It is very likely that you have already heard about the importance and value of data. It seems that everyone is talking about Big Data, Data Science or Data Analytics nowadays. All three terms are associated with data, or to be more precise large volumes of it, but you may not be aware of the exact meaning of each term and their respective differences. The terms are indeed related, but they aren’t synonyms.
Big Data vs. Data Science vs. Data Analytics: similarities and differences
Data Science applies to everything related to organization and analysis of data regardless of whether we’re talking about structured or unstructured data. Essentially data science combines different scientific disciplines such as statistics, mathematics, and programming and matches them with a particular set of skills. A data scientist should be always oriented towards problem-solving and look at different ways to analyze data while trying to capture and recognize patterns that aren’t always obvious. The ability to analyze in order to extract as much information as possible from the given data is key here.
Big data, on the other hand, refers specifically to large amounts of data that can’t be processed effectively with traditional applications. Generally speaking, if the amount of data is larger than the amount that can be stored on a single computer, then we’re speaking of big data. Big data is a sort of a buzzword that we use to describe and discuss immense volumes of data, again regardless of whether it is structured or unstructured. Handling big data can be vital for a company and dealing with this data can enable the presentation of information in an orderly and understandable way which in turn can lead to better decision making. Companies that know how to deal with big data are more likely to make the right strategic business moves.
Data Analytics involves analyzing raw data in order to reach the right conclusions that’d lead to the right decisions. It doesn’t involve a lot of manual operators, it is based on algorithms and a variety of tools are used. Basically, it is an application of an algorithmic or mechanical process with the aim to get the right insights.
The application of Big Data, Data Science and Data Analytics
Based on what we’ve said so far, it should be easier for you to understand each term and what it stands for, while also realizing the differences in meaning and application. For example, data science is very effective with internet searches (queries). Search engines use data science algorithms that enable them to deliver the best results for a particular query, often in less than a second. Data science is also used in digital advertising and the algorithms have a tremendous impact here, the displaying of banners and digital billboards is dependent on data science. It is data science that can significantly increase CTRs.
Big Data, on the other hand, is essential in businesses such as banking, private wealth management advisories, as well as insurance companies. In fact, if it has something to do with investment, it will involve big data, mostly because making the right decisions often depends on the need to unravel a massive amount of multi-structured data that coexist in multiple different systems. Nowadays, big data is also used in many sectors and industries, and not just the big companies either. Big Data can mean the difference between profitability and failure of many SMEs.
Data Analytics is widely used by public institutions, mainly in the healthcare sector, but it can be also helpful in the travel industry, the energy management sector, and gaming. Hospitals use Data analytics to measure their effectiveness, patient satisfaction as well as the time that is spent on every patient, which enables them to improve the quality of the care. Data analytics can help you optimize the buyer’s experience via web or mobile. The travel industry gains insights into customers’ preferences. Personalized travel recommendations are delivered thanks to data analytics. Game companies gain insights into what the players like and what they dislike, which enables them to improve the relationships with their clients. In the energy management sector, the application of Data analytics is focused on controlling and monitoring network devices, dispatch crews, as well as managing service outages.
Specific knowledge and skills
The required skill sets and knowledge for each of the three areas also differ. Data Science experts need to be familiar with specific tools such as SAS or R; need to be versed in Python coding language, which is the most common in the world of data science along with others such as Java, Perl, C/C++. Furthermore, they also need to be masters of the SQL language and use tools such as the Hadoop platform and, last but not least, have the know-how when it comes to working with unstructured data.
On the other hand, Big Data demands many personal skills that have to do with analytical ability, and creativity, but also knowledge of disciplines such as mathematics and statistics. Besides, Big Data experts need to have an understanding of the business objectives, as well as the underlying processes that drive the growth of the business, i.e. what it takes for the particular business to be profitable.
The requirements for Data Analysts are similar, they must know programming languages such as R and Python and be skilled in statistics and mathematics.
The requirements for Data Analysts are similar, they must know programming languages such as R and Python and be skilled in statistics and mathematics. Additionally, they should have certain soft skills such as social and communication skills, plus other professional skills such as machine learning skills, data management, and data intuition. Data analysts should have the ability to prevent unwanted situations by being able to visualize and anticipate them, especially when it comes to situations that can have an impact on the future of a company.
Now that you know the differences, which one do you think is most suitable for you - Data Science? Big Data? Or Data Analytics?