Today’s digital-first world has placed a tremendous emphasis on using enterprise data intelligently to drive important business decisions and manage risk. High-quality datasets function as the fuel for enterprises looking to map customers, track where they are in the lifecycle, and inform next steps on how to better engage them.
However, simply having access to high-quality data is not enough to gain a competitive edge. Businesses need to have robust data quality management (DQM) strategies in place if they want to use datasets effectively. Organizations that do not place an emphasis on these may end up with little to ineffective business outcomes, despite having high-quality data available.
For data stewards and managers, now is the time to look holistically at the systems and processes used internally to drive data quality management strategies and determine what the future holds for their role.
What Is Data Quality Management?
DQM is a collection of the processes, tools, and knowledge associated with data to drive forward business decisions in a flexible and agile way. When done effectively, it improves the efficiencies of processing data flows and ensures that businesses can realize their maximum potential of datasets.
Data stewards can help businesses better communicate across siloes, standardize the way data is handled, and make governance models more robust by clearly defining the purpose and intent of data.
These programs have evolved over the years from a highly technical area, often managed by a single team or department, to an enterprise-wide effort involving a host of players. The structure and scope of these programs vary from business to business, but the goal remains the same – to bring greater awareness to the importance of using high-quality data to drive decision-making.
This full-team approach toward driving better data management has been crucial for businesses over the past few years, especially given the explosion of data creation since the beginning of the COVID-19 pandemic. Unprecedented events such as this forced many companies to throw their data management playbooks away (or quickly create one) and adapt to rapidly shifting consumer behaviors – including how they shop, where they live, and how they work. These shifts produced waves of new datasets that organizations need to analyze and use to ensure they stay competitive in a changing business landscape.
What Does it Mean for the Future?
The continued explosion of data will mean that as data stewards define their future data management plans, they must account for volume. Simply put, an enterprise can no longer rely on a single department or set of individuals to process wave after wave of datasets. This has raised a need for efficiency to ensure businesses can process data while maintaining its quality and value.
To assist with volume processing, and future influxes of data, DQM plans need to include a level of automation that can help filter out quality data and better integrate it across the enterprise. Automated processes can assist with data profiling, cleaning, parsing, and deletion, as well as backing data up on a regular cadence. Continuous data monitoring offered by automated technology will also assist enterprises in finding issues within their datasets more quickly than doing so manually, better positioning businesses to solve problems faster.
Additionally, the future of DQM will be increasingly collaborative. With an emphasis on better data integration across the enterprise, more teams will be able to align on shared goals and priorities. Data managers can create tools that stewards on other teams can use to observe and measure the quality of their own data. This collaborative setting will allow for greater knowledge-sharing and transparency across the business regarding how data is stored and used.
As data continues growing in complexity and scale, it is important for business decision makers to prioritize effective DQM strategies for today and for future organizational growth. If businesses can’t process their high-quality data in a way that is both adaptable and scalable, they will not be prepared when another disruptor or volatile market event comes along.
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