The 7-Habits Of Good Data Scientists
There’s one sure thing you can say about data science — it’s a lot of things. Data science is not necessarily one single thing, skillset or methodology. This is why data science is always said to be an ‘interdisciplinary branch’ of science that combines mathematics, human behavioral and workflow studies, flexible use of logic systems and a core employment of algorithms.
This makes being a data scientist pretty hard work, as if algorithmic logic wasn’t already pretty tough.
More than just data analytics, more than just big data insight, more than just the ability to handle new streams of raw unstructured data and more than just knowing how to drive a database while blindfolded, data scientists have to understand business and be flexible super-performers. So what core attributes make a good data scientist?
Simon Asplen-Taylor is interim chief data officer (CDO) and founder at data analytics advisory company Datatick. He has previously served at casino and online gaming company Rank Group where he and his team have made use of WhereScapetechnologies for data science centric work, using the WhereScape’s data warehouse automation & big data software.
Asplen-Taylor suggests that the first thing we need to realize is that the data scientist’s role is never homogenous. Different skills are required for different tasks in different roles in different ‘digital workflows’ in different industry verticals in different world markets.
He advises that organizations who want to embrace data science competently need to have a data strategy that is aligned to the business goals – and, crucially, it needs to be written by a ‘business savvy’ chief data officer (CDO) who can align all the capabilities of data to the business – increasing revenues, reducing costs, reducing risk, increasing customer and employee satisfaction.
“The work of data scientists is, by definition, experimental. They need to be allowed to experiment and the outcomes may or may not be successful, but do enough experiments in the right areas… and you will find the value,” said Asplen-Taylor. “Considering problem solving experimentation further, data scientists need to follow not to lead i.e. they need to be given a problem to fix, which means they need business analysts to define the problem… and, after their experimentation phase, they need someone to test the outcome of their projects, validate the results (so they are not marking their own homework) and they need IT people who will put their models into a production environment… and to then document them (which is key from a data privacy perspective – ensuring that what they are doing is transparent) and support the models.
