Successful organizations rely on data and analytics, even if business units don't realize it.
"Data and analytics is the most important thing right now," said Gareth Herschel, research vice president at Gartner, during the Gartner Data and Analytics Summit 2021 on Tuesday. "Everyone we serve, from the boards of directors to CIOs, agree that data and analytics is a top priority."
Businesses relying on data to make decisions are 58% more likely to beat their revenue goals, compared to non-data-driven companies, according to a Forrester survey released last year.
But for those seeking data success, they need to know how to get started.
Technology and data leaders need to take a step back and look at the big picture, according to Herschel. Finding fellow change-leaders, building systems that adapt to business change and inserting data into decision-making serve as the foundation of data strategy.
Herschel shared three ideas to consider when prioritizing data analytics in business decision-making:
Don't go at it alone
The painful truth of data and analytics is that no technology leader can go at it alone, according to Herschel. Even if other business units don't realize they need data, it can be applied to solve problems across sales, procurement, logistics, marketing and more.
Data leaders have to build those alliances to successfully implement analytics strategies. "You get results by making it possible for others to make change happen," Herschel said.
Find those partnerships by scouting out other leaders who are not happy with the way things are and have decided to stop doing things the old way, Herschel said. Then, pitch them on how data analytics fits into the change they want to make.
"The only thing they care about in ... data and analytics is 'What's in it for me?'" Herschel said. "This is where you learn to speak their language."
Look at their dashboards, costs, risks and opportunities for innovation to establish a shared vision of how to solve their problems with data analytics, Herschel said.
Know the tech landscape
Planning data and analytics opportunities requires an eye to the future on upcoming technology opportunities.
"Right now, we are in the midst of significant change to move to cloud, the collision of data science with capabilities, and the growing demand for [artificial intelligence]," Herschel said. "We live in a world of necessary and continuous innovation."
Success with technology relies on doing the right things with the technology, not just implementing the newest offerings, according to Herschel. But an awareness of what's next prepares the business for necessary adaptations.
Gartner predicted four technologies will help businesses adapt to data changes:
- Data fabric will automate data integration.
- Graph technology will identify connections between data.
- Generative adversarial networks will use simulations to identify improvements.
- A deep-learning, human-like predictive text will enable machines to tell data stories.
"We need to think about the systems that will not only deliver value today, but also evolve and adapt to continue to deliver value as the world changes around us and these systems have to do it automatically because humans cannot keep up," Herschel said.
Take the opportunity to extend data's influence
With relationships across business units in place, data and technology leaders can take the "profound opportunity to extend influence" and integrate data and analytics into business processes, Herschel said.
"We cannot just build a data analytics infrastructure and hope they will use it," Herschel said. "Take some time to identify opportunities to nudge the way people think."
Leaders can seek out opportunities to insert data analysis into formal business processes, and share data success stories across the organization to influence others on its effectiveness, according to Gartner.
At the same time, give humans credit where it's due.
"Although we as data and analytics practitioners can work to educate business users about what can be automated, we need to ensure we have the right governance model to decide whether it should be automated or left to human decision-making," Herschel said.