Until the early 2000s, most firms accumulated data at a manageable rate and were able to collect, store and use that information with little additional effort. However, over the past two decades, the introduction of automation mechanisms (i.e., robotics, IoT, etc.), social media, and cloud storage has made it increasingly cheap and easy to collect and store data.

Today, organizations are accumulating an unsurmountable amount of information. Innovative academics have identified a plethora of ways to leverage data, and tech giants have developed (virtually) limitless computing power to process it. The world is now exponentially accumulating information so fast and vast, that we’re observing instances where laws are struggling to protect it and companies are frantically trying to leverage it.

As one would expect, with an increase in the amount of available data comes an increase in usage across organizations. From executives and employees, to customers and regulators, it’s become a vital component of interaction across a myriad of stakeholders, making it extremely critical for competing in today’s emerging data-driven economy.

Data as a Strategic Asset

This increase in data usage has led us to a world where the quality of data matters. When the data that is stored has inconsistencies in form, has missing components, or is not up to date it can have a significant impact on an organization.

A colleague shared with me an example of this quality issue occurring during her previous job at a global bank. When generating monthly reports that showed details about funds (i.e., returns, exposure by geographic region, product type, currency, etc.), she found herself spending a ton of time exporting data from systems, making manual adjustments so that it was in the correct form, realizing it didn’t look right, going back to the numbers to figure out what was wrong, and so on.

In such situations, poor data quality can turn out to be costly because analysts and managers often find themselves spending more time preparing data than analyzing it. Unlike a decade or two ago, today, poor data quality has a direct and greater financial impact. Governing and managing it inadequately is often indicated by subpar operational performance (i.e., bad decisions due to incorrect reporting), reputation loss (i.e., data leaks), or worse (i.e., regulatory fines due to non-compliance with privacy laws).

Data Governance and Data Management Lay the Foundation

To thrive in today’s data economy, the CDO/CIO office is often pressured to be intentional about continuously improving data quality across the organization. To do this, they need to rely on both Data Governance and Data Management mechanisms. While the concepts of Data Governance and Data Management are commonly understood and documented, one of the major differences between the two is that Data Governance is a strategy (i.e., macro) and Data Management is a practice (i.e., micro). But both are necessary for any organization to thrive.

Organizations that formalize Data Governance have roles and responsibilities defined for data ownership and stewardship. They also have policies, procedures and enforcement in place to ensure that data quality standards are upheld from data entry to data delivery. On the other hand, Data Management is prevalent in organizations that have tools and technologies that serve various purposes, such as visibility into metadata (i.e., what data is in which table, measure calculations, data lineage, etc.), access controls, etc.

Examples of common problems solved by Data Governance and Data Management:

Both Data Governance and Data Management are complementary in nature and essential for an organization to run optimally in today’s world. In the ideal state, a combination of these two would allow organizations to understand the positive effects data can have on their business and offer the ability to create that impact with little effort. Such an ideal further enables organizations to connect the right insights with the right people for driving value all the way from optimizing business processes to propelling innovation.

Driving Value from Data Governance and Data Management

Structuring and initiating Data Governance and Data Management implementations can appear daunting and intimidating. Common trends suggest organizations often struggle to launch and sustain a Data Governance program, or lack buy-in on basic Data Management tools because it seems to be an expensive proposition.

Kenway’s approach to data is different. By understanding and prioritizing based on use cases supported by business objectives, organizations are enabled to structure and pace the development of their data infrastructure in such a way that the project could potentially pay for itself by adding immediate ROI to business initiatives.

We’d love to learn more about your organization’s Data Governance and Data Management initiatives. Drop us a note at [email protected], or check out our Information Insight page to learn more.

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