What is data science and data analytics

Data science versus data analytics

“What is data science?” Is the title of our series, in which we want to introduce you to some basic terms and methods from the field of data science. But what is data science actually? Then what is data analytics? What do data scientists and data analysts need to know and be able to do? We show similarities and differences.

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In any case, data science and data analytics have one thing in common: Both professions deal with (very) large, sometimes heterogeneous and sometimes more or less structured data from which knowledge is to be extracted for a specific purpose. In the best case, with the result that new knowledge has been generated and action is indicated: Customers willing to churn are recognized and can be addressed in good time, impending machine failures are forecast and downtimes can be counteracted, the most urgent complaints are identified from angry customer emails and corrective measures can be initiated become.

The above examples make it clear that it does not matter which industry or area the data for the analysis ultimately come from. All areas of the company have data and almost every one of these areas is currently going through a digital transformation. The digital transformation is not an end in itself: Companies want to learn something about existing or future customers, or their own processes and products, regardless of whether it involves associated warehouse processes, route planning or ordering processes, or whether cases of fraud are to be uncovered.

A date in itself is initially only a value and has little meaningfulness in itself. The same date in connection and in comparison with other data points suddenly becomes part of an overall picture that can be interpreted and interpreted, which data scientists and data analysts grapple with.

Correspondingly, companies are looking for those specialists who can derive knowledge from the data now available and thus help the company to make successful business decisions in the future.

IBM predicted two years ago that the number of jobs for US data professionals would increase more than 7-fold within a very short time. Open end. In job advertisements, either data scientists or data analysts are searched for - or both directly. The job titles sound similar, but the two activities and the corresponding profiles are different. Since data should first be analyzed in both areas and the results used for decision-making processes, a distinction is sometimes not made (or often incorrectly) between data science and data analytics.

Who or what are these data scientists?

Whether in the supermarket when using the customer card, during online shopping, when logging into the computer or when liking, disliking and sharing content in social networks: Everything we do in the digital and analog world becomes a date and of course saved as such. However, the larger and more heterogeneous the amount of information, the more difficult it is to evaluate and define a question to be evaluated. Data scientists have to be able to deal with such large and heterogeneous amounts of data by extracting, cleaning, merging, processing and analyzing data from different sources, with or without a question (exploratory). As for the analysis part, data scientists are experts in applying methods from the field of artificial intelligence and machine learning.

In order to generate real added value for the company, however, a simple analysis of the available internal company data is rarely sufficient. Companies want the integration and evaluation of internal and external data in order to be fully informed, to react faster and to be able to plan better in the future. Not only should internal company data from the past be evaluated, but it should also be enriched with additional external data such as air purity, weather, public holidays, vacations, promotions, advertising, etc. A good data scientist therefore also has know-how from the technical area: What kind of external data is there? How do I get access to the data? How can these be merged with the internal data?

In order to be able to support the company management in future business decisions, a basic set of “soft skills” is required in order to be able to communicate with the decision-makers in an industry and to derive their needs. The tasks of a data scientist also include the transfer of the analysis to operational business operations, i.e. the development of customer-specific and needs-oriented software products, with the help of which the company employees can update the analysis every minute if necessary.

The various activities result in the following requirements for applicants for data scientist positions:

  • The basis for a job as a data scientist is usually a degree or a doctorate in the field of data science, computer science, business informatics or mathematics. Further education, training or simply “having fun coding” can of course also lead to the goal.
  • According to the chosen field of study, the evaluation of large and heterogeneous amounts of data is a welcome challenge for data scientists, especially with the help of methods from the field of artificial intelligence and machine learning.
  • In addition to the actual analysis of the data, data scientists are just as familiar with data acquisition and integration as well as the development of software products. Because in the end there is rarely a singular, static analysis, rather it is about the development of tools that should support the customer or the company in everyday life.
  • This central, increasingly important function in the company then means that, in addition to the "hard skills", the "soft skills" in the sense of communication and presentation skills must not be neglected by a data scientist. In the role of “problem solver” and “solution developer”, data scientists work together with specialists as well as with management and various specialist departments and must recognize, abstract and implement their needs and needs.

Then what is data analytics?

Where data scientists often have to develop or develop a question to be answered themselves or on the basis of the existing database, data analysts are increasingly concentrating on explaining and answering existing, known and recurring questions / problems in the company. As a result, the process of data analysis is correspondingly more focused and is not infrequently based on an already existing, i.e. compiled and pre-structured data export.

If the task of a data scientist is to predict the future, data analysts are often primarily responsible for (retrospective) reporting in the sense of specified KPIs or evaluations in the area of ​​"business intelligence". For the analysis of key figures, complex statistical models are rarely used, rather basic methods and test procedures from statistics are used here.

The analysis of corresponding company key figures often requires associated industry knowledge or a departmental affiliation. Compared to a data scientist, a data analyst often has detailed specialist knowledge and belongs to a company division, i.e. is rarely used independently of a department or only on a project basis.

The following requirements for applicants for data analyst positions result from the various activities:

  • The basis for a job as a data analyst is usually a degree or a doctorate in the field of mathematics or computer science, but sometimes other subjects are also suitable that have a mathematical reference in addition to a subject-specific one (e.g. engineering, psychology). The same applies here: Anyone who likes to evaluate data in their free time can, regardless of their educational qualification, develop into a data analyst through their own practical experience.
  • According to the chosen field of study, the evaluation of (pre-) structured data given a specific question is part of everyday professional life for data analysts, whereby primarily “classical” (descriptive) statistical methods are used in the context of the analysis.
  • Of course, in addition to the "hard skills", the "soft skills" in the sense of communication and presentation skills are also an important point for the data analyst. Data analysts have to recognize the problems of the company employees / departments, abstract them and prepare and present the results of the analysis in an understandable way.

The two profiles are closely related, but still differ in not insignificant aspects. According to the saying “A picture is worth a thousand words”, below is an overview of the similarities and differences between data scientists and data analysts.

What does the Westphalia DataLab do in the areas of data science and data analytics?

The Westphalia DataLab (WDL) focuses on data science. The focus of our work is the development of AI-based software products for companies. In addition to the company's historical data, we use additional external information when developing our products, where appropriate, in order to improve the quality of results and increase customer value. In contrast to consulting firms, who end up recommending the purchase of new business software, our products do not require cumbersome change management, as this often means unnecessary and time-consuming changes to company structures, processes and behavior. Our solutions are ready for immediate use, which significantly reduces the "resistance" that is often encountered when introducing new tools among employees and managers. Customers can determine the level of support and the duration of use themselves, everything can be canceled on a monthly basis.

Our self-service app "Westphalia Forecast" is currently on the market and enables all users to create AI-based forecasts to increase planning security. Previous knowledge of data science or programming languages ​​is not required. All that is needed to use it is historical transaction data - data that is already available to every company. Just give it a try, there is also a free version.

What can you expect in the next month?

In December we will explain to you what is behind the buzzword “Big Data” and whether more is actually always better.

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