Which technology Job Skill is in demand 2021?

Technology has made advances since ancient times and today we have reached a level where everything is possible with just a few clicks.

Collecting data for a particular topic is very important from a commercial point of view. Data collection is an art and uses a lot of modern techniques and is summarized under the topic of “Big Data Analytics”.

Traditionally the word data used to make a person think about numbers on a spreadsheet but today, a huge amount of data (be it numerical or not) can be collected, stored, processed in software, and used when required under Big Data Analytics.

• Data management Losing out on important data can decrease the brand value of a company and even result in loss of customers, properly managing it comes under data management. These management teams set up data policies and invest in a variety of tools to help them handle lump-sum data.
• Data mining This involves digging for the data required in the data collected, exploring and analyzing the data properly using AI and various statistical methods. In data mining, the goal is either classification or prediction. Various algorithms are used involving: classification trees, logistic regression, neural networks, clustering techniques, etc.
• Hadoop This is a tool that helps store different types of data involving structured, semi-structured, and unstructured.
• In-memory analytics This helps reduce query response time as it accesses query data residing in the computer’s RAM rather than physical disks.

Analysts, Data Scientists, statisticians, Quantitative Analysts, and Data miners are the few people in the field who collect the huge amount of data that is required by the organization they work for.

The raw data collected is processed, cleaned, and analyzed. The analysis part of the work is done using certain technologies there are two classes’ namely operational big data technology and Analytical big data technology.

These are complementary technologies and both are required to develop a complete big data solution.

• DESCRIPTIVE (BACKWARD LOOKING) This summarizes PAST data. This data is of the least value but may help come up with new suggestions that won't repeat the same mistakes of the past.
• DIAGNOSTIC (BACKWARD LOOKING) This is done to understand why a particular problem was caused. Thus, helps in determining the cause of the problem.
• PREDICTIVE (FORWARD-LOOKING) This helps collect data from the past and present to predict the future of a particular organization.
• PRESCRIPTIVE (BACKWARD LOOKING) This helps produce data to give a solution to a problem created.
• CONSUMPTION (BACKWARD AND FORWARD-LOOKING) This helps collect data that gives us the outcome of the product produced.

There are various tools used today to organize the data generated out of which the best 5 are listed below:

• ZOHO Analytics
• Splunk
• MongoDB
• Spark
• Xpenty

This technology is applied to many areas of work and research as it benefits the user in many different ways.

Big data helps realize that there is more data to look at and to work with and more can be done; this helps the company to come up with various new products.

Big Data technologies help organize data sets and gather new information. Any organization can use these techniques and improve its business.

Big Data Analytics helps organization collect their data and use it to identify new opportunities. The collected data reveals a lot about the company, the market, and the consumers and helps improve the production process of a company.

Business analytics is another term (technology) used in today’s century that defines storage and usage of data to expand the business. It uses predictive analytics with variable inputs to test projects and make decisions.

To say that Big Data Analytics has a huge impact on Business Analytics is an understatement.

Big Data in healthcare has the potential to avoid preventable diseases, predict outbreaks of epidemics, improve the quality of life in general, and reduce costs of treatment.

In medicine & healthcare, big data analytics covers the analysis and integration of large amounts of complex heterogeneous data such as biomedical data, electronic health records data, and omics data.

The data listed above are difficult to manage with traditional software; the application of big data provides the required knowledge from the available huge amount of data.

Application of BIOINFORMATICS with big data helps analyze data related to structural abnormalities more efficiently. Many popular areas of medicine are under research where the concept of big data is currently being applied, some of which are Image processing, Signal processing, genomics, etc.

Apart from the above two Big data analytics can be applied in various fields like Agriculture, Telecom, Banking, entertainment and media, Education, Government, Insurance, Wholesale, and many more, the list is never-ending.

Lastly, dealing with hundreds of Gigabytes of data is no joke and seems impossible to handle manually, that is where this technology comes into play and helps to manage such a massive amount of data with a few clicks which is thus proved.