What is data science?

Data science is the field of applying statistical and mathematical methods in order to extract insight from data sets. The methods applied could be machine learning, neural networks, optimization models or heuristics aiming at finding the right solutions, to name a few. The algorithms are often highly complex, and mathematicians put in a lot of effort to understand the mechanisms of each method.

When able to understand a variety of algorithms, one has a whole new toolbox for analytical problem solving, and thus the field of algorithms receive a lot of attention both in academia and companies. But what are the challenges of integrating the highly academic field of data science to companies that may not be specialized in AI or any other data science field? Where do you start?

For any data scientist coming from academia to the business world, there are a few significant differences one should be aware of, at least if you start working in a company where data science is not the core activity. In business, you should be aware of the short-term aspect and the need for frequent deliveries. Many organizations are impatient in applying state of the art technology. The benefit of this impatience is that it makes the technology evolves faster. On the other hand, it makes many organizations focus more on which technology they want to apply instead of analyzing their needs and choose their technology according to this.

The importance of data

Before AI’s big breakthrough of processing unstructured data, i.e. pictures, natural language and text, algorithms and computing power were the bottleneck for many AI applications. Today as a data scientist, you have a great toolbox of well-documented algorithms that can provide the desired insight. Since the algorithms that already exist are more than good enough for the needs of many companies, the most significant bottleneck will then be the data.

No algorithms will be able to extract valuable insight from datasets of poor quality. Many traditional companies are considering their amount of data to determine if they are ready for making AI applications and are not acknowledging that the quality is just as important. Data inconsistency, deficiencies and lack of documentation are all examples of poor data quality that will slow down the data scientist’s work significantly.

The data science race

The companies that get a head start of gaining valuable insights are those who focus on getting the best data. Based on their needs, they systematically and continuously collect data, ensuring high quality from the start. Of course, this is not enough, so they should set up a team that take care of data, choice of algorithms, IT infrastructure and integration with frontend tools. An agile development process could also be applied in order to obtain results within tight deadlines. Then, after weeks and months of data processing, experiments, testing and evaluations, you will have learned a lot about your company’s operations and hopefully have your AI application ready to help you make better decisions.

Marianne Grødem Reine

Author Marianne Grødem Reine

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