Is big data and data analytics the same?

Difference between Big Data and Data Analytics

In this modern era, a tremendous amount of data is being generated and processed every second. With this data, diverse platforms have been created in this landscape, which includes Big data, Data analytics, Data science and many more. Data is the biggest asset of any business organisation in today?s world. According to Forbes- ” The total data market is expected to nearly triple in size, growing from US$69.6 billion in revenue in 2015 to US$ 211.3 billion in 2020.

With an introduction of new technologies and application in the digital economy, many organisation have opened up in Big data landscape. Data science, Data analytics, Data mining, Data engineering etc, all fall under the same category and work together in the same platform. Most of the people quite often interchange these terms but there are very large differences among them. A similar type of ambiguity exist with the term Big Data and Data Analytics.

Data Analytics

Data analytics refer to the systematic process of examining data in order to draw conclusions from the information those data contains. Data analytics commonly refers to an assortment of application and technology, from Business intelligence (BI), reporting and online analytical processing (OLAP) to advance analytics. Data analytics is almost similar to Business Intelligence, which is one of the major forms of analysing data.

Data analytics can help to increase the revenue, improve operational efficiency, responding to customers from time to time and providing better services to them, and gain a strong side in this competitive era. At the application level, Business intelligence and reporting provide business organisations with useful conclusions on key performance indicators, business operations, strong and weak points in their management.

Data analytics includes data mining and data optimisation to draw the behaviour of the customers. Data mining involves sorting different data sets to draw useful patterns and relationships, predictive analytics which predict the behaviour of the customers, Machine Learning and Artificial Intelligence which generate algorithms to collect useful information from the data sets. Big Data analytics involves data mining, predictive analytics to conclude better results from structured and unstructured data.

Data analytics application involves analysing more than one form of data. In advance projects, it takes much more efforts in collecting, integrating and preparing data and then developing, testing, and analysing models to ensure that they produce the best results. Data analyst works in business intelligence which focuses more on model creation and other important tasks such as setting meeting, making customer calls etc.

Big Data

Big Data is a field that deals with systematically extracting the information from the large and complex, structured and unstructured data which are difficult to solve using normal data processing software. Big data is generally associated with three main V’s- volume, variety and velocity. When there are large structured and unstructured data, we are primarily concerned with observing and tracking those data.

In the current scenario, Big data uses predictive analytics, user behaviour analytics and other typical analytics methods to extract information from data. This analysis of data can be find new development in medicine, science, technology and so on.

It is not easy to process bid data using the traditional methods of data analytics. Therefore, specialised modelling techniques are being used to process these unstructured data and extract useful information required by the organisations. Big data help to solve these unsolved and unpredicted problems, revelling the unknown information and strategy behind customer needs and requirements.

Application of Big Data:

1. Manufacturing- According to recent global studies conducted by TCS, improvement in supply planning and product quality provides the real benefits of big data in the manufacturing sector. Big data provides a platform for transparency in the manufacturing industry which shows the availability and growth in performances. Thus, big data acts as an input of predictive tools and preventive strategies in Health Management.
2. International development- Various researches on Information and Communication technologies suggests that big data can make an important contribution to international development. Recent advancements of big data offer different opportunities in the field of health care, cyber, security, crimes, economic productivity, natural disaster etc. Additionally, user-generated data offers new discoveries which are lacking behind due to some reasons. However, there are still some problems which are yet to be covered by big data such as privacy issues, imperfect methodology and interoperability issues.
3. Medicine and health research- Big data in health research is continuously striving to increase its results in the field of biomedical research, as data-driven analytics is moving forward than hypothesis-driven research. This trend generated by big data can be tested in clinical researches and traditional follow-up biological researches.
4. Information Technology- Big data has help business operations as a tool to help the business employee to wok more rapidly and efficiently, collecting and distributing the resources in IT. By applying the principles of big data and machine learning and deep learning, IT departments can easily trace potential issues and move to provide a more accurate solution before the problem arise.
5. Insurance- Health insurance providers are collecting data on different useful topics on social Determinants of health such as food and TV consumption, clothing size, purchasing habits where they can predict the cost and revenue, in order to spot health issues of their clients. It is difficult to say whether this information is being used for pricing purpose or not!

Application of Data analytics:

1. Management of Energy- Business organisation uses data analytics for energy management such as energy optimisation, automated machines for utility, smart grid energy, energy distribution etc. the primary concern is to manage and monitor network devices, crew members and services for the customers.
2. Healthcare- Data analytics helps hospitals to improve the quality and standard of treatment by providing proper instruction and resources to them. Machine and medical instruments data are using data optimisation for patient tracking treatment, patient flow and medical instruments use.
3. ?Logistics- Data analytics utilises data for tracking the product from one place to another. Data analytics helps to find the lost parcels very easily.
4. Travels- Data analytics optimises buying preferences of the customers via blogs, data analysis, social media. Customers preferences and desires can modify the existing sales and revenue of the organisation.
5. Gaming- Data analytics collects data to optimise the behaviour and nature of the games. This helps the gaming companies to get a good sight of likes and dislikes of the customers and enhance user’s relationship.

What is the main difference between Big data and Data analytics

Data analytics draw conclusions which are easily understood by the people. Big data needs an expert to draw and understand the conclusions. Data analytics does not create a huge pile of unstructured data whereas Big data create a huge pile of unstructured data since the data available to Big data are very huge and take more time to process and draw conclusions.

Data analysts have a clear goal in their mind and therefore they look only through specific data to gain support for their problems. On the other hand, Big data is a collection of a huge pile of data that requires lots of understanding and techniques to draw better conclusions from the raw data.

Data analytics uses predictive, statistical and mathematical modelling to display the results which are very easy to understand and draw the conclusion from the available data. Big data uses complex technological tools and techniques like parallel computing and other automation tools to solve the complexities of Big data and thus it is quite difficult to understand and draw the conclusions from the raw data.