In today’s digital age, data is king. With the rise of big data and advanced analytics, businesses are able to gain insights into their customers, operations, and markets like never before. At the same time, with the growing importance of data science, more companies are hiring data scientists to help them make sense of this flood of information.
Although data scientists are experts in analyzing data and creating models, they lack the business skills needed to translate their findings into actionable insights.
This highlights the importance of data scientists having a deep understanding of the companies they work for and the industries they operate in, while also being able to understand the problems their companies are trying to solve and how their work can contribute to commercial success. goals.
It was precisely this challenge that was the starting point for Croatian entrepreneur and scientist Sinisa Slijepcevic and his efforts in the field of data science. Holding a doctorate in applied mathematics, Slijepcevic has worked for international consulting companies such as McKinsey. It was there that he recognized a gap in the relationship between Big Data and the business world.
“There was a huge gap between what we could do with data and what businesses actually needed. They were two worlds that could not speak to each other. On the other hand, business leaders know they need to leverage data and also need to understand how to use huge new capabilities because they don’t really know what you can actually achieve with them. And they don’t seem to speak the same languages – they think they understand each other and they talk very often, but there are so many failures because of that,” Slijepcevic tells The Recursive.
Understand business specificities and ML technologies
Bridging this gap then led Slijepcevic to found his startup Cantab Predictive Intelligence (Pi)whose solutions combine a deep practical understanding of business specifics and the most advanced machine learning technologies.
The company’s core offering is machine learning as a service (MLaaS) through its own cloud AI platform, focused on the financial and pharmaceutical industries in areas such as credit scoring calculations, sales optimization and communications optimization, among others. Their clients also include some of the largest banks in Europe and Africa, such as South Africa’s Nedbank.
For example, one of the approaches used by Cantab Pi is to calculate AI engagement predictors by leveraging over 5,000 dynamic data points updated daily, which are then aggregated and persisted in a data model . The client for this approach was a large pharmaceutical company, and the results showed a 3% increase in sales after four months of deployment.
One of the pillars of Cantab Pi’s success when working with these large industries is that all data scientists on the team must understand the business world, Slijepcevic says.
“It’s difficult for them because it’s very far from their comfort zone. But we insisted that they need to understand the business problem and they need to understand every data point that we use and how that data was created. What is the workflow that created this data point? If you don’t have that understanding, then within minutes or maybe seconds, that data scientist will make a bad decision that will result in days, months and years of work becoming completely useless,” says Slijepcevic. Recursive.
According to the Croatian founder, there are different data points that data scientists should pay attention to and which are then used by customers for their sales.
“Each sales rep gets a recommendation on which channel, what message, what time – they get specific instructions on who to visit, how, what email they should send, etc. All this data is calculated by our platforms and then sent to companies. And when they do, companies that deploy it earn 20 to 30% more than when they don’t,” he explains.
Several skills that can help data scientists master business better
Few business skills that data scientists should also possess are how to prioritize achieving the maximum ROI while minimizing the usage of time and resources, rather than emphasizing on complexity and the size of their code.
They should start with a basic approach and gradually expand their scope as the project proves its value. Additionally, data scientists should be able to recognize when a project is no longer relevant and abandon it quickly, an area where persistent data scientists may struggle.
Concrete examples also show the importance of business acumen for data scientists from the healthcare sector. The healthcare industry generates large amounts of data from electronic medical records, clinical trials and wearable devices – and data scientists here need to be able to understand the needs of healthcare providers and patients, as well as the regulatory environment in which they operate.
For example, data scientists can use machine learning algorithms to analyze patient data to identify high-risk patients and develop personalized treatment plans. However, they must also be aware of the ethical considerations of using patient data and their potential impact on patient outcomes.
Another example comes from the e-commerce industry, where data scientists use machine learning algorithms to develop recommendation engines. These engines use data about customer behavior and preferences to suggest products that customers are likely to purchase.
However, data scientists also need to be aware of their company’s broader business goals. For example, recommending products that are not profitable for the company may result in short-term gains in customer satisfaction but harm the company’s long-term financial performance.
According to Slijepcevic, such examples simply illustrate the growing need for solutions in a particular niche, further motivating data scientists to become more business savvy. One approach the company takes is to first hear what the key business needs are and then come up with a sufficient model.
“Our offer is based on the feedback we receive every day from our customers. So we always start with a deployment, not a concept, brainstorming or anything else – and this approach helps us stay ahead of what others are currently doing,” he adds.
At the same time, each particular use case requires a different approach and this will become even more common in the coming period – another skill that data scientists will need to learn to understand specific business needs.
“I think there are going to be tons of use cases where you have a very specific data architecture, machine learning architecture, etc., that are going to revolutionize that particular area – it’s going to be a mix of business statistics and machine learning. statistical models adapted to particular use cases. Slijepcevic concludes.
