Organizations use data analytics in a variety of ways:

  • A health insurance company may want a way to identify emerging risk factors for disease in its patient populations.
  • A nonprofit that connects bone marrow donors with people requiring transplants may look for ways to slash the time it takes to identify a likely match.
  • A national retailer may seek to better understand the shopping preferences and habits of its target customers.

On the surface, at least, there’s little in common among these organizations and what they hope to achieve, but they each must excel at the same thing to succeed: They have to be adept at organizing and analyzing data, and they need a data strategy that ultimately works for them.

Learn how CDW can help your organization optimize its data analytics efforts at CDW.com/DigitalTransformation.

As organizations everywhere have embraced digital transformation, many are now sitting on veritable treasure troves of data that only get bigger every second of the day. One estimate from IDC predicts the amount of data generated annually worldwide will reach 175 zettabytes by 2025, up from 33ZB in 2018 (one ZB equals 1 trillion gigabytes). With data, it’s no longer a question of whether an organization has it — it’s about the capacity to put that data to use.

In the case of a hospital, for example, data might come from patient surveys, medical records, insurance claims and dozens of other sources, and be collected through networks of connected devices — from patient monitors and electronic health record systems to the computers used by physicians and administrators.

But without the tools and experts to run analytics on that data, the hospital can’t understand what that information means, and it can’t leverage it to guide operational decisions.

5 Common Factors for Data Analytics Success

What do organizations that effectively use data analytics have in common? In my experience, I’ve seen five factors:

  1. Their leadership embraces change. They get buy-in at the executive level to build a data-driven culture from the top down.
  2. They assemble a strong team. Data analytics success may start at the executive level, but from there, the team inevitably expands to include internal stakeholders, technology providers, vendor partners, line-of-business owners and a tightly aligned mix of other experts and innovators.
  3. They start small. Rather than swinging for the fences with their data program right away, they look for easy wins in the beginning and adopt an agile approach that lets them grow over time.
  4. They celebrate their wins to create cohesion. As they make progress on data-focused projects, they’re vocal about those successes and how they were achieved. By communicating outcomes to the organization at large, they help others understand how data can help them.
  5. They govern their data. Once they’ve moved beyond the proof-of-concept phase, they scale the program — and invest in the right tools — to fit the organization’s business needs. Further, they create governance policies dictating safe and responsible data use.

No matter their missions and long-term goals, the question I tend to hear most from organizations interested in developing a data strategy is, “Where are we supposed to begin?” My answer, always, is that they already have — and now they just need to take the next step.

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