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How AI Democratization Will Change the Way Analysts Work

Data Basics, Scaling AI Marie Merveilleux du Vignaux

The continued rise of data democratization and, more ambitiously, of AI, is transforming the role of analysts little by little. As vast datasets become the norm, organizations must begin to make data more accessible. This means that analysts will now be able to access and work with data in a self-service manner and extract value to make better day-to-day decisions. What does this look like in practice? 

→ Download 8 Key Concepts for Data Analysis in the Age of AI

No More Relying on Data Scientists and Engineers

With self-service data initiatives and platforms (like Dataiku) that enable AI democratization, analysts no longer need to rely on data scientists or engineers to give them the data for their analysis. In other words, there’s no need to wait for massive amounts of data to be preprocessed and go through complex data cleaning pipelines. Analysts will be equipped with ways to understand data and leverage it to inform decisions and unearth new, value-added opportunities for the organization.

Embracing Richer Datasets

The key to analytics in the age of AI is to use broad sources of data for enrichment. While small data often means siloed departments working with the data they collect, using larger datasets and more advanced tools allows analysts to break down the barriers between departments (within the limits of established governance and data privacy guidelines, of course) and even bring in third-party data (i.e., data from sources outside the company — think weather, social media data, etc.). 

hourglass and clock

Doing More in Less Time

Whether through data aggregations, mass actions, or data visualization, analysts can produce more analyses faster with this newly increased access to data. 

  • Data aggregations allow analysts to separate data into groups, which can be aggregated independently of one another. They can be used to detect trends, make comparisons, and reveal insights analysts would not have noticed if they were looking at isolated data elements. Data aggregations are the best way to get statistics and results faster or to dive into deeper data analysis.
  • Mass action capabilities allow analysts to work on all their data, not just static extracts or siloed databases. With data science platforms like Dataiku analysts can dramatically speed up data preparation by joining and grouping datasets or aggregating, cleaning, normalizing, enriching, and deduplicating records, all with a few clicks.
  • Data visualization allows for quick and easy identification (plus sharing) of unknown trends and patterns analysts would probably not have spotted just by looking at their dataset. It provides a quick answer to complex questions, regardless of the volume of data. 

Conclusion

The point of the matter is this: it’s time for analysts (and other non-technical profiles) everywhere to take back the data. The technology and the tools today make it possible for anyone to take their analytics game to the next level. 

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