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Data-driven decision-making is crucial to the growth and survival of organizations, according to Boris Evelson, a principal analyst at Forrester Research.
But simply using data to drive decisions is not enough, Evelson said on Tuesday during a presentation hosted by Orbit Analytics. Agility -- the ability to act and react quickly amid constant change -- within an organization's data-driven decision-making framework is also critical to success.
Gut instinct and personal experience can't be discounted, Evelson said, but data is fact without emotion, and the organizations that rely on data to steer their decision-making process are far more likely to achieve growth than those that don't.
He noted that data-driven businesses have grown eight to 10 times faster than the global economy since 2016. Global GDP has grown 3.5% since then, while data-driven public companies have shown 27% annual growth. Meanwhile, according to Forrester's research, companies that self-identify as having advanced data-driven decision-making philosophies are 2.8 times more likely to report double-digit year-over-year growth than those only getting started with analytics.
But that's the past. Now, growth and survival are dependent on more than mere analytics adoption.
"In the modern world, change is the new normal," Evelson said. "If you didn't know that before last year, you definitely found that out last March when the global pandemic hit and all business strategies were out the window. We believe that this is going to be the new normal going forward, and this new world is going to require adaptive enterprises."
Adaptive enterprises will be those that derive insights from data, he continued. That ability to adapt will be enabled by analytics technology and a data-driven decision-making process built and delivered to foster agility.
Boris EvelsonPrincipal analyst, Forrester Research
Developing an agile data-driven decision-making system essentially takes five competencies, according to Evelson:
- Strategy: The vision, commitment and communication of a plan.
- People: This includes the hiring of a chief data officer and chief analytics officer, upskilling the data literacy of the workforce, and developing a center of excellence made up of skilled knowledge workers who can provide their organization with a series of best practices.
- Process: This includes the enablement of self-service analytics, a data governance framework that strikes the right balance between limiting what end users can do with data while enabling them to be creative, and automating certain tasks using augmented intelligence and machine learning.
- Data: An organization's data pipeline, database and other technologies that bring together its data from various sources into a single environment.
- Technology: AI and machine learning capabilities that empower end users to become citizen data scientists.
"We highly recommend that you invest an equal amount of effort and budget and resources into improving all of these capabilities," Evelson said.
Most key to developing a truly agile data-driven decision-making process is the enablement of self-service analytics.
Empowering end users with the tools -- both knowledge and technology -- to make decisions based on analytical insights in real time drives growth, he said.
"You can't achieve agility, and you can't be adaptive unless you empower your business users with as much self-service analytics and business intelligence and reporting as they can consume," Evelson said. "Self-service is really the only way to become agile and adaptive."
That, however, is linked to data governance, which is also imperative to agile data-driven decision-making.
"There is a very fine line between too much self-service and not enough governance, versus too much governance and not enough self-service," Evelson added. "Hopefully, there is a middle ground between the two, which we call Goldilocks data governance."
All of the competencies together, meanwhile, enable an organization to be agile through what Evelson terms multi-modal analytics and reporting. They empower organizations to do descriptive analytics through dashboards and reports, diagnostic and predictive analytics to get insights, and ultimately prescriptive and actionable analytics to make decisions and trigger actions.
And should organizations fail to become agile and adapt to constant change, they risk irrelevancy and ultimately insolvency.
Forty years ago, the average lifespan of companies in the S&P 500 was about 30 years, Evelson said. As the pace of change has progressed, however, that lifespan has dropped to about 20 years and is trending further downward.
The way to avoid being one of the companies that goes by the wayside, meanwhile, is being able to make data-driven decisions based on changing metrics in a matter of hours and days. Taking weeks and even months to turn data into insight is no longer sufficient.
"Thirty or 40 years ago it was OK [to react slowly], but today that's not acceptable," Evelson said. "They need to be agile and adaptive."