keskiviikko 15. kesäkuuta 2016

Value creation in data science

Creating value in data science is hard work and requires down to earth attitude. One must understand business model thoroughly and provide actionable insights or better yet provide end-to-end working system that captures value and serves it to the end customer. Note that end customer may be e.g. consumer, an employee or a company.

What is real value?


A genuinely good thing. Not great because you say it is great, not credible because of your reputation nor supported because it advances somebody's personal agenda but because it helps corporate customer to solve a problem than prevents business growth (in existing or new business) or allows consumer to enjoy life more. A real value makes measurable difference and improves lives. 

Kinds of insights


A good insight is profitable and actionable. Let's break this down to components and examine them separately.

Profitable insight is a plan or a non-trivial fact that provides value to customer if acted upon. You must also estimate size of the opportunity to decide if the opportunity of worth pursuing.

Actionable insight is doable in practice. That is, you can reach the end customer some way and provide to the end customer the value created by the insight.

Insight "People who buy carrots also buy jeans" is profitable because in principal it allows you to bundle these products non-trivially together but actionable only if there is a way to serve this bundle to end customer, e.g. through shelf placement in retail store or by mobile application.

Ways to create value out of an insight


An insight can be valuable either directly or indirectly. Advising on trends or segmenting the market tend to fall into indirect category whereas recommendation system or predictive maintenance are direct ways to create value out of an insight.

When creating value directly one implements a system that serves value directly to the end customer, e.g. by providing personalized, meaningful and non-obvious leisure time activity recommendations through Facebook.

When creating value indirectly one must provide advice to somebody else who has means to make end customer's life better, e.g.. providing advice to a car dealer on car accessories that make end customer's driving safer and more enjoyable. 

Direct way to provide value has benefit that it can usually be measured and iterated in rapid manner whereas indirect way to provide value tends to have much longer feedback loop.

How to discover profitable and actionable insights?


Start from the basics. Plot this, double check that, check for data consistency, talk to all stakeholders and make sure you got the basics right. You cannot hope to gain valuable insights if you don't understand the business both quantitatively and qualitatively. 

Max out business intelligence tools and existing infrastructure.  Many insights can be found by using business intelligence tools and business intelligence tools can also be used to double check and clarify insights found using more advanced tools like Spark.

Be tools agnostic. If the job requires R then use R and if job requires Spark then use Spark. As a rule it is easier to learn to use multiple tools compared to trying to squeeze the job to the form that your favourite tool chain supports. This is especially important if you change topics in a rapid manner and build on top of existing libraries and packages. Remember that the best tool varies case by case basis. 

Be practical and never forget that your job is to provide value. If problem can be solved using whiteboard and spreadsheet all the better because you just saved everybody's time and money. Then again, if advanced tools are needed then they must be used and it is up to you to overcome any technical hinderance that you might run into.