Just as the pandemic has forced organizations of all kinds to adapt, business intelligence tools have had to evolve as well to keep up with the changing needs of customers.
While analytics has enabled organizations to navigate the pandemic, the pandemic has had an effect on analytics as well.
Governments of all sizes and scopes, healthcare organizations and businesses have all used analytics to inform their decisions since COVID-19 began to spread widely late in the winter of 2020. But the needs of those organizations have led to changes in analytics as well, and analytics is now fundamentally different than it was little more than a year ago.
During a virtual roundtable discussion on June 2 hosted by analytics vendor MicroStrategy, four analysts -- R "Ray" Wang, of Constellation Research; David Menninger, of Ventana Research; Mike Gualtieri, of Forrester Research; and Robert Tischler, of German research and consulting firm BARC -- discussed the many ways analytics is different than it was just 16 months ago before COVID-19 became a global threat.
First and foremost, analytics is valued more now than it was before the pandemic, according to Menninger.
Whether dashboards showing the spread of COVID-19 early in the pandemic and now the percentage of people vaccinated in any given area, or predictive models that led to decisions about how much personal protective equipment health care organizations required and whether businesses needed to make drastic changes to survive, data has been at the forefront since March 2020.
And that's led to a perceptual change about analytics. Organizations now recognize its value more than in the past, and make it a priority more than in the past.
Now, a culture of analytics holds sway.
Now, we start the day with analytics, we continue the day with analytics, and we end the day with analytics. That's the biggest reset we've ever seen in how we consume business intelligence.
R 'Ray' WangFounder, chairman and principal analyst, Constellation Research
"We now value analytics much more than we did pre-pandemic," Menninger said. "It's not hard to forecast if you have enough data. We've been through downturns before. We're now much more aware of what data is available and the importance of having a long history of data so we can extrapolate to what's happening today."
Similarly, Wang said that perhaps the biggest change since the start of the pandemic is that analytics is now omnipresent.
"Fifteen months ago, we'd come into [weekly] meetings and ask, 'What happened last week?' That went away," he said. "Now, we start the day with analytics, we continue the day with analytics, and we end the day with analytics. That's the biggest reset we've ever seen in how we consume business intelligence."
One thing the pandemic showed is that organizations need to be able to act and react quickly to change.
Healthcare organizations have had to adjust on the fly as COVID-19 cases ebb and flow, supply chains have had to suddenly deliver billions of doses of coronavirus vaccines since the start of 2021, and enterprises have had to adapt to rapidly changing economic conditions that included almost complete shutdowns at times.
Analytics has played a key role in enabling healthcare organizations to battle COVID-19, from ordering personal protective equipment at the start of the pandemic to managing the vaccine rollout.
In order to provide that real-time information and enable organizations to be more agile, however, analytics technology itself has had to become agile. The days of building machine learning models over weeks and months are over. The predictive information provided by machine learning models is needed immediately, so data scientists must build the models quickly.
Before the pandemic, analytics tools were built with stability in mind, according to Tischler. They were built to run the same analysis again and again.
Now, however, they're built to be flexible, and easily altered as conditions change.
"Now we see people wanting to build things they can change quickly," Tischler said. "If the environment changes, the analytics must change, and if it takes weeks and months to write requirements for new reports, new dashboards, that's totally meaningless."
Organizations, whether building their own analytics platforms on premises or using tools built by analytics vendors, are infusing their analytics tools with AI.
Forrester annually surveys its clients and asks whether they're adopting AI. In the years leading up to 2019, the percentage adopting AI would increase by about two percent each year, according to Gualtieri. In 2019, 54% responded that they were adopting AI. In 2020, that leapt to 68%.
They're automating processes, building machine learning models, doing what they can to become both more agile and more informed.
"There was a big leap in what companies said they were doing in terms of AI," Gualtieri said. "Clearly, something happened where enterprises are interested more in adopting AI at a faster rate. One of the things the pandemic has changed is that it's accelerated the adoption of advanced analytics technologies like machine learning for AI solutions."
Ultimately, analytics has evolved quickly over the past 16 months.
Despite developers being unable to work together in person to build new capabilities and advance existing ones, analytics tools have advanced rapidly since the start of the pandemic in order to enable organizations to make decisions in in the moment rather than long after the fact.
And there's no going back.
"The thing that people got to do that they never really got to do before is collaborate in real time on analytics," Wang said. "That's probably the most exciting thing that's happened for users in a long time."
