Data Science & co.

Unless you have spent the last ten years trapped into the deep cave surrounded by nothing else but darkness – the chances are you have heard of Data Science. You may have even tried to reveal the mystery of what Data Science really is – and found everything and – nothing at all. Why is it so hard to find a single point of truth? Well, we’ll try to demystify this for you in a series of texts dedicated to this field.

Data Science is a broad field of study of phenomena within some specified domain, observing their occurrence and behavior. The occurrence of these phenomena is best described by data. Being a “science of data”, it has the basic elements of scientific and methodological principles, such as defining hypotheses, testing them by conducting experiments, rejecting and formulating new hypotheses, all with the aim of expanding existing, and creating new knowledge. In addition, it is very important to understand that this field involves different stages, from data collection, data consolidation and storage, to processing itself, which involves exploration and modeling, that should describe some past patterns in behavior, and generate future patterns of behavior. This approach allows not only the analysis of the past, but also getting insights into the future, which is crucial for any business, as well as the decision making process and strategic planning. Precisely because it gives us the possibility to answer the questions of what, where, when, why, and how – data science has become a silver bullet for every manager aiming to leverage the business.

The evolution of Data Science

The evolution of Data Science is mostly directed by the evolution of computer science. Today we have enough processing power and memory capacities to perform complex computational tasks in a reasonable amount of time.  What this means for the business is that analytics is now not limited to manual work and human ability to dig into a bunch of papers presenting some numbers. We have machines that can give us answers within minutes, or even seconds.

Computational efficiency enables the usage of more sophisticated methods of analysis, like machine learning algorithms. Machine learning algorithms are able to detect patterns within data, and provide predictions of some future behavior. In a competitive world as it is today, the right timing is crucial for companies that aim to keep on track and prosper, while the possibility to get projections of the future means having the power to always be one step ahead of the competition, but one step ahead of the customer as well.

In the world of analytics, there are three main types of analysis – descriptive, predictive and prescriptive analysis. In traditional systems, the analytics is mainly focused on reporting and descriptive analysis. Descriptive analysis is good for analysing the past and evaluating the outcomes of some decisions. But it does not provide the answers to what is going to happen. That’s where the predictive analysis steps in. In predictive analysis, we deal with the projections of the future, in order to define next actions and make decisions. Prescriptive analysis is one more step ahead – provide projections of the future, taking into account different what-if scenarios, for a given context defined by its conditions and constraints. And that is what Data Science is all about – predictive and prescriptive analysis, often being referred to as advanced analytics.

Another great progress of advanced analytics is moving from silo-oriented to data lake architectures. Companies are now using data from different departments and channels, in order to get a 360 view of business. However, having ”big data” sets does not guarantee the power and dominance, but it can be very useful in order to analyze business from different contexts and to conjoin that information, in order to determine how they affect each other, and how can the malfunction of one part produce low performances on other parts, etc.

In the series of posts that follows, we will try to dig deeper into the world of Data Science, machine learning and artificial intelligence, so stay tuned.

About Valentina Đorđević 12 Articles
Valentina Djordjevic is a huge Data Science enthusiast. Graduating from the Faculty of Organizational Sciences, she faced the challenge of packing her (then irreconcilable) interests into a unique career path-cutting machine. Still, she is a real "fox" when it comes to her areas of interest, so we have no doubt that this has given her good signposts. Valentina finds motivation in challenging projects and in working with equally enthusiastic team members. She believes that working on the Data Science challenges helps her to continually grow, examine (her) boundaries, and develop her imagination. Still, she has not yet determined whether the fact that she has never solved the same problem in exactly the same way is a good or bad thing. She hopes her colleagues, or maybe someone in the community, will help her finally come up with the correct answer.

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