New-age methods such as big data and analytics are in vogue as global companies are making a beeline to adopt these concepts to further enhance their business strategies. MM International explains its essence here.
The advanced analytics market isn’t just growing; it is accelerating, according to the latest report by Market Research Future. The report further states that growing enterprise data is one major factor driving the growth of the global advanced analytics market. Big data and analytics are often linked together but what do these terms actually mean? Let’s break them down to get a better understanding.
As the name suggests, ‘Big data’ is all about large volumes of data collected by a company from internal as well as external sources. This data can include information on customer contacts, market research, social media, supplier information, etc and is present in both forms – structured (eg. spreadsheets and data from machine sensors, and so on) and unstructured (eg. e-mail messages, blogs, videos, photos, audio files, presentations, webpages, etc).
This massive amount of data can enable one to understand customer behaviour, brand performance, market development and significantly create value for the company as well the customer but how does one derive at these parameters from the ever-increasing data? The answer is analytics.
Analytics is the use of algorithms and statistics in order to derive meaning from the data. This procedure also helps one to predict future reactions of the customer or the market depending on the business objective set by the firm. This forecast takes place based on the past and current data which enable companies to undertake better business decisions.
Analytics can be segregated into two main types – Predictive and Prescriptive. In predictive analytics, as mentioned earlier, the company is able to predict the future happenings in a given scenario whereas; prescriptive analytics guides workers to perform their tasks more efficiently.
Big data and analytics
The USA-based firm, McKinsey implemented ‘big data and analytics’ in its property and casualty insurance sector. It is interesting to note that from 2009-2012, promotional spend in this industry witnessed a rise of 62 %. However, McKinsey did not invest in any promotional spend but still managed to increase demand generation across all its channels by 15 % simply by trusting and following the ‘big data and analytics’ model. Another highlight of this case is that the firm achieved its goal with 11 % fewer head counts and about 5 million dollar less of market and data costs.
There are many other companies that have benefitted by incorporating big data and analytics into their business set-ups. Technologies are evolving and so are businesses, it’s high time that both of them work together to achieve significant business results.
So, when are you adopting big data and analytics into your business?