With digital transformation and the arrival of new technologies, specific terms emerge that might be confusing. This is particularly true when these terms also describe new concepts or processes that are not yet conceived as realities or are not understood based on their potential applications. Therefore, in ‘Data Science vs. Big Data vs. Data Analytics’ we explain the differences between these three concepts, the applications for each of them and how they are connected.
What is Big Data?
The starting point to find the differences between Data Science vs. Big Data vs. Data Analytics is defining the term ‘Big Data’.
It consists of a dataset or combinations of datasets that are large (volume), complex (variability) and have a specific growth rate (speed), and are generated in a specific context (an organization, a city, etc.) and obtained from different sources (for example, sensors installed on a system or users connected to a network).
This data can, in turn, take the form of:
- Structured data: data that contains information that allows for its organization, comprehension and classification.
- Unstructured data: data that does not contain this information, or only does so partially. This includes data generated by browsing websites or social networks.
A set of information is considered to be Big Data when it is not possible to analyze it or process it via conventional computer technologies or tools. There is no specific measurement or amount of data after which a set is considered to be Big Data: however, most analysts and professionals refer to datasets that range from 30-50 Terabytes to several Petabytes.
In any case, it is the quality and analysis of this data and its value for a company (and not the amount of data) that will make the difference in the following analysis stages and implementation of transformations. The integration, management and real-time analysis of all data is the true value of Big Data.
What is Data Analytics?
The discipline known as Data Analytics is related to the processes dedicated to using software to discover trends, correlational patterns or other useful ideas and conclusions in Big Data.
The objective of Data Analytics processes is to translate that data into relevant and actionable information for the institution that has obtained the data.
This discipline helps companies and other organizations that look to understand what data has to say and how that information can improve their processes. In this regard, the know-how of an analytics expert should include knowing the industry where the expert works as an analyst. In this way, this person can also offer valuable new perspectives: main trends, challenges that might arise or how the competition is addressing this matters.
Through the use of data, Data Analytics professionals can access conclusions that improve or add value to the processes of a business or institution, in addition to preventing possible future scenarios based on current trends.
What is Data Science?
Data Science could be considered to be the science and all existing methodologies focused on the study of data that allow for the generation of more effective models for the management and analysis of different data sources. A Data Science expert is also tasked with implementing these models in a way that the institutions can make the most out of the data that they have collected.
This being the case, Data Science specialists must apply a combination of disciplines that range from statistics, mathematics and computing to programming.
Data Science processes also involve disciplines such as Machine Learning or Deep Learning. This implies the ability to generate systems that are increasingly efficient, that “learn” and are capable of making data-based decisions. This is the driver of the application of Machine Learning in various industries.
Data Science vs. Big Data vs. Data Analytics: their applications
The collection of Data Science vs. Big Data vs. Data Analytics skills allow data professionals (data scientists) to extract value out of Big Data in the shape of trends or opportunities hidden inside the information.
The arrival of Operational Intelligence and Business Intelligence have implied a competitive advantage for those companies and institutions that are already applying information and data to their strategic decisions.
The use of these new disciplines is making a difference in several sectors:
Data Science, search engines and digital advertising
Data Science is making a difference in the ability of search engines and aggregators in finding increasingly accurate results that are suited to the user.
To do this, major market players (spearheaded by Google) are working to find algorithms and models that are more effective. One of their many objectives includes improving the understanding of the language used by users to make searches.
In addition, the advertisement and digital marketing sector are linked to this trend through disciplines such as SEO and SEM.
Big Data and investments & insurances
In an eye-opening step in regard to future trends, major investment companies (such as JPMorgan and BlackRock) have established research centers to make the most out of Artificial Intelligence and Big Data.
These research laboratories study the ability of Big Data to completely change the paradigm in relation to the way in which investment companies worked up to this day. The goal is for machines to be able to predict the stock market behaviour, using different datasets as macro or balance sheet indicators.
Furthermore, the world of insurances will also be completely transformed, being capable of, for example, offering more personalized policies to their clients.
Data analytics and industry
Industry 4.0 is now a reality: an industry that is capable of leveraging the potential of disciplines as Data Analytics to boost the productivity, security and sustainability of its processes.
Through the use of platforms such as Nexus Integra, it is possible to generate a global, large-scale industrial asset operations and monitoring environment.
Systems such as Industrial IoT are already a reality that is transforming the way in which the industry produces and is organized.
Do you want to know more? At Nexus Integra we help industrial companies complete their digital transformation and use the potential of data to generate processes that are more efficient. Get in touch with us and we’ll tell you how.
