“Machine Learning is redefining the type of questions a computer can answer, and my job as a data scientist is to put this power in our customers’ hands.” – Josu, Data Scientist
When researching Data Science, you will find a lot of different information. From generic explanations, articles about the importance of Data Science in companies to discussions about why Data Scientist is “the sexiest job in the 21st century”. With this overabundance of information, it can be difficult to get an authentic picture of Data Science.
So, let’s speak to an actual Data Scientist! And even better – to one who works at a fast-paced and innovative Tech Start-up. I talked with Josu, Data Scientist at Lana Labs, to not only get a better picture about Data Science and it’s potential, but also to understand why large companies are still struggling with that topic. Josu explains how no two working days are alike, how corporations often have unrealistic expectations and how we have not yet understood the full potential of Machine Learning.
Hi, Josu. Thank you for taking the time. Would you like to introduce yourself?
Sure. Hi, I’m Josu. I joined Lana Labs in March 2018 to work as a Data Scientist. I come from a mechanical engineering background, so working in Data Science was initially out of my comfort zone, but it has grown on me now and, retrospectively, it has been a very good decision.
How would you describe your job in one sentence?
A Data Scientist is a person who is better at statistics than any software engineer and better at software engineering than any statistician.
What does a typical working day look like to you?
I don’t think we have anything like a “typical” working day, which is a good thing. Every customer project is a new challenge since it not only involves the technical challenges of working with new systems and new data but also understanding the specifics of the industry. Outside of the project work, there is always a lot going on internally: Testing new features, discussing the product roadmap, prototyping new analytics,… Since we’re still a small company, the Data Science team is pretty much everywhere.
What do you enjoy working as a Data Scientist? What do you not enjoy?
Data has become so central to our lives and Data Science has bloomed as the main discipline to transform it into value. Working in Data Science today feels like being a computer scientist in the 60s: The discipline advances so fast that what was considered cutting edge a few months ago is obsolete now. It can be hard sometimes to keep up with the pace of development, but the Data Science community is very open and welcoming, even for people that don’t have a background in mathematics or computer science.
Do you think the hype around Data Science is justified?
Perhaps the right question is whether the hype around Artificial Intelligence is justified. There have been some incredible improvements in what computers are able to do in the last couple of years, and it doesn’t look like the pace of innovation is slowing down. Machine Learning is going to be everywhere, and I don’t think we have fully understood what that means for organizations, for the workforce and for society in general. I have the feeling that too much attention goes to the promise of general AI (that is, a machine that is as smart as a human) and not enough to the real problems: How can we make the most out of these innovations in the most responsible and ethical way?
What added value do Data Scientists bring to the company?
There’s an old saying in computer science that goes “a computer should never ask a question it should know the answer to”. Machine Learning is redefining the type of questions a computer can answer, and my job as a Data Scientist is to put this power in our customers’ hands. The outcomes range from improved transparency and compliance to automation, prediction, and ultimately a transition to a fully digital organization.
What’s the difference between a Data Scientist and a Data Analyst?
To me, Data Science encompasses a broader range of topics than Data Analysis. A Data Analyst typically focuses on transforming data into insights and making them consumable in the best possible manner. A Data Scientist, on the other hand, is also concerned with building models and making predictions about the future, so the work is not only focussed on the past.
You are working on Process Mining projects for customers of Lana Labs. How are you supporting those projects?
I like to think of our projects as an end to end journey where Process Mining is a tool that we use to solve a bigger problem. We invest a lot of effort in understanding the industry, the company and the processes we are working with, as no two processes are the same. On the deliverables side, we focus on generating insights our customers can act on so that they can continue the journey towards process excellence even after the project is completed. I believe this end-to-end approach sets Lana Labs apart from other companies in the market.
From your own experience, do companies use their data correctly and efficiently?
My impression is that companies, especially large ones, are often too conservative in the way they approach their data and too unrealistic in their expectations. When building Data Science capabilities within an organization, it is often more sensible to do many smaller projects that allow the Data Scientists to iterate faster and gain velocity. Setting unrealistic goals in a company that doesn’t have Data Science expertise can quickly lead to data projects being deprioritized and for the Data Science team to lose management support.
Last question – do you think companies will need Data Scientists in 20 years?
I hope so! Jokes aside, I think we’re only seeing the tip of the iceberg of what Machine Learning means. Currently, we’re just catching the low-hanging fruits: Better analytics, predictions, automation, etc. The interesting use cases, the ‘unknown unknowns’, are yet to come, so the next 20 years will be a very exciting time.
Thank you, Josu!
What have we learned? Data Science is a fast-paced discipline which is sometimes challenging to keep up with, even for Data Scientists. Thus it’s not surprising that companies are struggling to find the right approach for building Data Science capabilities. Although the potential of Machine Learning has not yet been exhausted, current possibilities can already contribute significantly to the success of companies. Because of that, we are curious to see how Data Science and the application of Artificial Intelligence – especially Machine Learning – will develop in the future and how this will influence our professional and private lives.