Where Do You Stand: Analytics Leaders and Laggards
Everybody knows how important analytics is to remaining competitive. Where does your company and industry stand in terms of advanced analytics maturity?
Different companies and industries are at different levels of analytical maturity. There are still businesses that don't use analytics at all and businesses that are masters by today's standards. Most organizations are somewhere in between.
So, who are the leaders and laggards anyway? The International Institute for Analytics (IIA) asked that question in 2016 and found that digital natives are the most mature and the insurance industry is the least mature.
How Industries and Sectors Stack Up
IIA's research included 11 different industries and sectors, in addition to digital natives. The poster children included Google, Facebook, Amazon, and Netflix. From Day 1, data has been their business and analytics has been critical to their success.
The chart below shows the descending order of industries in terms of analytical maturity. According to IIA, insurance is falling behind because its IT and finance analytics are the weakest of all.
Another report, from business and technology consultants West Monroe Partners found that only 11% of the 122 insurance executives they surveyed think their companies are realizing the full benefits of advanced analytics. "Advanced analytics" in this report is defined as identifying new revenue opportunities, improving customer and agent experience, performing operational diagnostics, and improving control mechanisms.
Two of the reasons West Monroe cited for the immaturity of the insurance industry are the inability to quantify the ROI and poor data quality.
Maturity is a Journey
Different organizations and individuals have different opinions about what an analytics maturity model looks like. IIA defines five stages ranging from "analytically impaired" (organizations that make decisions by gut feel) to "analytical nirvana" (using enterprise analytics).
"Data-first companies haven't had to invest in becoming data-driven since they are, but for the companies that aren't data-first, understanding the multi-faceted nature of the journey is a good thing," said Daniel Magestro, research director at IIA. "There's no free lunch, no way to circumvent this. The C-suite can't just say that we're going to be data-driven in 2017."
Others look at the types of analytics companies are doing: descriptive, predictive, and prescriptive. However, looking at the type of analytics doesn't tell the entire story.
What's interesting is that different companies at different stages of maturity are stumped by different questions: Do you think you need analytics? If the answer is no, then it's going to be a long and winding road.
Why do you think you need analytics? What would you use analytics to improve? Those two related questions require serious thought. Scope and priorities are challenges here.
How would you define success? That can be a tough question because the answers have to be quantified and realistic to be effective. "Increase sales" doesn't cut it. How much and when are missing.
One indicator of maturity is what companies are doing with their analytics. The first thing everyone says is, "make better business decisions," which is always important. However, progressive companies are also using analytics to identify risks and opportunities that weren't apparent before.
The degree to which analytics are siloed in an organization also impacts maturity as can the user experience. Dashboards can be so complicated they're ineffective versus simple to prioritize and expedite decision-making.
Time is another element. IT-created reports have fallen out of favor. Self-service is where it's at. At the same time, it makes no sense to pull the same information in the same format again and again, such as weekly sales reports. That should simply be automated and pushed to the user.
The other time element -- timeliness whether real-time, near real-time, or batch -- is not an indication of maturity in my mind because what's timely depends on what's actually necessary.
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