Skip to main content

What do I do with all this Data?

It used to be that collecting useful data was the hard part. We convinced ourselves it we could only get our hands on good timely data that we could change the face of the hotel and restaurant industry based on our ability to use the data. And then something wonderful happened: the technology evolved to the point where we could collect normalized data from disparate data streams (remember the popular cocktail party term BIG data?) and aggregate into a single location. 

So why are we still for the most part struggling with information deficiency syndrome?

Business Intelligence is no longer a technical issue, but has no become a human resource and human capital issue. The industry has through some normal business cycles cut away the level of middle management who would today be the Chief Intellectual Officers: those that would be providing the meaty nuggets of information to the executive suite in order for them to make the big left-right decisions. The fact that many of those that understand the "big picture" the best and were in a position to really leverage the data that is now available no longer are in a position to do this means that the industry as-a-whole is once again looking for solutions.

So where do we look?

Machine learning or AI (Artificial Intelligence)
It's truly amazing how the industry ebbs and flows. For years we had the people, but not the data, and now for the most part we have the data, but not the people. Accordingly we ask the technology industry to not only provide these disparate streams of data into a coherent data-set, but then to tell us what it means. Enter the concept of Pattern Recognition.

Let's quote Wikipedia: Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is not generally a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output of the sort provided by pattern-recognition algorithms.

So what does that mean in English? Simply stated, and again quoting those much more famous than me: "Past behavior is the Best Predictor for Future Behavior." Pattern recognition allows a scientific and proven method for analyzing your data to identify trends and patterns that once identified and understood can be leveraged to predict future purchasing behavior which can ultimately lead to improved profitability.

I don't trust words. I even question actions. But I never doubt patterns.

While this sounds great, we did get a little bit ahead of ourselves. It is true that machine learning and AI are progressing well and that we can now get analytics from our data that not that long ago was a human process, but we still need to know what we want to look for. In order to know what we need, we still need to design our data properly. One of the reasons designing data elements like selling centers and departments in a database is so important is that you need to decide what information you want to report and analyze before you ever ring your first transaction.

So, the reality is that we not only need BIG DATA; we need BIG THINKING. Technology - at least for the meantime - will not supplant the need for operators who understand the business, what makes it tick, and what questions we need to answer in the business to make it tick faster. Our tools continue to improve and the access to the data gets better and better, but without the acumen in the business and the right people asking the right questions, your business and its mounds of data is just taking up space at a datacenter.

The moral of the story:
1. Retain and empower people in your organization who can ensure that you are farming the right data to be able to get to the analysis that is needed to ask the right questions

2. Operational Alchemy - recruit people who have the ability to transform data from information into intelligence since they are not the same thing. 

Toby Malbec is the principal at TWM Insight LLC

More Blog Posts in This Series

This ad will auto-close in 10 seconds