Over the last five years, more data has been generated than ever before. Data has become the fuel of the 21st century. Enterprises are faced with the enormous challenge of understanding what all of their data means and how it is connected. With increasing compliance and security requirements, enterprises face additional pressure to protect, understand, and validate their data. Clients and regulatory bodies expect these firms to be responsible custodians of their data. Therefore, establishing an effective data governance model has never been more valuable or necessary for an enterprise than it is today.
Data governance is an essential piece of any enterprise data management strategy. With various explanations available (try searching for “data governance”), let’s establish one clear definition. Data governance is a set of practices, policies, and capabilities that enable an enterprise to ensure that high data quality exists throughout the complete lifecycle of data.
Data governance lies at the root of digital transformation, directing the way businesses view, store, and treat their data. To take advantage of quickly evolving technologies, enterprises must be able to quickly identify digital transformation opportunities. The only way to successfully identify potential opportunities or to simply be able to execute on business initiatives is to ensure the quality of the data that is providing these insights in the first place.
An effective data governance tool answers the 5 W’s of data; who, what, where, when, and why? Specifically, it can help enterprises answer questions such as:
Additionally, an effective data governance model puts processes in place that help organizations remain compliant with ever-evolving regulations, such as BCBS 239, CCAR, MiFID, GDPR, and CCPA. This is extremely important in highly regulated industries such as financial services. With a proactive data governance mindset, enterprises can be confident that their data adheres to regulations and policies, freeing up valuable resources for other areas of their business.
An effective data governance model provides a variety of benefits for enterprises.
A key outcome of data governance is improved data quality. For data to be used effectively, it must be trusted. Regardless of where pieces of data reside within an enterprise, they must be fit for purpose (i.e., consistent, correct, complete). An effective data governance model implements processes and rules to ensure that poor data quality is identified and addressed appropriately on an ongoing basis.
With ongoing data quality checks, enterprises have cleaner, safer, and better data, resulting in more accurate analytics, clearer insights, and predictive advantages. Having the right data that you can rely on results in increased actionable opportunities and better business decisions.
One of the most valuable reasons to implement an effective data governance model is for regulatory compliance (such as GDPR or CCPA). Better organization and classification of data will help businesses manage compliance risks and strictly adhere to evolving regulations.
As an iterative process, data governance prevents the duplication of efforts increasing operational efficiency, while decreasing operational costs that may come with correcting these errors.
Apply these benefits to any enterprise within any industry, and it becomes obvious that data governance is a necessary component of a successful business model.
The importance of data governance is evident, and enterprises are quickly realizing it, too. The global data governance market will grow from $863 million in 2016 to $2.2 billion by 2021—a 21% compound annual growth rate.1 While most businesses are quickly implementing new and improved data governance models, very few have an effective one in place.
Without an effective data governance model, the business’ entire ecosystem can be in disarray. Data can be inaccurate, misplaced, or lacking quality. This alone trickles down through the enterprise, decreasing operational efficiency, causing missed business opportunities, and even prompting heavy fines for not complying with regulatory requirements. This can quickly and negatively impact even the most successful enterprise.
Enterprises continue to experience data quality challenges because existing metadata tools are static. They struggle to measure KPIs across workflows, efficiently conduct root cause analyses and unlock enterprise knowledge. PeerNova’s Cuneiform Platform solves these problems by implementing an active data governance framework (we will discuss active vs. passive in an upcoming post) that creates end-to-end (E2E), integrated, and active lineages across disparate tools and systems. The solution perpetually runs data quality and timeliness rules on live data across the organization’s systems, applications, and workflows. This ensures data quality across the entire workflow, which reduces duplicate false positives significantly. Through automation and ongoing data quality checks, the solution not only remedies existing data issues but also continuously manages and monitors data throughout its entire lifecycle (and across siloed enterprise landscapes).
By using the Cuneiform Platform, enterprises can quickly recognize and resolve exceptions, exceed client and stakeholder expectations, and identify the most promising digital transformation opportunities.
It is evident that data governance is the cornerstone of a successful data-driven business. It provides necessary value to the potential goals of an enterprise, from everyday business decisions to long-term initiatives. Data is the currency of digital transformation; it, therefore, becomes imperative that organizations put serious effort into the people, policies, and rules used to govern it.
If your institution is looking for an active data governance tool, like our Cuneiform Platform, be sure to get in touch with us and request a demo today.
Sources:
1“Data Governance Market by Component (Solution and Service), Application (Incident Adjustment Management, Risk Management, Sales & Marketing Optimization), Deployment, Vertical, Business Function & Region – Global Forecast to 2021,” MarketsandMarkets, February 2017
