When the famous nerd webcomic XKCD pokes fun at how you use technology, it’s probably time to try a different approach. Recently he posted on Machine Learning and took a swipe at the mindless way some people approach machine learning. His characters discuss a “machine learning system” that involves pouring data into a big pile of linear algebra and stirring until you get the answers you want. Humorous though this is, it also represents a definite school of thought when it comes to advanced analytics – that more data and better algorithms is all you need. With enough data and algorithmic power there’s no need to think about the problem, no need to talk to the people who need the output, no need to do anything except “let the data speak”.
In our experience this approach has a number of problems:
- Just because the data can tell you something, there’s no guarantee that the business cares about it, can use it or will use it for decision-making.
- What the business needs – the analytic insight that they can use to make better decisions – is often not what your machine learning system/data science team think is most meaningful or important.
- How accurate your analytics need to be, and how they need to be operationalized, in order to improve business decision-making cannot be determined from the data – only the people responsible for the decision can tell you this.
- Most business decisions require policies and regulations to be applied too, not just the analytic insight from your data. Simply pushing data through machine learning or other analytic algorithms won’t tell you this.
In the end there is no substitute for knowing what the business problem is. In machine learning (predictive analytics, data mining, data science), this means:
- Knowing what the business metrics are that show if you have succeeded.
- Knowing what decision (or decisions) the business needs to make more accurately to achieve this.
- Knowing how that decision is made today, what constrains or guides that decision, and how analytics might be used to improve the results.
- Being able to place the analytic algorithms you develop into this decision-making context so they can be effectively used once you are done.
We have found on multiple projects that this is the biggest single problem – get the problem (decision) definition right and the odds of successful analytic projects (ones that actually improve business results) go way up. Decision discovery and modeling, especially using the Decision Model and Notation (DMN) standard, is tremendously effective at doing this. So much so that we do this as standard now on all our analytic projects.
But don’t just believe me – AllAnalytics identified this as the greatest problem in analytic projects and research by the Economist Information Unit talked about the Broken Links In The Analytics Value Chain (you can find some posts on this over on our company blog – How To Fix The Broken Links In The Analytics Value Chain and Framing Analytics with Decision Modeling).
If you want to learn more, we have a case study on bringing clarity to data science project as well as two briefs – Analytics Teams: 5 Things You Need to Know Before You Deploy Your Model and Analytics Teams: 6 Questions to Ask Your Business Partner Before You Model – to show how a focus on decisions, and decision modeling, can really help. Or contact us and we can chat.
