“The end goal of having a data culture is to make better decisions more often.”
—McKinsey, Why data culture matters (2018)
For the last few decades, brands have heeded the call for big data and analytics and have spent countless hours and budgets working on their internal capabilities that would turn the dream of data culture into reality. The concept is simple: aggregate all of a company’s data together so that it can be accessible and useful across the enterprise to aid data-driven decision making.
But, when COVID-19 struck the US in March 2020, historical data instantly became less valuable to guide decision making. For brands that had become reliant on data, it was as if the lights had gone out.
After all, the game had changed. The last pandemic was over a century ago.
Internal data was helpful directionally—at best.
Leveraging external data during COVID-19
From the earliest days of the crisis, one thing became blatantly clear. COVID-19 will permanently change many aspects of our society—from how we work, learn, communicate, and shop. The companies that can best navigate this storm will outmaneuver the competition and be in a stronger position once the dust settles.
When the data you have isn’t enough, you must look elsewhere. The world outside your company walls is data-rich with plenty of information, intel, and signals to understand the world. It exists in real-time, which makes it better aligned for times like these when the market is in constant flux—what may have been true last month may not be valid today.
But how does a company access external data? It is often unstructured and not written in a way easily understood by machines, because people talk out of context, in hyperbole, use emojis, incomplete sentences, incorrect grammar and sarcasm.
Analytics solutions providers have been able to help their clients tap into external data to guide data-driven decision making during COVID-19. Some solutions connect to more than 13,000 data sources such as social media, product reviews, patents, blogs, forums, key influencer posts, research papers and more, and use AI and natural language processing techniques to extract critical trends and predictive insights. When these connections are made and incorporate external data into the overall analytic framework that has internal data, accuracy jumps from 35% to greater than 90%, which translates into more confidence in the decision-making process.
This is critical at rapidly-changing times like these. Even before the pandemic, there was a clear correlation between companies using data and their success in driving profitability, increasing customer retention, and top-line sales.
COVID has proven that it’s time to double down.
About the Author
Gil Sadeh is Co-founder & CEO of Signals Analytics. Prior to co-founding Signals, Gil served as a military intelligence and reconnaissance consultant for several defense-related governmental entities throughout the world, utilizing his vast experience honed as a commanding officer in an elite special forces unit of the Israel Defense Forces. He is a frequent guest lecturer and editorial contributor on the application of open source and signals intelligence to drive innovation, delight customers and reduce the risk of commercial decision making. Gil holds an LLB and MA in Government, Diplomacy and Strategy from the Interdisciplinary Center (IDC) in Herzliya, Israel.
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