Analytics technology is developing at breakneck speed. Big data, artificial intelligence, the internet of things, digital twinning, nascent quantum computing—the list of technologies is almost endless. How will you lead your company’s analytics efforts in this maddeningly-complex environment?

Do you need to become an expert on each technology? No. Do you need to implement each of these breakthrough technologies? No. Is your competition thinking about how to thrive in the chaos? Are they taking action? Absolutely.

The winners in this IT revolution won’t be companies that dabble in every technological toy box that hits the market. Instead, they’ll be the ones that choose the right technology—and the right people to deploy it. They’ll also devise an analytics strategy to drive operational excellence and produce high-impact outcomes.


Every business needs analytics capabilities. The bigger you are, and the more complex your business is, the more sophisticated technology you need. I don’t need to tell you that. The key to choosing the technology that’s right for you is to look at where you are–and where you want to be–and match the technology to your goals.

A robust technology maturity model (TMM) can help. It can help you assess where you are, and where you want to be, technologically. No TMM will fit your business, or maturity stage, perfectly; they’re necessarily generic and wide-ranging in their assessment criteria. However, the good ones will be comprehensive and provide with you with an appropriate rubric to judge your current state, and the state necessary to meet your goals.
Let your people do their jobs. Encourage innovation, not hidebound rule following.

Plan your technology infrastructure based on an honest assessment of your current technological maturity, and a realistic timeframe for achieving your desired state. For example, if you’re in a low-maturity stage, it probably won’t be possible to get to a highly-realized maturity stage in a short time. So, build your infrastructure, stage by stage, until you reach your goal. Go quickly, but not recklessly. As an old IT clichés goes, don’t try to “boil the ocean.” Maybe just heat it up a little as you go.


You’ve probably heard all the titles: data scientist, data architect, data visualizer, data engineer, quant jock, data ninja, etc. There are as many titles and sobriquets as there are analytics tools. How do you know which one(s) you need?

The title you give people is irrelevant. What’s important is how their mind works. Below are my top-five essential qualities for the people you hire—both from a technical and people-skills perspective—to help you drive insights and value with data:

  • Analytical mindset
  • Curiosity
  • Creativity
  • Perseverance
  • Excellent communication skills

Also, let your people do their jobs. Encourage innovation, not hidebound rule following. You can buy the most feature-rich and sophisticated analytics tools on the market, and you can throw money at an analytics project until you run out, but if you don’t have the right people, with the right mix of skills, and give them autonomy and the freedom to innovate, your project will almost certainly falter.


Even if you build the most technically powerful and elegant analytics infrastructure possible, it doesn’t mean people will use it. Why? Because for all their technical glory, the analytics capabilities you deploy may not meet users’ needs. Analytics strategies involve high stakes. They’re your road map to success. If your map is faulty, your project will lurch along without direction.

When devising your analytics strategy it’s essential to have very specific goals, and explicit, measurable plans and metrics to meet those goals. Target those goals toward those outcomes that will help you achieve operational excellence and have the highest impact on your success. Give the development team autonomy to choose proof-of-concept or pilot projects that will show quick value.

With those high-impact outcomes in mind, create a systematic approach to analytics development that enables you to start small, win fast, and drive excellence. Then, deploy with a scalable, flexible method.

Just Do It

Deploying analytics the right way is a big challenge, but it’s one you must meet to compete. Chances are, you already have some form of analytics in place. Unfortunately, to keep up with your competition and retain and grow market share, you’ll need to implement new analytics technologies and scale up frequently. If you have the right mix of tech, people, and a strong analytics strategy, it still won’t be easy, but it will be doable.

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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