What are The Differences Between Big Data and Data Analytics?

Introduction

Especially in a business context, the metaprocess of digitization has established numerous terms relating to data analysis.However, on closer inspection, it is not always as easy to name their specific meaning and delimitation as one might think at first glance.

Lots of new terms and some need for explanation. We would like to shed some light on the darkness and explain the differences and similarities of a common pair of terms: ⭐⭐⭐All-in-one web analytics⭐⭐⭐ big data and data analytics.With the emergence of new branches and branches of industry, the need to make processes, occupational fields and technologies communicable with new terms grows.

Let's Understand

is usually associated with the continuous generation of a massive amount of data.However, the quantity that is generated isn't the crucial part but rather what is used with the data.

Understanding science and analytics as individual, independent areas is already the first misunderstanding that should be cleared up. This misconception is justified by the fact that the term “analysis is used as a generalized super-category for the general examination of data.

However, data science is specifically a sub-area of data analytics. And of course, both data ask about correlations, causalities and patterns and the findings that can be derived from them.

What a data analyst does

An Analyst deals with well-defined and therefore dedicated data sets. These are visualized, analysed and examined for patterns, errors and peculiarities. This almost always involves historical data. Which website visited how many unique users in which period?

Which products were bought by which demographic groups and when? In what period of time were most of the sensor values measured? Extensive statistics can be obtained from this data and visualizations can be created, for example to depict dependencies and relationships.

Analysts often have extensive knowledge of mathematical statistics. The main competency areas and tools include data bases and their management, SQL as part of them, and statistical programming languages such as R and SAS.

In addition, there is a well-founded specialist knowledge in dealing with large amounts of data, which is required for analyses of big data projects in order to understand data and make it communicable. It is a very application-oriented area of work, which in large parts resembles a job as a consultant.

What a data scientist does.

deals more with the scientific principles of pattern recognition and classification. Often the underlying database is still indifferent and anything but well-defined. Bigdata sets from different areas of investigation are included in the statistical evaluation.

Scientists use regression analyses and classification methods to enable predictions for the future. These predictions are usually not based on analytical methods. Rather, it is based on the statistical analysis of large amounts of data. Scientists combine scientific fundamentals with experience in development and programming.

This is really about processing on a large scale and the scientist will endeavour to automate as much of it as possible so that he can focus on his results.

Congruences between data analytics and data science

The work areas of analytics and science often overlap. For both, the development of data sources, the consolidation and cleaning as well as the integration in tools are essential in order to be able to work validly with the data sets.

Like the analyst, the scientist uses methods of visualization, for example, to map statistical assumptions. Both departments require extensive knowledge in the subject areas examined in order to also grasp recognizable relationships. Both will therefore dig into the fundamentals of the respective work area in order to gain a better understanding of what the data is telling. The statements determined from the data can only be correctly classified with this specialist knowledge.

An occasionally overlooked but very important area of work in both areas is communication within the team and with stakeholders. Data science takes place at the interface between technology and management and must communicate with both levels.

Very few managers really want to understand what a support vector machine or a neural network is and how exactly it works. How reliable the results are and what they mean for the decision-makers, on the other hand, is very important.

Balance between technology and consulting

Finding a good balance between the technical basis and consulting services is often a challenge, especially for scientists due to their mostly technical background. The results must be presented in an argumentative manner or published without placing the technical focus in the foreground.

These tasks are often carried out by Business analysts who, in direct exchange with managers and customers, provide reports and reports. The optimal team structure in data analysis projects therefore unites analysts and scientists in order to set up customer projects in a target-oriented manner, to guarantee a valid analysis and to guarantee successful customer communication.