Is Data Scientist still the sexiest job of the 21st century?
This is one article in a series of 5 publications on your organizations’ Data Analytics strategy. The series, called ‘Moving beyond point solutions and pilots in Data Analytics’ addresses five challenges that organizations most experience when they want to upgrade their Data Analytics from point solutions to strategic, data driven and coherent governed activities.
A lot of companies we come across are successfully carrying out a handful of Data Analytics use cases. Some even implemented a big data platform and loaded a couple of large data sources on it. Believers in the business and dedicated Data Scientists flagged a couple of successes for the company. These companies now question themselves, what is needed next?
What is to be learned from companies with strategic, data driven administrations? How can you organize the Data Analytics function within your organization, while working on maturing your Data Analytics approach? Which resources do you need? What does it imply for your business and for the people within your business?
So, the first question we are asking in this series: Data Scientists still have ‘the sexiest job of the 21st century’?
Why the Data Scientist as-we-know-it is a dying breed
The Data Scientist often seen with our clients is able to combine large amounts of unstructured data, extract relevant data (data mining) and analyze this data to come up with useful insights. In doing so, the Data Scientist uses unstructured data, originating from various sources as e-mail systems, databases and social media, using e-mails, pictures and chat messages as input. The Data Scientist is able to structure data by using multiple techniques and writing algorithms, building models to predict future happenings. Is this still a sexy job? Or is it a dying breed?
In the field of Data Science, self-proclaimed scientists are taking ground. Which is possible, as the Data Scientists’ job title is not protected. Shown by the multiple Data Science groups, often counting over 50,000 members and the 86,065 LinkedIn members calling themselves Data Scientist, the Data Scientist is a very diverse species.
Unfortunately, becoming a Data Scientist is not as hard as it once used to be. At the moment, the worldwide web is spreading data courses teaching the basic tricks. Nearly all institutes offer lessons in the topic of Data Science (as shown by Coursera, Udemy, Udacity, and DataCamp, for example). Resulting in an extensive community that thought themselves the basics of the game.
As visualization- and reporting-tooling has become widely available nowadays (and even adheres to the group of standard analytics functionality), a Business Analyst is able to create most of the insights needed, without the help of a Data Scientist. Current off-the-shelf products largely do the same thing as the classic Data Scientist used to do. And even better: Using the right tools outperforms the Data Scientist, as it limits mistakes by limiting manual tasks. Hence, Data Scientists as-we-know-you: you are no longer needed.
There is still hope...
But, luckily, there is still hope, dear Data Scientists. The Data Analytics competency is evolving quickly with extreme data, handling real time data and applying complex and unique statistical methods. Moreover the industry is heavily demanding applications of machine learning, deep learning and artificial intelligence. This is the sweet spot where the Data Scientist will have to stand out. Enabling organizations to move along small pilots and Proof of Concepts, to sustainable data driven services. Companies that want to take Data Analytics to strategic, data driven administrations should question themselves how they prevent their Data Scientists to be replaced by tools and make them future proof!
In our next stories, we will explain how businesses are able to optimize the competences of the real Data Scientist within their organization; what an optimal Data Analytics team should look like; what expertise best supports real Data Scientists; where organizations should position their Centre of Data Analytics; how should they organize the governance and processes to benefit from their Centre of Data Analytics ; and how they should make sure data is top notch in enabling futureproof Data Analytics. All are advised in enabling organizations in becoming a strategic, data driven administration.
The research for this article, which is a series of 5 with the collective title 'Moving beyond point solutions and pilots in Data Analytics', has been done by my team at Atos Consulting. So the thought of the gentle courtesy goes out to Tom Konings, David van Steen, Marcel van de Pol and Carline Nauta.