Inside Data Science
This week at Xcede we spoke to Eloy Sasot, Global Leader in Data Science and Pricing for HarperCollins, a News Corp company. Eloy explored with us his globetrotting career driven lifestyle before settling in his current job, from studying engineering and MBA across Europe, America and Asia to working in Aerospace and Financial Services. He reveals to us his thoughts on his market, from the challenges one would face to the opportunities available.
Whilst being able to speak five different languages, Eloy also represented Spain in the Chess world championships, and believes like Steve Jobs it is important to connect the dots!
How would you personally define your role as a Global Leader in Data Science and Pricing?
When defining my role I would have to discuss how I connect data with business needs. My role is to help the business perform better by leveraging the understanding of both what data says and what the business and people contexts are. We are experiencing an industry that is being heavily disrupted by a transformation towards digital, which brings tremendous challenges but also amazing opportunities. I currently apply my knowledge mostly to the pricing of products, but not limited to that. This involves working a lot with people, particularly in an industry that is more used to working with words than with numbers. As experts within the field will know, a lot of data scientists enjoy just working within the computer labs, and they may find that the organisation may not follow them and their findings. So it is also the job of my team to make these connections to the people within in the organisation in order for them to understand the importance. We are helping the organization walk through this change and it is exciting. We have recruited people through Xcede to join us which makes it even more exciting.
How would you say the data science market is at the moment? – Have you noticed any trends?
Firstly, let me discuss about pricing which is at the core of what we do. There is a boom in the “pricing market” at the moment. In the 60’s marketers put pricing within the four P’s of marketing - Product, Placement, Promotion and Pricing. Now with the advent of data we can look at pricing differently. Pricing has become a truly powerful profit center, especially when connected with other areas of the business. It has changed the way we tackle this area of expertise. In my case, Data Science and Pricing report directly to the Top Management as it needs to be isolated from functional bias.
Thinking more broadly about Data Science, what I hear, read and observe is the importance of communicating data findings to the business in a way simple enough so that they can be understood, which helps remove natural political barriers against change. However, while discussing about general organizational challenges that data science functions face, it is important not to oversimplify key messages that must be understood in order to allow the function to thrive. This is one of the main challenges of this exploding field, which can add so much value, but only if allowed and empowered to do it.
Which software/tools do you think will be most important moving forward for those looking to skill-up in the market?
When talking about data science, the beginning of the answer is often the same: “it depends”. What is the current state of development in your area? If you are starting, you may use very simple tools. At the very first stage I would say it is not all about the 4 V’s of big data: Volume, Velocity, Variety and Viability (some are already adding this 4th V to the initial 3). I would say it is more about truly understanding the concepts that are more important first. You could name it smart data instead of big data particularly at this stage. Then, when you start growing your data science capability and working with big amounts of data linked to your concepts, a review of the tools is needed. The solution must have the capability to fulfill current and future needs, so be flexible and scalable. One can see there are numerous possibilities, as companies specialize in putting different software capabilities together in a full “lego” packages that combines all functionalities together adapting them to the case in question.
All in all, start learning to walk before you can try to run. But once you feel you can run, get ready for a sprinting marathon!
What techniques do you think companies are missing when using data science?
Within Data science you have three different areas of expertise that should be prominent in how you should handle big data;
- mathematics and statistics
- software development
- linking to the business
Having a team with combined strength in all of these three is very important. It is very hard to find people that are strong at all three of them, so usually you have people working together that, combined, bring all three qualities together and work well in a team. Often when something is missing it is because there is an imbalance in the intertwined performance of these three areas.
What are the challenges someone working within the ‘data science’ field might face?
If I had to pick one I would say reluctance to change, which may take many forms. On one hand Data Science is transformational, and on the other human nature does not like change. If to that you add the current “digital disruption” as publishing and other industries are experiencing, you have an explosive cocktail. Again, when you have a much needed change, you must hold the company hand in hand and walk through it. It is important not to go too fast, but at the same time you must mark the pace and explore ahead with the data. I would compare it to driving in a high-way, managing different speeds depending on the situation.
Where do you see the market within five years?
The “data science market” has changed a lot in the previous years, and will continue to change, at an accelerated pace. The ones who will be successful will be the ones that embrace that change instead of fighting against it. I believe data is and will make our world a better world. For instance it is already being used to fight against cancer, or to predict natural catastrophes. Even the now considered privacy infringements will become ubiquitously accepted. Imagine, for instance, sitting in a restaurant with friends, all eating the same, but all being billed different amounts due to our individual data and past relationships with the restaurant and related brands.
What tips would you give to someone aspiring to get to where you are?
In terms of data science, you need a broad set of skills. However, it is understandable that you cannot be strong at everything, for instance in the three main areas that I was discussing earlier. Probably the path is to become particularly strong within one of those areas, with strong basics on the other two, and then get extensive experience that will allow you to adapt to different situations. I would also recommend working in an area with strong focus on digital, given trends and the availability of data in that channel. Nevertheless, above all, I would particularly stress the fact of being curious. Proper Data Science is very challenging. In order not to be consumed by it, you need to be hungry, starving for learning. It’s about drive, curiosity, passion and constant self-improvement. If you have those, you are up for enjoying yourself a lot, as well as opening the doors of success...
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