In the last couple of years, we have seen diverse technological innovations. From the Internet of things to cloud computing, AI, robotics, you name it. Technology has taken the center stage for guaranteed business scalability and sustenance. But one thing is intricately generated with the technological evolution – data. Forbes reported that 90% of the data today was generated in the last two years. While that may sound interesting, this raw data is practically useless. Raw data can be likened to untapped gold buried miles down the ground. Yes, it holds unimaginable potential. However, when left as is, It is as good as nothing. The same thing applies to data. Its extraordinary potential remains hidden until the said data is analyzed. 

Looking critically, analytics is an innate behavior of humans and it sweeps across various industries. We decide whether to buy a product based on factors such as price, customer service, seller’s track record, etc. We decide whether to travel by air, land or the sea, based on the time we have at our disposal, comfort, cost, the distance of the journey, safety records, etc. In fact, in ‘mundane’ things such as choosing our partners, we weigh our options before making a decision. How does this individual fit into your life? What do I like and do not like about this individual? Do we have aligned goals and a value system? You get the point.

What differentiates these actions and business analytics is that the data is on a larger scale with lots of features. This time, there are numerous factors to consider in the data and it is humanly impossible to get insight by just staring at it. State-of-the-art data analytics are widely considered in such cases. 

Speaking of data analytics, it is sometimes mistaken for business analytics. Some people use both terms interchangeably. In the real sense, however, they are not exactly the same thing. In business analytics, the data is based on a business’s past records, and the analytics is done from a business standpoint. 

Business analytics has proven to be immensely valuable to businesses that care to grow. Right now, about 90% of both big and small businesses have set up a business analytics team aimed at leveraging past records to make the best possible decisions for their business. This is what makes business analytics a lucrative career path. You can become a business analyst by enrolling in a project-based business analytics certification course. 

So what is Business Analytics?

According to Harvard Business School, Business Analytics is the process of using quantitative approaches to make sense of data in a bid to make informed business decisions. These decisions can be seemingly small but they play a large role in increasing business sales, and in return revenue. For example, business analytics can reveal that changing the position of the call to action on the webpage can increase revenue by 20%. Or that reducing the price point of a product would not necessarily lead to more profit. 

Types of Business Analytics?

Business Analytics can come in different shapes and forms. Experts have broadly classified the types of business and into four: 

  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics

Descriptive Analytics Explained

Descriptive analytics, as the name implies, tries to describe the data. It looks at past records and tries to understand them. This can be done by aggregating the data based on some features and expressing the data in a more succinct way. Descriptive analytics is the foundational type of business analytics as it gives an intuition into the timeline of the business and the commensurate results produced. Amongst other things, the results guide the business analyst, investors, sales managers, and other stakeholders on the right steps to take next. 

It also gives insight into the strategies that are working best for the business and the strategies that do not. For instance, descriptive analytics may reveal that, in the past 5 years, sales between June and September are significantly higher than sales between December and March. Thus, the business owners may infer that the weather has a toll on their sales as Summertime results in higher sales than during the Winter period. 

Diagnostic Analytics Explained

Diagnostic analytics shift focus from past events and gravitates towards the current happenings. It involves scrutinizing recent decisions to find out what may occur using statistics and probability theories. Diagnostics analytics also looks to discover the root cause of events and what may happen if a certain action is taken. Some of the algorithms used to achieve this for both classification and regression problems are sensitivity analysis, training algorithm, etc

A practical example of diagnostic analytics would involve a situation where a business owner wants to determine whether selling his product to a new audience has yielded appreciable results.  

Predictive Analytics Explained

This involves the use of machine learning techniques to predict future outcomes based on historical data. It builds upon the information from descriptive analytics to wrangle data, clean it and transform it to the best possible state for the machine learning models. Machine learning has grown in leaps and bounds over the years, penetrating virtually every sector of human endeavor. In business, it can be used to predict the most suitable audience for an ad, the best price point for a product, or the estimated growth for a business year. 

Another practical example of predictive analytics is sentiment analysis. With past data, businesses are able to understand customer behavior, the kind of product they will like, and the best way to upsell them. Sentiment analysis is a crucial part of business analytics. A business owner can know that showing this ad to this audience would have positive, neutral, or negative feedback. 

Prescriptive Analytics Explained

Prescriptive analytics builds on predictive analysis to give recommendations if a course of action is taken. It utilizes the feedback from predictive analytics to check the possible outcomes given different conditions and suggest the best ones based on your KPIs. Predictive analytics is hinged on two things:

  • A robust feedback system and 
  • Constant iteration. 

The system must understand what would be the outcome of an event given a condition and also attempts to do this again and again for different conditions. This is what forms the basis for a recommendation. Recommendation systems have become widely popular over the years due to their incredible results. Big companies such as YouTube use it to recommend the next video to watch from the watch history. Amazon uses it to recommend complementary products when you make a purchase. Facebook uses it to recommend a possible friend. Netflix uses it to recommend the next movie you’d love. The list is endless. 

Wrapping up

Over the years, each of these types of business analytics has proven to be extremely useful for a given scenario. To have practical experience on how these apply in real business settings, you can join a business analytics internship where you will learn from industry experts and work with real-time projects.