Yesterday, I summarized two statements and asked four questions about Big Data Analytics. Today, we knock off the first question.

Did anything go wrong in Business Intelligence generally and Web Analytics specifically?

The extreme skeptics towards Big Data Analytics argue that Business Intelligence failed. Others point to cases of success. Who’s right?

Today, we see:

  • Major decisions about pricing, like those recently at Netflix, continue to be made without any analytical support (or, the analytics were completely ignored).
  • Few people, to this day, really understand the numbers they’re looking at. (Few can explain the accurate definition of ‘time spent on site’, for instance).
  • Hundreds of thousands of firms continue to compete just fine without any analytics at all, without any consequences or the cost!


  • Business Intelligence systems are expensive and relatively hard to implement
  • The world moves very quickly, so, frequently, by the end of the third year of a major integration the infrastructure is out of date (Latency)
  • Most people experience data through Crystal Reports or SPSS viewers, which hardly inspire, and generally speaking, it’s a bad user experience

So, there’s good reason to say that BI failed, in the context of the expectations that were initially set.

The high expectations set for such BI systems frequently fail to materialize

And yet, BI is software engineering. And, failure is common in software engineering. Why would we expect 100% success in BI when the success rate in software engineering is so low? Why was this sold as a sure thing?

(Because people buy sure things.)

There have been failures. There have been expectations. There are plenty of scars to go around.

Moreover, everybody, including the people funding these projects, believed that a better dashboard would make them a better driver of a car.

The high expectations set for teams of people frequently fail to materialize

All too often, we expect that we only have to explain a concept once, and that an entire team of people will understand and retain that knowledge.

How many times have you explained the difference between a visit and a daily unique visitor? Or, what time spent on site really means?

It’s not that everybody is stupid or ignorant. Those traits tend to be normally distributed and they tend to cluster in areas of the economy where stupidity and ignorance thrives.

It’s certainly the case that learning takes effort and many people are lazy. It’s also the case that most people don’t spend all day working with this material. Is it any wonder that people forget? It’s not in their job description to remember anything specific.

We expected people to improve their numeracy. To be just as comfortable with a trend line as they are with a word processor. Most analytics professionals expected much better collective decision making.

We expected so much more of people.

What went right?

BI and web analytics made certain individuals a hell of a lot smarter. While the plural of anecdote isn’t evidence, I can say that it made Scott, a line manager I worked with in 1999, much smarter. He used analytics, in real time, to optimize the price of drink specials at the night club he managed. That’s right – contrary to the popular Strata laugh line – certain managers are really capable of making decisions in real time.

Centralized BI practices caused a massive reduction in the bullwhip effect in supply chain logistics. It also led to much more efficient use of warehousing space.

A lot went right. And it made a whole bunch of individuals a whole lot smarter.

What went wrong?

The promise of a new technology didn’t deliver all the benefits as expected. The possibility of failure wasn’t discussed, and the expected results – both in terms of a change in performance and decision making – wasn’t fully realized.

Even those who are hyping Big Data Analytics, and those who are playing down Big Data Analytics, could agree on that.

Do you agree?


I’m Christopher Berry.
I tweet about analytics @cjpberry
I write at