The COVID shock forces enterprises in every market to accelerate and reshape their data analytics strategies. This trend is likely to continue. “Data Elite” enterprises survived this year through a mix of agility, efficiency, and intelligence. They met these requirements of survival as they accelerated their digital transformations, adopted cloud data platforms, and embraced advanced analytics. As these data leaders continue their momentum in 2021, the data laggards will strive to catch up. 

In this episode, Kevin Petrie, VP of Research at Eckerson Group, interviews Sumeet Agrawal, VP of Product Management at Informatica, to discuss the impact of COVID on enterprises. Sumeet talks about the trends of adoption during the onslaught of COVID and how enterprises are navigating in the post-pandemic era. 

Key takeaways:

• COVID forces enterprises across industries, including both data laggards and data elite, to accelerate their digital transformation journey, adopt cloud platforms, and advance their analytics strategies.  

• As IT budgets feel pressure, data leaders must adopt AI-powered tools, MLOps, agile, and DataOps to automate and streamline processes. 

• The scale of real-time data in the COVID era renders many data analytics models ineffective. Enterprises must augment their existing models and create new ones at a rapid pace to make predictions in turbulent markets. 

• Enterprises continue to consolidate data management tools, processes, and workloads on the cloud. This helps them save time and budget that can be reallocated to key digital transformation initiatives. 

• Enterprises struggle to derive value from historical data alone in a highly volatile economy. They must look outwards and consider procuring external data on open data exchanges in order to get a broader contextual view of their markets. 

• Data governance and MDM are key to data democratization as enterprises integrate multiple sources of real-time, historical, and external data. 

• The COVID era forces data analytics leaders to employ tried and test principles and data management best practices, modernizing their environment, and building data into more of their business decisions.