Business Analytics and Business Intelligence are the most-heard terms in the global corporate environment lately. It refers to utilizing advanced technological tools to gain business insights and enhance performance. The predictive analytic methods with Big Data are becoming so prevalent in every industry.
Big Data is believed to be here to stay. The hype we see about it is not temporary. The fundamental technological change it applies to the universal business landscape is creating a root-level revolution just as what computers did when they first arrived to our offices. With abundance of easily available and cheap information or data to support critical decision-making, Big Data is now playing a core part of the daily operations of the corporate, including the world’s most renowned brands such as Facebook, Google, and Amazon etc. Even smaller companies are now realizing the importance of Big Data for business.
Usage of available performance data to make business decisions is not a new concept, but has been there in practice for decades, though it had different forms. The major difference now that it has become more technology-intensive is that one can gather much more in-depth information about each and every element that is relevant to decision-making. Thanks to technology, the cost of data collection, storage, and processing is becoming cheaper day by day.
An online retailer can now easily collect diverse range of data needed for analytics including:
Adding up multitude of data from all available resources, the size of the data-set to be recorded grows quickly in every second as more relevant features are gathered repeatedly over time.
Conventionally, the process of business analytics can be classified into:
Descriptive and predictive analytics methods require a human expert as an analytics manager who can assess and interpret the results, whereas prescriptive analytics will automate the decision-making process too.
As we have seen, traditional business analytics used data more descriptively. If you own a bakeshop, you always took the stocking decisions from the wholesale provider well in advance. For this, you would have been using historical sales data to observe the trends as ‘higher sales during year end’ to make a time-series forecast of future product demands.
Now, with the future prescriptive analytics approach, you can even perform an analytics on the available dataset to compute the order quantity of a particular product, which may optimize estimated revenue for a particular time period. It is not just limited to product-centric businesses, but a myriad of industries such as Information Technology, logistics, mechanical engineering, mining also make use of prescriptive analytics nowadays.
Adding to it, in the new age of Big Data analytics, your scope of data collection is not limited to just historical sales and demographic data, but you can also access data from various other avenues to supplement to the demand. With Big Data tools, the future is not uncertain, and becomes as simple as predicting the future of an apple falling off the tree. By recording a few key elements such as the velocity of the fall, weight of the object, and the current location, predicting the precise location of the falling apple two seconds later is very much possible. This is made easy by keeping the system simple.
Predicting the future demand for a product or the spread of a malicious virus is much more complex as the underlying relationship aspects of the present and the future may still remain unknown for some systems. However, at the base level, future can always be seen as the function of present, and gathering as much relevant information as possible about the present can help better resolve the future. This is where Big Data plays vital with its ‘4V’ power of:
While doing the volume task of comprehensive analytics, traditional analytics tools may fail due to the enormous size of data sets with the need for larger memory and slower computational speed. If the worse case, it is also possible that artificial relationships between the unknown features can be interpreted, and the prediction about future can be an overconfident one (also known as over-fitting).
In order to extract real value from Big Data analytics, you have to perform all the descriptive, predictive, and prescriptive analytics. Descriptive and predictive analytics with Big Data is becoming more prevalent in various industries lately. Some examples include:
Various such predictive algorithms are coming out, and they are generically referred to as machine learning. These algorithms analyze the relationship between past, present, and future in light of the data metrics.
Descriptive and predictive analytics have been effectively deployed by making use of huge datasets to get optimum results. But prescriptive analytics, despite the help of technology tools like Big Data, is still at an early phase of development, and is mostly confined to the field of academic research.
With so many advantages being packed with it, how can you implement Big Data into your daily business operations? To implement it, the primary need is to do an assessment of your current analytical capabilities. If you only have very limited data collection and analytic tools, no need to worry as it is the situation with many of the organizations. You can still make use of small data analytics to generate a great value.
Shifting from small data to Big Data analytics or to advanced prescriptive analytics is much thicker to get through. This process requires highly skilled data scientists. A McKinsey report estimates that there will be shortage of about 180000 experts with a deeper analytical experience by year 2018, and about 1.5 million data-literate executives and managers in the United States. The technology giants as well as large investment banks will be intensively head hunting for skilled professionals, and very soon data scientist will be the most sought after job of the century.
To describe it in short, the more big data you get hold of, the more you can learn in depth about your business process in hand. Altogether, when it comes to business analytics, it is focused keenly on one core metric such as ‘finances’. You can also find this point in majority of the online debt reviews as to how failure of proper business intelligence led to poor business performance, huge debts, and even bankruptcy.
The two other major analytics contributing to your overall business analytics include:
The primary difference of Big Data from business analytics is that it involves machine processing more than the involvement of a human being. There are hundreds of cheap analytics tools and software packages available to users. Some are DataWrangler, Google Refine, Google Fusion Tables, Tableau Public, OpenHeatMap etc.
The primary principle when it comes to Big Data and data analysis is that the more you come in tune with your data in hand, the better insight you can get about your business problems, and find solutions for instant fix. Implementation of Big Data and devising a business analytics strategy are important business decisions to make, which should be always backed up with intensive research to identify data flows and follow data gathering and processing practices dedicatedly. If done relevantly, your data itself can work to your best benefits over a long run.
Author Bio: Barrack Diego is a data analytic specialist who is also working as a Big Data implementation consultant for many major corporate. Alongside, he is a financial expert too, providing services to a leading debt consolidation company while writing articles on many blogs and websites.