The State of Enterprise Data Quality: A Survey of Data Team Priorities

There are endless stories in the tech and business press about data issues. And it doesn’t really matter the context or timeframe; history is rife with scenarios where data told someone to turn left when they actually should have turned right. In our modern world, poor quality data, or lack of the right data insights, leads to massive air travel gridlock, crypto exchange implosions, and a host of other genuinely disruptive and harmful issues that occur with regularity. 

Poor data quality poses a substantial threat to businesses, and the resulting harm can be significant. It can distort analytical insights and drive detrimental decision-making, ultimately impacting business performance.

We could go on, but the punchline in all of this is the need to emphasize data reliability. Reliable data is foundational to every aspect of business operations, enabling informed decision-making, operational efficiency, and disciplined and accurate management of technology costs. 

The results of a recent survey conducted by Acceldata indicate that there are significant challenges facing organizations in maintaining data quality. The survey polled data leaders and data engineers about their efforts to deal with data quality issues. What’s glaringly apparent is that these individuals are not only investing heavily just to maintain baseline quality, but are clearly spending an inordinate amount of time putting out unplanned data issues. This amount of firefighting has trickle down effects that not only add risk to data across an enterprise environment, but also divert efforts from initiatives that are intended to yield great results from data and maximize data investments.

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The findings paint a picture of data leaders who need help. Let’s look more closely at the key insights from the survey:

Data Leaders Are Under Pressure

The most striking result of the survey was that 95% of data leaders admitted that their teams spend more than 25% of their time addressing data quality issues. This statistic highlights the magnitude of the problem, indicating that data quality concerns are a pervasive issue in modern data environments.

Half of Data Leaders in Crisis Mode

Within the group of respondents who reported spending more than 25% of their time on data quality issues, a staggering 50% stated that they are allocating more than 50% of their time to resolve these concerns. This means that a significant portion of data leaders are mired in a never-ending cycle of addressing data quality issues, leaving them with limited time for strategic initiatives.

The Impact of Bad Data on Productivity

The continuous firefighting of data quality issues has a direct impact on productivity. When data leaders and their teams are consumed by data quality concerns, they have less time to focus on analytics, innovation, and strategic projects. This will undoubtedly result in missed opportunities and hinder an organization's ability to compete effectively.

Expensive Consequences From Bad Data

Poor data quality can have costly consequences. It can lead to errors in reporting, regulatory compliance issues, and customer dissatisfaction. Organizations may also find themselves making decisions based on inaccurate data, which can result in financial losses and damaged reputation.

Need for a Data Solution

Given the severity of the issue, it is evident that organizations need a solution to address data quality challenges more effectively. Whether through improved data governance, automation, or advanced data quality tools, the survey highlights the urgency of investing in strategies to alleviate this problem.

Using Data Observability to Drive Better Business Outcomes

Acceldata's recent survey provides a sobering view of the state of data quality in the modern data environment. The fact that 95% of data leaders are spending a significant portion of their time addressing data quality issues is a clear indication that there is much work to be done in this area. The consequences of neglecting data quality are not only productivity-related but can also have far-reaching financial and reputational implications for organizations.

As businesses continue to rely on data to drive decision-making, it is critical that they prioritize data quality as a strategic imperative. This includes investing in the right tools, processes, and governance to ensure that data is accurate, reliable, and trustworthy. By addressing data quality challenges head-on, organizations can unlock the full potential of their data and gain a competitive edge in today's data-driven landscape.

The Acceldata Data Observability platform is helping leading brands like Dun & Bradstreet, Hershey, HCSC, and others improve the reliability, productivity, and cost of their data environments.

The Acceldata platform provides:

• Insights into your data and data pipelines from start to finish to ensure data gets delivered properly and on-time
• Better data quality and timeliness by tracing transformation failures and data inaccuracy across tables and columns
• Rapid data incident identification by shifting-left problem isolation
  

Get a customized demo of the Acceldata Data Observability platform and learn how data reliability can help you.

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