10 October 2018
When is somebody a data scientist? And how do you use data in your marketing department or wider organisation to create value? The focus was on these questions at a clinic session held by Kees Groenewoud and Jurriaan Nagelkerke on Friday 21 September 2018 at the Beeckestijn Business School. This particular Friday morning was all about the real added value of data workers in the field of marketing.
After a brief introduction by Hans Molenaar (Principal Lecturer and Director of Beeckestijn Business School), there was an interactive session discussing various trends relating to the role of data for the marketing industry. There were further interesting contributions from the floor: the role of Artificial Intelligence, neuro-marketing and the rampant growth of tools and technologies for performing data analysis were all covered. This was followed by a hands-on introduction to the Target to Data model. This model enables organisations in a highly practical – agile – way to form a link between targets and data. All too often we see data-driven marketing come unstuck without concrete objectives that data and analysis need to follow. This sounded familiar to the participants in attendance, and the importance of having a short time to market was highlighted too: in today’s market, gone are the days when the time from strategic planning to marketing execution was several months!
We then took a short break, after which the emphasis was on how you arrange data roles for marketing. Firstly by working through a number of concrete examples of how leading offline and online retailers, banks, publishers and energy companies have set up their data teams around marketing. We discussed the advantages and disadvantages of various arrangements. Finally, the spotlight was on the data professionals making up a dream team for the data-driven marketing department. Because there is so much uncertainty about the role of data scientists we zoomed in more closely on their role. What’s the difference between a data scientist and a data analyst? First of all, we discussed a clear definition of the roles. Next, we examined the differences between data scientists, data analysts and data engineers by looking at the knowledge and skills needed for these roles on 7 dimensions. Finally, we discussed three generations of data scientists: Today’s data scientist, following a data analysis of 1000+ LinkedIn profiles of self-proclaimed data scientists. Followed by the trained data scientist, by reflecting on the courses currently on offer in the field of data science. And finally by looking to the future and mapping out the expectations relating to the data scientist of the future.
After the session we had detailed discussions about how you become a data scientist, whether or not your organisation needs a data scientist right now and what a good strategy could be to gradually attain data-driven marketing.
“A nice, clear presentation. The dual format was clear and complementary and held your attention. Really illuminated a lot.”
Would you like us to deliver a data science clinic at your organisation? If so, you can contact Kees Groenewoud using the details below.
Do you want to know more about this subject? Please contact Jurriaan Nagelkerke or Kees Groenewoud using the details below
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