Today's real-time economy requires companies to leverage data faster than ever before. While real-time insights aren't always necessary, the accelerating pace of business is forcing companies to speed time to insights by an order of magnitude or more.
An organization's technological prowess, its existing technology stack, its organizational make-up and agility, its people, and its culture all affect how quickly data-driven insights can be accessed and acted upon.
"Technology is way ahead of culture. All of this stuff is technologically possible," said Brian Hopkins, VP and principal analyst at Forrester Research, in an interview. "Vendors are packaging data and analytics tools and insights platforms to remove a lot of the complexity. The problem is you still have to fund it, you still have to get people to agree with it, and you have to overcome the organizational issues of data. It means data sharing, co-investment, and governance. All of that has always been a problem and it still is."
As companies strive to use their data more strategically, they often face technological and cultural barriers that impact time to insights. For example, the data leveraged well in one department may not be available to other parts of the organization because it's trapped in a closed system, or because company politics are getting in the way.
"Organizations may be using hundreds of different tools, but the tools may not [interoperate] for business reasons. Every vendor wants to be the hub or key, so they usually allow other tools to inject data, but make it hard to get out," said Luca Bonmassar, CTO and cofounder of hiring platform Gild, in an interview. "Companies that are looking to compete with real-time data need to stop for a second and understand what they're trying to achieve."
According to William King, founder and executive chair of healthcare insights-as-a-service provider Zephyr Health, "The power of big data is being able to serve up relevant insights at a relevant point in time. I don't necessarily need to have massive analysis of massive datasets at all points in time."
Once you've reviewed these nine tips for speeding data-driven decisions and time to insights, tell us about your own experiences. Have you applied any of these in your own organization? Are there other best practices that have worked for you? We'd love to hear from you in the comments section below.