I have heard too many times: “We have tried this and it is no good” or “We tried it and it is not functioning for us” and several other excuses, that have little or any direct relation to what has been done in the industry or what can be done.
Yes, I am pointing to advanced analytics. And by advanced I mean everything stretched beyond frequency tables. Even though, many multivariate analysis and statistics are just frequency tables viewed from different aspects, it is in general everything that goes beyond pivot table in excel. Advanced analytics can besides bi- or multivariate statistics also be any kind of mixture of different aspects of multivariate statistics, data mining, sampling and probability theories, to evermore popular statistical learning, machine learning, etc.
Given many excuses, I generally find following reasons the biggest barriers when creating, establishing and managing any kind of advanced analytic.
1) Lack of knowledge is primal barrier of accomplishing and establishing a mere culture of analytics in the environment.
2) Fear of knowledge is close accompanied with lack of knowledge. People (especially decision makers) are normally afraid of unknown, due to the lack of rationality.
3) Ego, Pride, Prejudiced,…all the psychological aspects of allowing someone else doing “something” trickery that they, themselves will or might not understand.
4) Being fine with what we have is just another lame excuse. If you come to decision makers and give them numbers, the argument “being fine with what we have” will soon not hold water.
5) “There will be insignificant lift.” But there will be a lift. Remember, if you can make a lift of 0,1% using advanced analytics in comparison to simple frequencies, this is still a lift. With couple more steps, you will come to 1%, 2% or maybe more. But remember, this is still a lift, that mathematics or statistics is doing in your favor. So, don’t neglect it!
6) “We have tried and it is not working”. You haven’t tried enough. Sometimes you need to change a simple parameter in your process and there you go. So think about it in this way. It might also be the fact that you haven’t had the appropriate knowledge or understanding on the problem and after letting it evolve through time, you might be able to tackle the problem again.
7) “We don’t have enough data” or “We don’t have the right data”. Often reason that is an advocate to a lot of decisions. But have you exhausted the data you have? It might be, that a very popular algorithms / methods is not applicable to your dataset, which means you have to try other methods. The quantity of data is also just a lame excuse. Once you have exhausted most of possibilities with your current data, go and start collecting new data.
8) Business model is too complex. Well, break it to smaller and more manageable rules in order to apply any knowledge extracted from data to it. Some legacy business rules (I like to call them boutique rules) usually cost us more to implement and maintain, but in return they give us very little. It is up to you to include or exclude it, but remember, getting information out of data is usually to understand the complexity behind (in other words to reduce the complexity).
9) “We don’t want to pay for software” Ok, what is the next lame excuse?
10) It can also be the problem of persuading the right people and the right decision makers. When you do that, don’t over complicate, don’t go deep into details and mostly, try to stick to simple benefits. SWOT analysis might be to cliche, but in fact it has several good aspects. And remember, build a prototype. A model. Use case. People like to see the trade-offs and curiosity.
There are many other fears people tend to have (as an excuse) to evade advanced analytics, mostly I have listed. And normally, there is also a personal reason stuck in behind, that departments, companies are having difficulties moving toward more advanced and yet more efficient analytics. Compare cloud computing today and minus 5 years ago. What a leap.
If there would be a Latin for the fear of advanced analytics (or statistics) I would certainly name it.