For businesses to take advantage of their data and be data-driven, they need a solid data governance strategy. Source: Shutterstock

For businesses to take advantage of their data and be data-driven, they need a solid data governance strategy. Source: Shutterstock

Why governance is critical for all data-driven enterprises

DATA has emerged as one of the most critical assets for businesses in the digital economy, and subsequently, companies are generating unprecedented amounts of data.

However, the deluge of data also calls for strong and robust data governance, a concept which many business leaders struggle to grasp.

Simply put, it is a set of processes and policies that manage enterprise data assets, designed to identify essential data, draft procedures to manage it, and track all the relevant metrics that help achieve business objectives.

It is generally lead by a team of focused technology professionals and business stakeholders who are in charge of capturing, defining, storing, and distributing it across the organization.

And as collecting data has become much simpler thanks to connected devices, social media platforms, and web browsers, companies have diverse data sources to work with.

Combined with sophisticated analytical tools and massive computing power, businesses can now be truly data-driven, helping them make more informed business and strategic decisions.

Furthermore, the insights from the data could also be used to increase operational efficiency contributing to their bottom line.

But to achieve that status of data-driven enterprise and benefit from actionable insights from their data, they need to establish a strong data governance strategy.

Drafting a data governance strategy

There are many challenges that businesses must face before getting it right.

Some of the common causes of failure include the lack of clear goals around the data project, lack of talent or expertise, or inadequate tools and technology to achieve the set KPIs.

To be data-driven, organization have to take several crucial steps.

First, business leaders have to clearly define the project goals and success metrics based on the data on hand. By doing so, leaders from different business units will be able to align themselves with shared objectives.

Subsequently, there has to be organization-wide adoption of data and analytics tools.

Leadership and relevant management can lead this adoption drive by sharing quantifiable benefits and success stories. As the uptake rate increases, it is expected that collaborations among different divisions too will follow suit.

Beyond that, companies also need to ensure certain critical elements are in place for the organization to become data-driven. Some of the tools required include:

  • Master data management (MDM) These are tools and processes to help ingrate and maintain master data and ensure the integrity of company data assets.
  • Metadata management These allow companies to get a more profound sense of the data, and study other aspects of the data set.
  • Business intelligence — Deploying sophisticated business intelligence tools, companies could yield more actionable insights and predictive analysis.
  • Robust Data architecture Organization need to establish an architecture that allows for the data to be processed in an organization manners, from the moment its extracted, integrated and finally analyzed, to yield actionable insights.

In a nutshell, data governance is a crucial framework that serves as a requirement for businesses to be data-driven.

It helps ascertain that the quality of data is up to specified standards, and ensures decision makers always have the most accurate, precise, and reliable insights about the company, its customers, and the market.