Imagine, you are the first data scientist in a company, may be in the industrial company, in one of the old industries, old economy branches. Then, you are an unicorn. Basically, you start data science from the scratch: you must introduce, explain, promote and establish data science. To manage this challenging task, dare to start simple!
Three years ago, I was exactly there – the first and for some time the only data scientist in the traditional, old industry company. The challenge was to start. They all say, start with the low hanging fruits. For a data scientist the low hanging fruits are KPIs and other metrics, reports, dashboards, historical analysis. But here is the thing. On one hand you, your ideas about machine learning projects, your training on advanced, fancy methods which you are really looking forward to apply. On the other, the reality of progressing but not completed digital transformation, messy data, people do not fully understand what your job is, do not know what is possible. To overcome this low hanging fruits dilemma, I needed to get rid of my, a bit snobbish, academic glasses.
There are a lot of reasons to start with low hanging fruits. Above all and the most reason is that these use cases help you to understand the business model. This is the very first thing each data scientist should always approach at any company. Deep dive in to the business model helps you to access where is your contribution, where are the high impact use cases and projects, where is business value. Sometimes applied method equals business value. But unfortunately for us, method affine people, the organizational needs of a company determine solely the business value of any project or use case, not by the method. Applications like dashboards and reports are relatively simple to produce, even through time consuming, and have high impact for the company. Moreover, they are easy to communicate. Especially, if your company is in the beginning of data science journey, the combination of high business value and good communication of the project will help you to promote and establish data science. People get to know you, you build a network, and you build trust with your stakeholders. This will bring data science forward at your company, together with your personal success and forthcoming sophisticated projects.