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Potential data insights and what companies can actually get from their data are two very different things. In fact, one frightening study suggested that more than 70% of data goes unused for decision making. Several obstacles to data insight include the wrong tools, poor infrastructure, or lack of expertise. Another significant challenge to data-driven decision making is data health. Companies must take charge of data health for full digital transformation, and luckily, it’s now easier than ever. Let’s explore why.
Data health is a company’s data-readiness. To determine data readiness, businesses should consider if data is valid and of sufficient quality. But what does quality mean in the context of business data?
Each company may have a different set of quality metrics for determining if data quality is sufficient, but a few common benchmarks are:
Without data quality, businesses cannot make decisions and risk major compliance issues. Therefore, regular data health checks should be on the schedule.
Human error causes a significant number of data incidents, both accidental and security breaches. Investing in regular data health checks is not just an ideal; it’s an essential part of running a data-driven organization.
Poor data quality costs each business millions per year on average, according to a survey from Gartner published back in 2017. For industries with sensitive data like healthcare, those bad data can be particularly egregious. A 2021 study published in the HIPAA Journal noted an average of two data breaches per day in healthcare
Even without data leaks putting companies at risk for serious sanctions, bad data costs companies in other ways. Decision-makers, IT teams, and departmental groups wrestle with poor data quality in their day-to-day decisions, creating bottlenecks for ingestion and scrubbing and wasting time on poorly executed business strategies backed by faulty data. The end result is over $3 trillion per year in soft and hard costs, thanks to poor data health. Without a plan, data moves from asset to liability quickly.
Businesses must recognize their data as a whole, almost living being. As such, maintaining data health is every bit as important as maintaining a person’s health.
Decision-ready data only happens with a plan. That plan includes three essential components.
These components provide the foundation for managing data and ensuring that the quality of all data, both real-time and historical, is high enough to get to valuable insights. A troubling statistic back in 2017 claimed that only around 3% of companies’ data met basic quality standards. In the years since then, things have only moderately improved.
A data fabric provides a unified environment, allowing all services, technologies, and data storage to run on the same architecture. It streamlines management and offers a revolutionary way to manage governance.
Data fabric offers:
Data fabrics manage everything from collection to sharing to insights. It addresses challenges like unifying multiple data environments and reducing the reliance companies have on legacy systems.
DataOS® is a first-of-its-kind data operating system. It’s designed to remove complexity so that businesses can manage data health easily with fewer mistakes. It offers declarative data management with AI-powered determination for the most appropriate dataset and transforms data security through a one-of-a-kind tag-based system. It’s time to see what real data health can be.