The pandemic has implications for virtually every area of business, including data analytics. As Lisa Morgan put it in a recent InformationWeek article, “Regardless of whether businesses have been shut down or they’re operating above or below their normal capacity, every company’s data and analytics strategy has been impacted because the underlying data has changed. Customer behavior has changed, supply chain behavior has changed, company operations have changed.”

Read on for a few areas of your analytics strategy that must evolve to keep pace with these changes.

The Predictive Analytics Challenge

Given the unprecedented nature of COVID-19, there is no historical information to fuel predictive analytics strategies. Companies must track how customer behavior has shifted as it’s likely to continue to change as lockdown restrictions lift and some people face unemployment, lack of childcare, or other scenarios with an impact on purchasing behavior. It’s also important that organizations understand customer state of mind—for example, deploying text and speech analytics in the call center to determine how customers are feeling and adjust messaging, strategy, and customer interactions accordingly.

As Brandon Purcell, principal analyst at Forrester, told Morgan, “Data scientists like to talk about the concept of data drift, and typically that happens over time. That process just accelerated and now companies have to start collecting new data and creating new models based on the data from the point when folks started sheltering in place.”

Big Data has Holes

According to Erick Brethenoux, VP analyst at Gartner, knowledge graphs should be an important part of organizations’ evolving analytics strategy as they capture relationships in addition to facts. He also stressed the role of small data, a topic that we’ve previously discussed here at the APEX of Innovation. Finally, according to Morgan, “He also said that people need to realize that machine learning isn’t the right technique to solve every type of problem. It should be associated with other techniques such as rule-based systems, optimization techniques, and graph techniques so organizations can get to production faster in a more accurate manner.”

Consider Data from Outside Sources

To help combat the uncertainty stemming from the lack of historical data, companies should consider outside datasets. Whether its data on infection rates from John Hopkins or Google’s social mobility index, there is a wealth of external information companies can tap to better understand, forecast, and react to the crisis.

Your Data Pipeline May be Incomplete

Morgan writes, “Businesses are discovering that they’re ill-prepared to deal with present circumstances because their data pipeline is incomplete.” Given the current state of rapid change, addressing this gap via automation is critical as organizations can’t afford the time associated with manual approaches.

To learn more about what you can do to evolve your analytics strategy in light of the factors outlined above you can read Morgan’s article in its entirety here.