Big data is growing every day and becoming a very popular word in the tech world. Many people around us keep talking about it, but do they know what it actually means?

Big data is nothing but the collection of unstructured data. This data is not in a particular format and because of this its datasets sizes are generally huge — measuring tens of terabytes — and sometimes crossing the threshold of petabytes. The term big data was preceded by very large databases (VLDBs) which were managed using database management systems (DBMS).

Having so much of data pertaining to the business provides a very niche way of increasing the sales or profits of any company. But in order to do so, we need to make use of Big data analytics. So what is Big data analytics?

Big data analytics is the process of examining large and varied data sets — i.e., big data — to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Driven by specialized analytics systems and software, big data analytics can point the way to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.

Here are the top 10 sectors that could benefit the most if they make use of Big data analytics:

  1. Banking and Securities: For monitoring financial markets through network activity monitors and natural language processors to reduce fraudulent transactions. Exchange Commissions or Trading Commissions are using big data analytics to ensure that no illegal trading happens by monitoring the stock market.
  2. Communications and Media: For real-time reportage of events around the globe on several platforms (mobile, web and TV), simultaneously. The music industry, a segment of media, is using big data to keep an eye on the latest trends which are ultimately used by autotuning software to generate catchy tunes.
  3. Sports: To understand the patterns of viewership of different events in specific regions and also monitor the performance of individual players and teams by analysis. Sporting events like Cricket world cup, FIFA world cup and Wimbledon make special use of big data analytics.
  4. Healthcare: To collect public health data for faster responses to individual health problems and identify the global spread of new virus strains such as Ebola. Health Ministries of different countries incorporate big data analytic tools to make proper use of data collected after Census and surveys.
  5. Education: To update and upgrade prescribed literature for a variety of fields which are witnessing rapid development. Universities across the world are using it to monitor and track the performance of their students and faculties and map the interest of students in different subjects via attendance.
  6. Manufacturing: To increase productivity by using big data to enhance supply chain management. Manufacturing companies use these analytical tools to ensure that are allocating the resources of production in an optimum manner which yields the maximum benefit.
  7. Insurance: For everything from developing new products to handling claims through predictive analytics. Insurance companies use business big data to keep a track of the scheme of policy which is the most in demand and is generating the most revenue.
  8. Consumer Trade: To predict and manage staffing and inventory requirements. Consumer trading companies are using it to grow their trade by providing loyalty cards and keeping a track of them.
  9. Transportation: For better route planning, traffic monitoring and management, and logistics. This is mainly incorporated by governments to avoid congestion of traffic in a single place.
  10. Energy: By introducing smart meters to reduce electrical leakages and help users to manage their energy usage. Load dispatch centres are using big data analysis to monitor the load patterns and discern the differences between the trends of energy consumption based on different parameters and as a way to incorporate daylight savings.

Early big data systems were mostly deployed on-premises, particularly in large organizations that were collecting, organizing and analyzing massive amounts of data. But cloud platform vendors, such as Amazon Web Services (AWS) and Microsoft, have made it easier to set up and manage Hadoop clusters in the cloud. And slowly many companies have started moving towards big data analytics to make use of the abundance of data they already have but failed to make sense from it.