Satish Rai is an award winning Technology Executive with more than 18 years of industry experience in ERP/Application and IT infrastructure management in Orthopedic/Bio-Tech/Medical/Power Sector. Satish is presently working as IT Head at Smith and Nephew, India.

"Information is the oil of the 21st century, and analytics is the combustion engine.” (Peter Sondergaard, Gartner Group)

Traditional Data Capturing vs BIG Data
There were times when gathering data itself was a tremendous task. Times have changed rather drastically in the last 10 years or so. Now, there are multiple ways to capture data, be it through traditional input devices such as a keyboard, a desktop mouse or through the latest technology such as smartphones, PDAs, Optical Reading, RFIDs and Optical Character Recognition.

Today, we are at the stage where we are discussing the Internet of Things (IOT). IOT is straight forward; simply put, it is about connecting devices over the internet, letting them talk to us, applications, and each other. Where it is most common is home heating and energy use. They have clever functions that allow you to turn up the air conditioning remotely if it is too warm or similarly turn up the heating if it is too cold.

There are over 7.5 billion people on Earth and the number of internet connected devices far outreaches this number. This is a clear indication of the volume of data being generated every second. Globally, we generate 2.5 quintillion bytes of data everyday – so much that 90 percent of data in the world today has been generated in the last two years alone (IBM Report). Here, the process of Big Data analytics is introduced. Big Data analytics is the examining of large unstructured data sets containing a variety of data types e.g. blogs, social media feeds etc. and is often used to uncover unknown correlations, market trends, customer preferences and other useful business information.

What is the potential?
The concept of Big Data analytics is not a new one, where before businesses gathered information and analyzed the data for future decisions, businesses can now identify the patterns and insights for immediate decisions. Real-time information is available at just a click away and allows businesses to have the ability to stay agile and give them a competitive edge. At our disposal, we can gain access to information such as an individual’s profession and age that are full of key insights once analytics are applied. Similarly, different household devices and transport vehicles can also provide details on date of purchase, last service dates or breakdown records to name just a few. All this information can be used by businesses to define their strategies and marketing plans and from this they can mobilize, allocate and deploy resources accordingly.

MIS and Big Data
With advancements in data being generated and captured, the role of MIS is becoming more and more vital. Traditionally, MIS scope was limited to gathering data from ERP, CRM, Financial System and then presented in a usable format for business
users, managers and decision makers. This then in turn allows those key stakeholders to make decisions on course correction and how to improve effectiveness.

To make use of unstructured Big Data, analytics need to be applied. The data needs to be well-governed before it can be reliably analysed. With Big Data constantly flowing in and flowing out of businesses in real-time, or close to, it is essential that decision makers are presented with the information in a clear format in order to make quick,
effective decisions.

MIS and Big Data can contribute significantly towards decision making. In today’s environment, most organisations rely on data and information coming out of MIS to make strategies and marketing plans, hence having a robust MIS capable of capturing data and processing is critical.

In order for MIS to be effective, it is important to establish a system and put repeatable processes in place to build and maintain standards for data quality. An essential component of this is to have people with domain knowledge part of a core group responsible for defining and managing MIS requirements.

The figure below indicates the nature of decisions being taken at different management levels and the level of information required for making these decisions.