Driving Value with Analytics: Technology, People, and Strategy

Driving Value, the Right Way

You can’t ignore it. Well, you can, but sticking your head in the sand won’t stop the analytics landscape from changing at breakneck speed. Big data analytics, 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 maddenly-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 this IT revolution and taking action? Absolutely.

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—for their business, and devise the best strategy to implement it in a way that drives value and produces optimal results quickly and efficiently.


Every business needs some sort of analysis capability. 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 the goals.

A robust technology maturity model (TMM) can help. It can help you assess where you are, and where you want to be, on the technology-sophistication scale. No TMM will fit your business, or maturity stage, perfectly; they’re necessarily generic and wide-ranging in their assessment criteria. However, most of them will give you a pretty good idea of where you are and what the stages beyond your current state are.

Plan your technology infrastructure based on an honest assessment of your current stage, and the stage you can realistically achieve. 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, start building your infrastructure, stage by stage, until you reach your goal. As one of the 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 and monikers: 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 really 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 managerial perspective—to help you turn your data into information that drives insights and value:

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

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 an analytical mindset, your project will almost certainly nose-dive. People are that important.


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 falter.

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 your most important business priorities. Also, give the development team autonomy to choose proof-of-concept or pilot projects that will meet your goals and deliver data-driven insights quickly and show the value of your initiative to the organization.

In the development process, use techniques such as use cases, persona development, and journey mapping to ensure that the analytics you deploy work for the people you’re trying to help. Create a systematic approach to analytics development that enables you to start small, win fast, and 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.

I’d like to hear your thoughts about implementing analytics initiatives. You can leave a comment below. You can also contact me by email at anu.jain@thinkbiganalytics.com, or leave me a message or shout out on Linkedin or Twitter. Also, if you like what you’re reading, please click below to share it.

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