A recent InfoWorld article examines how “shifting-left” can help companies measure and automate data quality improvements. This DataOps approach encompasses all the steps in integrating, transforming, joining, and preparing data for consumption. It’s also the optimal place to address any data quality issues and ensure that all downstream analytics, data visualizations, and machine learning use cases are operating on consistent, high-quality data. 

While there are obvious DataOps benefits, many organizations are struggling to understand which improvements and metrics to prioritize. The InfoWorld piece attempts to clear up this confusion, highlighting three critical focus areas: 

Data Accuracy 

According to one study, 75 percent of business executives lack a high level of trust in their data, and 70 percent don’t consider their data architecture to be “world class.” These trust issues can be improved through better data quality. As such, this should be a primary area of focus for DataOps teams, who can assess data quality metrics for:

  • Accuracy: Accuracy is improved when DataOps integrates referenceable data sources, and data stewards address conflicts through automated rules and exception workflows. 
  • Completeness: With technologies like master data management and customer data platforms, DataOps teams can centralize and complete golden records using multiple data sources. 
  • Usability: Companies can address usability concerns by simplifying data structures, centralizing access, and documenting data dictionaries in a data catalog. 

Data and System Availability 

As business leaders gain more trust in their data through the above measures, they will rely on it for more decision-making, analysis, and prediction. As this happens, there will be a greater expectation that the data, network, and systems for accessing key data sources are available and reliable. As such, this is another important metric for DataOps teams to prioritize. Check out the InfoWorld piece for more on how to do this, including the role of detecting and resolving data downtime.

Data Timeliness and Real-Time DataOps

Another important consideration is improving data speed and timeliness so that users are accessing near real-time information across all environments. After all, data is only as good as its ability to keep pace with up-to-the-minute business needs.

The InfoWorld article has more on these three critical metrics and what you can do to establish them in your organization. And for more on the benefits of DataOps, take a look at this previous APEX of Innovation post.