Few commodities as crucial to an organization’s success as data. From IT and business operations, through strategic decisions, to sales and marketing pipelines, technology professionals need to maximize value from their organization’s data. As such, organizations are giving data governance more attention and increasingly prioritizing it in their IT planning. The concept of data governance encapsulates the policies and procedures necessary to ensure an organization’s data is accurate and the strategies needed to deliver increased visibility, transparency, and control, while reducing risk across the entire data estate.

At every level of business, the ability to access, manage, analyse, and make decisions based on data is of paramount importance. And yet, with so much information, so many entry points, and vast reservoirs of data stored and often remaining dark (unknown or unused), the technology and processes that facilitate access, enhance visibility, automate workflows, and improve analytical output based on data are increasingly critical. Managing data continues to evolve; where once it was treated as individual tasks or efforts with limited cohesive strategy, today methods such as DataOps streamline and consolidate fragmented data management strategies. DataOps is a driver to accelerate and strengthen an organization’s data delivery capabilities to its consumers.

DataOps and data governance are tightly interwoven and, as such, continue to grow in importance.

The dangers of ‘dark’ data

It all comes down to data quality. The downstream effects of data quality have ramifications felt throughout data governance efforts. Recent findings from a survey by Enterprise Strategy Group showed that data management is greatly challenged by a lack of visibility and compounded by data quality issues. Concerningly, 42 percent of all respondents indicated at least half of their data was “dark data” - retained by the organization, but unused, unmanageable, and unfindable. An influx in dark data and a lack of data visibility often leads to downstream bottlenecks, impeding the accuracy and effectiveness of operational data. Data quality was the top driver for organizations’ data governance programs but was also the top challenge that these organizations have to overcome to maximize the return on their data governance efforts.

When you consider the fact that many organizations are experiencing data quality issues, which are difficult to manage (from accessibility to visibility), and in many cases have significant amounts of data that is dark, there is a clear need for more robust data governance solutions providing data landscape transparency united with business context and guidance. Trustworthy data and efficient data operations have never been more influential in determining the success or failure of business goals. When people lack access to high-quality data and the confidence and guidance to use it properly, it’s virtually impossible for them to reach their desired outcomes. The first step is ensuring that data quality is not an insurmountable challenge.

Improving data quality

There are many avenues available to improving data quality under the data governance umbrella of solutions and tools. In addressing the need for improved and efficient data quality, those solutions and tools featuring automation (i.e., automated data profiling, data quality assessment, ongoing data observability, and data remediation) greatly enhance data quality visibility for a group not limited to merely traditional IT roles, but to all data governance teams and lines of business.

This enhanced data quality, therefore, benefits downstream analytical confidence and accuracy for key stakeholders’ insights and business decisions. The ESG study also found that DataOps was overwhelmingly recognized as the primary solution to drive forward data empowerment. 9 in 10 people surveyed agreed that strengthening DataOps capabilities improves data quality, visibility and access issues across their businesses. DataOps helps organizations to improve the quality, delivery, velocity, and management of data and shapes the value of analytics within an organization.

By implementing DataOps-based tools, organizations can not only alleviate the pain and delay of accessing data for end-users, but also improve their ability to govern, provision, and manage the appropriate data to be accessed and analysed, as necessary. In short, successful data governance efforts are built on an array of tools that can strengthen the processes of data management, data stewardship, and the data quality and discoverability of the data itself.

Equally impactful to a successful data governance program is the ability to empower its end-users. The value in a robust data governance is enterprise-wide. The efficiency and visibility in which the data can be used operationally is improved, alongside better data access, controls, and security. A robust data governance strategy also helps build the foundation for automation and greater analytical capabilities, all of which ensure more stakeholders in the organization are guided and empowered to confidently derive insights and take action.

Today’s businesses still face significant obstacles that prevent their people from being fully empowered to bring data to every decision. To gain the advantage, organizations must invest in building a data-first culture - fueled by automation and DataOps processes, high-quality data, holistic governance, and enterprise-wide accessibility.