy, vision, and roadmap. They should understand that data is a journey and will change and adapt over time, as should the policies and procedures. It needs to be a living, breathing entity where flexibility and change are embraced, and will help support the growth and transformation of their organization through a series of initiatives, not trying to “boil the ocean” all upfront.

Policies and Standards

Compliance, regulations, and standards

Defining data governance standards, policies, and procedures should be a highly collaborative and team-based approach across the organization. However, it is the responsibility of the data governance team to ensure their successful execution and adherence.

From a data perspective, an essential piece of policy is the definition, allowed values, and restrictions of data elements. For example, in the United States Postal Service (USPS), a key data element is simply the postal zip code of a customer. This piece of data can be named, defined, and inputted in multiple ways so an authoritative standard must be set to maintain consistency. Some things to think about: zip codes are sometimes labeled postal codes; do you only use the five numeric values or do you include the hyphen? How many characters do you allow? What about domestic versus international? Without definition and consistency, data cannot be integrated throughout the organization, which can result in errors, confusion, an organization sending mail to an undeliverable address, and wasted time and money. Ideally, each data element or grouping of data elements is assigned a data steward who documents the standards and ensures quality. In most cases, the recommended data stewards are the business stakeholders directly, or those most closely aligned to that type of data.

Data stewards examine how data enters the system through all the possible entry points. For example, data might originate through a point-of-sale application, a website, an iOS or Android application, a mailed-in form, or a call center. A full data workflow must be created, mapping all the channels and entry points where errors or inconsistencies might occur. Any transformations the data goes through also need to be documented and distributed to all consumers of the data with agreement and communication as to its definition.

Data can be defined and captured in numerous ways, with different choices benefiting different lines of business. Each standard and policy produced will result in a tradeoff between the overall corporate needs and the individual line of business needs. It’s critical that data stewards across all lines of business meet regularly, have healthy debates, and strike a good balance in the structure and content of every policy and standard.

The Importance of Data Governance Programs

We live in a data-driven economy and the amount of data being produced is far outpacing the amount being consumed. Organizations will continue to collect and store massive amounts of data regardless of whether that data is effectively managed or not. It’s essential for businesses to understand that data governance initiatives are not done for the sake of data but for the benefit of the business to be a mechanism. If done properly, data governance initiatives can increase efficiencies and reduce overall costs.

At AIM Consulting, our robust data governance offerings provide our clients with everything from strategy to execution, providing high-performing teams to help organizations at any juncture in their data governance journey. Our focus is to optimize the management of enterprise data throughout the entire data lifecycle and affect culture and organizational change that leads to more reliable business decisions and enhanced productivity and efficiency.

It’s essential for businesses to understand that data governance initiatives are not done for the sake of data but for the benefit of the business to be a mechanism.

Building a high-quality, data-driven culture requires a change in not only the process but the overall culture that prioritizes consistency and accountability through data governance. The end result should be highly available, trustworthy data that allows consumers across the organization to make more informed, accurate, and data-driven decisions that yield higher results.

There’s so much more to data governance than just the basics and we’re so excited to continue this series. Stay tuned to learn more about the different roles within data teams, types of data, measurements, cadence, and more!


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