We are living in a fascinating time where information is accessible and intertwined in all aspects of our private and professional lives. Digitalisation and big data changed the perspective on the consumer as it opened a whole new world of possibilities. Businesses have the power to harness the influx of information and use it to deliver better products, customer experience and customer journey. The complex process of connecting the dots between big data, analytics and business insights is a science on its own – and very much in demand today.
Enter data science and data scientists.
When you hear someone is a “data scientist”, what comes to your mind? Can you imagine what they actually do?
One of the most wanted jobs today still has many misconceptions around it. With this article – which also includes an interview with one of our in-house data scientists – we want to demystify the role of data scientists and help you understand why you need them in your organisation.
Let’s first look at the definition of what a data scientist is.
According to Tech Target, “a data scientist is a professional responsible for collecting, analysing and interpreting extremely large amounts of data…A data scientist requires large amounts of data to develop hypotheses, make inferences, and analyse customer and market trends. Basic responsibilities include gathering and analysing data, using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets.“
Sounds easy? Don’t think so.
The title appeared only a few years ago, yet data scientists are taking the world by storm. No wonder, since companies are overwhelmed with the data that comes from different sources and in staggering volumes. As a result of the digital age, we are “blessed” to have access to abundant amounts of information, but without knowing what to do with it, all that amazing data becomes useless.
Try to imagine data scientists like scuba divers that explore, analyse and reveal insights while swimming in the ocean of digital data. They are curious about “what lies beneath”, and they strive for bringing sense and structure to large quantities of information.
Another critical skill that a data scientist needs to have is storytelling. Once they gather their findings, the next step is to communicate with the team what they discovered and how the organisation should apply these insights. At this stage, it’s crucial to be a creative storyteller and combine it with compelling data visualisation. Data scientists need to look from another perspective, meaning to see how others look at the data and make sure the managers understand the guidelines.
Dr Eva-Marie Muller-Stuler, a Chief Data Scientist at IBM, says six ingredients make a successful data scientist:
Looking at the list, it seems like data scientists are a hybrid between a mathematician, hacker, analyst, communicator, and consultant. This unique combination of skills makes data scientists extremely appreciated and unfortunately, hard to find!
At boobook, we’re lucky to have great data scientists in our team. Frederik, one of them, joined boobook almost five years ago and has been an indispensable team member ever since!
With his background in Psychology (specialised in Theoretical and Experimental Psychology), love for analytics, and creative mindset, Frederik honed the craft starting with data processing and advanced analytics, to finally roll into the data scientist position.
I believe it should be a healthy mix. I want to be included in both analysis and interpretation, and often I want to do the “dirty” work and crunching numbers as much as extracting the conclusions and giving clear directions to businesses. Some data scientists are more focused on results, but in my opinion, the next level is asking more questions starting with “why”.
As a consultant, I start by defining the problem, how we can approach it and find the right method that will lead us to the best solution. Call it consultancy or data science; ultimately, my goal is to give answers to business issues and clear directions on how to act upon them.
Creativity! The ability to think about the problem from many perspectives and test out different methods and models is what makes a great data scientist.
The second thing is being open to constant learning. Expanding your mind is crucial to understand the business you don’t know that much about. Dare to ask, because, without a proper understanding, you won’t do your job well.
My brain! 🙂 I use a combination of Excel and programming languages such as Python together with software such as SPSS, Sawtooth, and R.
I also consider my colleagues an excellent toolkit! This is not a “one-person” job, and often I ask my team members for a second opinion because they find another perspective and ask questions I didn’t think of.
A lot of companies own so much data without knowing what to do with it. They are usually afraid or reluctant to start exploring that data simply because they aren’t sure why and how to approach it.
Make sure to learn how to make a good coffee!
Being a data scientist is a rewarding job, and it can be a very creative profession, but it needs to fit with your character and interests. The new generations have a far better foundation when it comes to languages and programming. They figure out the “how” early on, but what’s more important is to cultivate the interest and practice the mind to focus on the “why”.
Being a data scientist also means having motivation for self-learning! It’s challenging to keep up with the innovations and trends, so make sure you’re always stepping up your game.
Data science is booming, and it’s definitely here to stay! Now I see it more as a bucket that includes a lot of jobs, but I predict it’s going to become more granular and branched out. When you think about technological advancements like 5G connection or the Internet of things, it’s all about being connected and integrated through data.
The data that companies get from users is their most valuable and relevant asset as it helps businesses cater their users with automated and customised suggestions and personalised experiences.
Diverse and inclusive groups are always a winning combination, so your All-Star data team should (ideally) include data engineers, business analytics, project managers, programmers, ML (Machine Learning) Engineers, data journalists, and graphic designers.
Everywhere where there is data, you can apply data science. For example, in healthcare, you can use it for patient diagnostics and predictive modelling, in government you can use it to improve operational efficiency, in agriculture for accurate crop predictions and automation, and in finance for fraud detection. Those are just some use cases, but the possibilities are endless, whether it’s small, medium or enterprise-level organisation.
Now, what will you do with your data?
Don’t leave it floating around without any purpose! Reach out to us at firstname.lastname@example.org and let’s find out together what your data is trying to tell you!