Today every business accommodates a business analytics team, that specializes in drawing inferences from available data or data not readily available, needs to be mined out, and present it in a meaningful way to the stakeholders, for them to take an informed decision. For every business to succeed and stay competitive in its domain, there is a need for continuous improvement and increase efficiency in business processes to cut corners and streamline the business. Business analytics does not only show inefficiencies in business it also can show business opportunities in the marketplace, thus paving way for innovation and evolution of the business.
So what is Business Analytics? As per a popular definition from authors Michael J Beller and Alan Barnett, “Business analytics refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning”. If we study this definition and relate it to the present practices, the only things that might have been added are, one, the real-time analysis available on leading analytical software and two, the predictive analytics that is a standard feature in present-day analytics software. In the end, business analytics is measuring the performance to make improvements to the bottom line of the business.
Business Analytics started getting its attention with the explosion of data and evolution of data processing, data mining methods, and affordability of data processing software. Also, an initial contributor can be the rapid development of storage technology (both hardware and software), allowing writing, reading at unprecedented speeds also taking up less space. In a way, the evolution of business analytics has been an organic one, growing in importance as the underlying technology improved and the complexity of business and associated decision making got complicated.
Lets delve into the types of Business Analytics. Primarily there are 4 types.
The first generation of business analytics was based on studying historic data and drawing inferences about the performance of the business. This is exactly what descriptive analytics does with the available data. Summarizing data into a few key metrics give a reasonable understanding of how well or not well a business is doing.
This type of analytics is the next organic step after descriptive Analytics. It focuses on the how and why of past business performances and aims to take learning from this historic data to correct course. A root cause analysis might also be initiated using various industry-standard practices.
An advanced technique that combines historic data, statistical models, and machine learning, to arrive at a most probabilistic future performance given that the business ecosystem stays pretty much the same. Predictive analytics is a great way to understand how the business performs if certain business inputs are tweaked and then work towards optimizing those business input parameters.
A step ahead of predictive analytics is the ability of the analytical system to be able to suggest the optimal solution, in other words, a recommendation system that puts forth the best course of action after optimizing the chances of a beneficial outcome.
Business analytics is a much-evolved domain today with it attaining the status of science. Also, a term used interchangeably with Data Science, business analytics has scope in every field that aims to improve in every aspect as long as there is data to measure. With the proliferation of IoT and the data explosion that it will bring about, the standard methods applied in business analytics to sift through tons of data to see what’s happening behind this curtain of numbers and to make sense of all of it, to make better decisions will be the game changer for many businesses in the future.
So in today’s world, a team that studies the data that a business churns out day in and day out and to step back and look at the larger picture is very crucial for the long-term survival of businesses.
Business Analytics can help financial organizations to optimize budgeting, determine creditworthiness in case of a loan, and also suggest the chances of a customer defaulting on a loan. In a business that grapples with fraud, Business Analytics plays an important role in raising red flags in time.
Business Analytics helps in extracting crucial information hidden behind the credit and debit transactions and lets the business know, the spending habits, lifestyle preferences, and financial standing, raising red flags wherever there is a probability of loss of business. Credit card companies can decide on the fly which customers they can extend a line of credit to and by how much.
Business analytics built into today’s CRM systems, enable businesses to gain deep insights into demographics, socio-economic information and lifestyle of their customer groups and what would be the best fit strategy to retain and increase the customer base.
The manufacturing business is typically a low margin, highly volatile business. It becomes crucial to stay on top of things, so you cover enough to stay protected against equipment downtime, delays in raw material supply, the inventory levels to maintain, and the maintenance expense of machines among others. Business analytics helps you decide on the optimum levels of inventory to maintain and how much to make up for equipment downtime and keep production at optimum levels and much more. Also, business analytics also encompasses continuous improvement, identifying corners to cut and helping streamline the business and nimble.
Business analytics plays an important role in determining the effectiveness of marketing campaigns by generating insights on which kind of campaign is most effective and which one is most penetrative in the market. How much each type of campaign should be invested in to gain maximum benefits and cut losses.
Today the e-retailing business is expanding like never before with more and more people preferring to order online than visit brick-and-mortar stores with covid pandemic attenuating it further. There are many players in the market and it becomes necessary for the e-retailer to keep a hawks eye on inventories to maintain with suppliers and keep the pricing competitive while cutting losses. Business analytics and more recently Data Science comes to the rescue. The better a business applies its strategy basis the outcomes of Business Analytics the better it will fare in the market.
There are business analytics software solutions customized to markets it serves. In many cases business analytics software comes bundled with whole systems like CRM and Financial management systems.
A software suite that offers predictive analytics via machine learning models, bundled with reporting and data analysis tools. Being mobile has become a necessity and software in this domain are no exceptions. There is a mobile-friendly version of the software.
A business intelligence tool that offers high speed and dynamic dashboard and data analysis helping the business catch trends, identify new opportunities, and more. The ability to read any time of data source, be it structured or semi-structured, locally stored or cloud-based is an important characteristic of today’s analytical software programs.
Business Analytics is a complex field where along with core business analytical skills like critical thinking, problem-solving, an inquisitive mind, and good communication, you also need to have technical skills in SQL, Statistical modeling, Statistical languages like R, Programming skills in a general-purpose language, and statistical software.
Business Analytics will be a specialized domain in the next decade. The analytically inclined will do well to shore up their technical skills listed above to continue to be relevant in the market place.
If you are interested in making it big in the world of data and evolve as a Future Leader, you may consider our Integrated Program in Business Analytics, a 10-month online program, in collaboration with IIM Indore!