As any reader of this blog will know, data reliability is critical to the success of any data science initiative, yet achieving it is easier said than done. What’s more, failure to ensure accurate and timely data can be a source of significant financial drain. For example, according to Gartner, data drift and other poor data quality issues cost companies an average of $12.9 million every year. 

As we head into a new year, there’s no time like the present to review your data environment, which is why we gathered the following quick tips to help you get optimized to deliver peak ROI throughout 2022 and beyond. 

Establish the Right Data Requirements

The first step in obtaining better quality in your data is to establish clear requirements for the data sets needed and identify where these data sources lie. From there, data teams can decide which features, files, and tables should be included, determine the expected data types, and establish how to extract and integrate the necessary data. These requirements inform a broader organizational framework that ensures all data stakeholders are working with the right sources and getting the correct data into the right pipelines. 

Emphasize Data Orchestration

The average enterprise environment has changed significantly over the last few years with the introduction of various new technologies. With more apps, data sources, use cases, and users, it’s not uncommon for these elements to quickly fall out of sync. As a result, it’s important that inter-team communication, transaction, and delivery are aligned for rapid delivery and high accuracy. 

Automation and Systems

AI-powered data reliability, data discovery, and data optimization are powerful capabilities that keep data accurate, reliable, and complete throughout the entire pipeline without requiring significant manpower from the data science or engineering teams.

The above considerations ensure that companies have better quality data and more accurate insights. They also lay the framework for future innovations in AI, ML, data monetization, and more. For more on the latter and what you can do to increase data monetization efforts heading into 2022, take a look at this recent article from MIT Sloan Management Review.