You filled a data lake, but no one's swimming. You hired all the data scientists you could find, but they're swamped with too many requests from the business. You know there are some great easy-to-use analytics tools out there, but everyone's still stuck in Excel.
What's next in the world of analytics? For companies ready to convert the promise of analytics into real results, the key action step is to develop an enterprise analytic competency.
Your dedicated analytics teams can't scale fast enough to give the business the answers they want, so push the power to the people on the front lines!
You may have heard the term "self-service analytics," and this is what we're talking about. In our new white paper, "Building an Enterprise Analytic Competency," we take a deep dive into how self-service analytics can fuel the enterprise analytic competency. In short, this competency is your organization's ability to perform meaningful data analysis by department—or even by individual—in a way that provides analytic freedom to anyone who wants it.
There are a few key reasons why building this analytic competency into the fabric of your organization is so important.
- The people who understand your business problems are the best ones to solve them.
Think the hardest thing about analytics is the tech? Wrong. The real challenge, once the tools are in place, is figuring out the right questions to ask using the tools. And functional knowledge is what it takes to get at those really meaningful questions.
Instead of expecting a small analytics team to understand the pressing issues within each business function, rely on the experienced analysts on the front lines of your business. These are the people who can ask the right questions and dig deep using an iterative discovery process.
- Specialized analytics teams can't scale.
You might already be feeling this pain. No matter how talented (or numerous) your data scientists are, there will never be enough of them to support the needs of the business.
Your business analysts are frustrated with long report queues, and the highly skilled data scientists are bored with simple requests that don't need specialization to solve. A self-service analytics capability means your data scientists are freed up to work on the complex problems, and the front-line business analysts get the power to fish for themselves.
- Disengaged employees quit.
You've hired smart people who want to make a difference throughout your business. If you sequester the analytics capability inside an isolated data science vault, your talent on the business side will disengage, stop asking questions, and eventually leave the company to find a more fulfilling job somewhere else.
When you trust and empower people with the tools they need to do their job, you create a culture of engagement. High performers want to be a part of the solution, and with the right tools, more and more people can be involved in the thrill of solving your biggest problems.
We believe the case for an enterprise analytics capability is compelling. We've seen plenty of real-world examples at companies large and small where the insights discovered by business folks on the front lines have had game-changing impacts for the enterprise.
But if tomorrow's industry leaders will be the companies that get this right, why isn't everybody doing it? Plenty are trying, but, surprise—it turns out it's not a trivial endeavor to develop and deploy this capability!
Here's the thing: For too long, everyone's eyes have been on the technology piece. Millions of dollars have been spent on data warehouses, new tools, and fancy platforms. And, still, the big-time results don't materialize. You have executive sponsorship, committed funds, and an effort to push the tools out to the business, but everyone still just uses Excel.
If you're trying to build an enterprise analytic competency within your organization, you must recognize this truth: The biggest challenges you face are cultural, not technological. The reasons for this are as dull as they are predictable: Even though you pushed out the new shiny tool, everyone is still using Excel because it's what they know. People don't feel like they have the time to learn a new process or a new application.
This inertia can be overcome, but it presents unique hurdles as you move toward self-service analytics. Here are five reasons why these types of implementations can falter.
- An Over-Complicated Solution
"My past Big Data initiatives were large and expensive, so this one should be, too!" If you're not careful, you can end up stuck with a solution that doesn't scale, lacks versatility, and that can't support the ad hoc usage patterns the business really needs.
- IT Drives Requirements without Business Collaboration
You're trying to build an enterprise analytics competency, so make sure your tool meets the needs defined by enterprise users.
- A Drawn-Out Process with Unclear Success Criteria
When you're trying to change culture, it's important that everyone knows what success looks like. Make sure you can be successful on a timeline that makes sense for the business.
- Data Stays Locked Up
Be ready to trust your employees with the data they need to do their jobs. For an enterprise analytic competency to develop, you must be committed to unlocking the data vault.
- Transparency Can Bring Insecurity
An effective enterprise analytics competency will bring new insights to the business, but this can mean a painful adjustment for some. "What if everyone finds out that I've been doing this all wrong?" Embrace and celebrate the discovery of improvement opportunities rather than using it as an occasion to rehash past missteps.
The good news is that you can overcome these challenges. We've helped companies successfully develop their enterprise analytics competency, and we know what works. We've laid out a blueprint for success in this white paper, and we're happy to share it as a roadmap to get you on your way. No matter your company's size or existing analytic capabilities, you can start pushing the power to the people today!