Attention: High-quality Data is the New Oil

Achieving the right business outcomes from enterprise data initiatives has remained a lofty goal. Here’s why CXOs must be heedful of data quality.

Organizations are in a race to be ‘data-driven’ entities today – a highly desirable state where data, analytics and AI help them make the most appropriate business decisions, predict customer behaviours and gain competitive advantage. In this connected world, we generate staggering amounts of data - global data creation is projected to surpass 180 zettabytes by 2025, up from 64 zettabytes in 2020 - which can be further sliced and diced to arrive at informed decisions.

Still, drawing the right business outcomes out of data has remained a lofty goal. In fact, as many as 85% of data science projects is estimated to be a failure.

Why Data Quality Matters

One of the reasons is the classic “garbage in, garbage out” scenario. The insights and results delivered by even the most sophisticated analytics or AI/ML systems depend largely on the quality of the input data. Factors such as accuracy, completeness, consistency, and reliability of your input data have been decisive in the process.

Every year, poor data quality costs organizations an average $12.9 million,according to Gartner. The research agency warns that poor data quality has direct impact on the organizations’ top lines and business decisions.

Melody Chien, Senior Director Analyst, Gartner, says, “Good quality data provides better leads, better understanding of customers and better customer relationships. Data quality is a competitive advantage that D&A leaders need to improve upon continuously.”

As data volumes soar, the challenges around validating the data and ensuring quality only go up. So, how do data & analytics leaders ensure data quality and success of their data projects? These best practices might come handy:

Set Data standards: Duplicate data, incomplete fields and inconsistent formats etc. are some of the common issues that most organizations deal with. Data verification processes must be comprehensive and preferably automated, with deduplication capabilities. A good starting point would be to define ‘data quality’ in alignment with your company’s business goals and other key metrices. These goals can vary across business functions, so periodic reassessment of ‘quality’ is critical. Data quality profiling helps you with course correction and prioritization.

Identify what’s critical: With the growing volumes of data, it’s not always practical to apply the standards of quality to all your data assets. Identify the organization’s Critical Data Elements (CDEs) or the ‘data that is critical to succeed’. A matured data governance practice will help you identify CDEs to prioritize tasks and goals more accurately. A CDE-based approach will help the organization to scale and sustain their data quality efforts without breaking the bank.

Establish Data Literacy: There are several challenging in establishing an effective data governance plan. The most common problem is the lack of accountability and ownership within the organization. Your employees across departments must identify the importance of data quality and governance. Human error is another persisting roadblock in this journey. Establishing data literacy practices will help users adopt a data-first culture and set consistent data quality metrices. Make data quality a part of the KRA for at least employees that work with data directly. Understand that data silos is a reality; work on aspects to ensure that the organization’s data is trusted, leveraged and understood by employees and other stakeholders alike.

Dealing with exponential data growth: The issue of data quality will only intensify as organizations produce and deal with more and more data. It is estimated that, more than 150 zettabytes of big data will need to be analysed over the next three years .A vast majority of companies believe that poor quality of data in businesses negatively impacts consumer interaction, reputation and the efficiency of operations. With scale and volume, teams often feel overwhelmed and this leads to burnout. You may want to expand your data quality team or invest on AI/ML-powered tools to cut down repetitive tasks.

Work in partnership: Data is an enterprise asset and any initiative around deriving value out of this asset should be a team work. By aligning the data quality initiatives with business goals, data and analytics leaders can ensure board buy-in and involvement. Data quality should become a board agenda and organizations should look at new roles such as data champions or stewards to ensure quality of data assets. It’s important to work closely with individual BU leaders and subject-matter-experts to ensure the sustainability and success of data projects.

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