By now, there’s not much dispute that advanced analytics capabilities can be a game changer in terms of gaining deeper, actionable insights into your business. However, in my conversations with colleagues and clients alike, I often hear stories of analytics implementations that don’t live up to their potential, and that, in some cases, have gone horribly awry.

If your analytics infrastructure isn’t delivering value by providing you with the answers you seek, maybe it’s time to re-assess and ask why. There are a few common reasons why analytics doesn’t live up to its potential. It’s never really one thing; instead, it’s often a mix of the wrong tools, governance, team mix, and leadership that bedevil analytics projects.


Analytics isn’t a one-size-or-product-fits-all deal. Every tool or technology that finds its way to the market won’t be right for you. Your business goals should dictate your analytics capabilities. Every tool and technology you’ve deployed should function to meet a defined set of business needs.

Do you use each analytics application in your infrastructure, or are there systems people avoid because they’re hard to use or untrustworthy? Does each analytics tool you use deliver value to the organization? If you don’t like your answers, it’s time to re-assess your analytics infrastructure and make a commitment to choosing the right tools for you, not the software sales person.


The tsunami of unstructured data has overwhelmed most companies’ data governance programs. Many are struggling to develop governance policies for data that they don’t understand and can’t wrangle. As a result, some all but give up and just load the data into data lakes (or similar stores) and hope for usable information from queries and analyses. Hope isn’t really an option if you want to drive value with analytics.

You can govern unstructured data. The key is to hit the sweet spot between flexibility and control. If your governance program is too flexible, your data will be dirty, and no one will trust the answers. Conversely, if you overzealously govern, you’ll reduce the advantage that data lakes and similar technologies can provide when implemented as part of an overall IT architecture. In your governance program, consider all data types and set up policies and procedures for each. Integrate them and govern accordingly.

If your analytics infrastructure isn’t delivering value by providing you with the answers you seek, maybe it’s time to re-assess and ask why.

Team Composition

Technology is ever-changing. Today’s hot technology may be obsolete in two years. The skill sets of your analytics team must change as well. If you’re not getting the value you need out of your analytics systems, despite investing in appropriate tools and hardware, it might be time to assess the composition of your analytics team.

You can ensure that your team members are well-trained. What you can’t ensure is that they want to be trained—that they have the right mindset to embrace change and contribute to delivering analytics value.
In my opinion, besides the foundational ability to solve problems, analytics team members should have the following characteristics:

  • Curiosity
  • Creativity
  • Confidence
  • Perseverance
  • Communication skills

Your team must be ready, every day, to take on the problems they face and help answer the tough questions you ask. If they aren’t, it may be time to make some hard decisions.

Leadership and Strategy

Leadership is a delicate issue. No one wants to hear that they lack leadership skills. But most often, it’s not the capabilities of individual leaders that are the problem. Instead, it’s typically that the analytics leadership and strategy is fragmented, both in terms of personnel and goals.

If analytics is left to individual business functions to implement as their needs dictate, the corporate analytics strategy and infrastructure will be fragmented. You’ve read this many times, but I’ll reiterate it because it’s critically important: you must have one person—or at least a small, unified team—in charge of analytics at the corporate level. If you don’t, your fragmented environment will get worse, and your analytics implementation will leak value like a sieve.

Do it Right

Analytics technology is expensive to implement, and a bad implementation can have a high cost, both for your company and you. Before you throw another good dollar after bad, trying to make your analytics technologies live up to their promise, take the time to assess your tools, governance plan, team, and leadership and strategy. If you need to make changes, do whatever it takes to find the money—and the political will—to do it.

Anu Jain
Anu Jain, Vice President, Americas, is at the forefront of the analytics, machine learning, and workflow orchestration revolution. Anu is a leader in Teradata’s transformation from a perpetual license model to a service organization that will drive innovation in open source, business solutions adoption, analytics, and workflow. He has deep technology and domain-specific thought-leadership and expertise in ad tech, media, front-office effectiveness, digital media and analytics-powered industry solutions. His expertise in technology-driven business transformation includes big data, cognitive analytics, predictive analytics, data mining, data warehousing, and business intelligence. Before coming to Teradata, Anu worked for IBM and Deloitte Consulting.

Anu also frequently blogs on his personal site:
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