Business Intelligence And Business Analytics The New Buzz Words - Henry Harvin

In an earlier blog I had written about AI and machine Learning. A similar connection lies between Business Intelligence and Business Analytics. What is this connection, it is the fact that within the domain of Business Intelligence lies Business Analytics.

So let us first understand what is Business Intelligence? Its importance in today’s world and the impact it has on the world at large and in the world of Business more specifically. Increasingly top flight companies are using these technologies to spearhead their growth in new areas while maintaining their dominance in their original operating arenas. 

From the point of Business the very term “Business Intelligence” really means taking data generated, looking at that data and understanding trends. Then, on the basis of that companies would take/ make sound strategic decisions. In short Business Intelligence is not a crystal ball that will tell a company what and when to do it. 

As an example: If you are a component manufacturer with 5 plants in a city like Bangalore spread over a 50 km radius, then you would use Business Intelligence to figure out which plant is behind schedule for delivery of components and whether the delay is of a particular component or is it a delay due to logistics of shipping the components. But this is as I said earlier just an example. However, there are other uses for Business Intelligence which we will examine further. 

One of the growth areas of Business Intelligence:  


  1. Business Intelligence helps marketing teams to look at raw data and understand it better. This is important for marketing teams, because they can then plan on when to launch a new product and at what time of the year the launch should happen. 
  2. Business Intelligence also helps in deciding in which geographic locations your product would have a greater success rate. It also helps in understanding customer trends and behaviour
  3. Business Intelligence helps in predictive analysis, for example, take Flipkart one of India’s leading e-retail platforms. When a customer comes on to the Flipkart platform, he /she may browse several pages over a period of time. If he customer keeps coming back to these pages of the platform then the e –retailer is able to understand what are the customer’s area of interest and  would be better placed to then send offer and mails targeting the customer on areas of interest deduced from his / her browsing history.  
  4. Business Intelligence also helps companies with the designing of Budgets, allocation of funds and forecasting the future spends
  5. Business Intelligence is also a very valuable tool when a company needs to track the performance and ROI on marketing campaigns and advertising


With the arrival of Business Intelligence in the arena of Healthcare, the entire healthcare industry is undergoing a sea change and this is being witnessed in the various areas of healthcare. This industry more than any other relies on data to be a success. Data about patients, medical advancements, latest therapies and drugs available. 

How is Business Intelligence helping the Healthcare industry?

Efficient and Expedited decision making: Most often in critical cases a slew of specialists would be involved and to arrive at a quick decision from disparate points of view can be trying at the best of times.  However, this is where Business Intelligence can be deployed with a centralised portal all patient data, available resources and other inputs would be centralized leading to seamless working thereby improving speed of decisions and medical assistance. 

Cost Management: With a centralized portal, which helps to speed up decisions and medical attention, this will automatically help to reduce costs on all fronts; by reducing the wastage and using resources efficiently, thereby reducing the total cost per patient.  

Some Business Intelligence Software and Systems

  • Dashboards
  • Visualizations
  • Reporting
  • Data mining
  • ETL (extract-transfer-load —tools that import data from one data store into another)
  • OLAP (online analytical processing)

From all the above options a check across industries already using Business Intelligence software and systems found that from all the offerings, the most popular Business Intelligence tools are Dashboards and Visualizations. 

 A way around reducing IT intervention

One of the problems with using Business Intelligence tools was the need for IT support. Many times this becomes difficult and so there has been a concerted effort to move into an arena where the IT support is reduced. This has resulted in the emergence of “Self-Help Business Intelligence”.

What is Self – Help Business Intelligence?

However as Business Intelligence evolves and has greater market penetration, the next frontier will be Self – Help Business Intelligence, also called Self Service Business intelligence. 

This is an arena of Business Intelligence that has done away with IT support for generating reports. Hence the term Self-Help, it means that teams across the organisation can generate reports with the assistance of Self – Help Business Intelligence tools. 

But with very thing that is evolving there will be pain areas and Self–Help Business Intelligence has its fair share. It could lead to decentralising of data collection and could result in creating more confusion than in helping. However, this is something an organisation would have to address at the design stage itself. Why is that; this is because Self- Help Business Intelligence tools are not available as ready to use. It would be required to be designed as per the given organisations’ need.

A real-time example and something that would resonate with our readers is the fact that one of the most well-known brands Starbucks uses Business Intelligence.  As we all know, many brands have arrived on Indian shores with much fanfare, bitten the dust and left. A case in point is the well-known British brand Debenhams, which launched multiple stores all over India and eventually shut shop keeping just one store going in Delhi. So how does one explain the amazing success of Starbucks, even though the brand faced well-entrenched players like Barista and Café Coffee Day.

The answer lies in the fact that Starbucks uses Business Intelligence tools to understand their customers buying choices, what is their favourite/s and are able to thereby discern what kind of offers made by the company, that the customer would opt for. This is done by collecting and collating the masses of data of their customer base. Making their offers and promos more effective and successful. Obviously this helps to increase foot falls and sales at Starbucks outlets.  Their success lies in their growing foot print in this country.

So having got a small brief on what is Business Intelligence, let’s understand what Business Analytics is. 

Before we delve into the world of Business Analytics, let us understand what exactly are we talking about when we speak of Analytics. If one looks at the word Analytics, in any dictionary worth its weight. Then you would find out that Analytics refers to the science of understanding and making sense of data. Of reading between the lines so to say. Analytics has helped organisations the world over to better understand the reams of data that they receive through various avenues. This data helps organisations to understand the success of their products, their preferred markets, consumer response to their products and even what products need to be phased out. Analytics has become a by word in our Information driven society and as society becomes increasingly technology driven then Analytics forms the bedrock for decisions for not just corporates, but also for sports, healthcare and even governance. 

Today Analytics impact a slew of arenas from Advertising and marketing, to risk management and  Market optimisation. 

Therefore, it is necessary to  understand what exactly is Business Analytics

Business Analytics,  uses the process of collecting, collating and sorting to then process and study the data. This data is then analysed with the help of Statistical models. In this manner the data gives you business insights and helps in taking business related decisions. 

There are four types of Business Analytics:

  • Descriptive Analytics: 

This form of Analytics looks at data and this helps a business get an idea of what was the status in two time frames; the past and the current. This is the simplest form of Business Analytics. This type of Business Analytics helps to make existing data accessible to members of the organisation, from across the board i.e from board members to sales teams. 

Descriptive Analytics also helps to discern customer behaviour and also helps the organisation to identify its strengths and weaknesses. 

  • Diagnostic Analytics: 

As the name suggest Diagnostic Analytics helps to diagnose the “how and why” in an organisations performance, this of course is to better understand the past failures and improve on the future results. Diagnostic Analytics uses tools like data mining to really understand the why and how of past performance. 

However Diagnostic Analysis has its draw backs, in that its ability to provide insights that can be actioned on are limited. One of the most common tools that is a product of Diagnostic Analytics is the Business Dashboard.

  • Predictive Analytics: 

Uses analytics to predict future trends and markets based on Statistical models. In short Predictive Analytics builds on the results produced by Descriptive Analytics. 

One of the main uses of Predictive Analytics is to understand customer sentiments. Why is understanding customer sentiment so important? To understand what product / service would be more successful.

  • Prescriptive Analytics:

Actually goes beyond the scope of Predictive Analytics, and gives you suggestions and also assists in manipulation of events so as to achieve better results. It can also help to suggest certain strategies and actions that will help drive a better all-round result. Prescriptive Analytics relies heavily on strong feed back mechanisms and testing to understand the correlation between various actions and their end results. 

While these four types of Analytics can be used sequentially, this is not a hard and fast rule. Many companies may and can choose to move from Descriptive to Predictive Analytics, the possibility of doing this is thanks to Artificial Intelligence which helps to streamline the entire process.

Example :  Use of Business Analytics: 

Axis Bank: Using Predictive Analytics, Axis Bank was able to identify customer behaviour and this helped to prevent customer movement to other banks by addressing customer pain areas like document processing and being able to offer special promotions to target audience even before the idea of changing their banker took root. This lead to improved customer experience with Axis Bank and reduced the wait time for interactions at hub branches and during peak times on the Axis Bank virtual platforms. 

The down side of Business Analytics

Top management Buy in: This is one of the key challenges for getting acceptance for the use of Business Analytics in an organisation. While some form of Business Intelligence maybe already in use, there tends to be a level of discomfort amongst the top echelons of companies to wholly embrace Business Analytics to drive decision making. A case needs to be made for the efficiency and speed that Business Analytics helps organisations achieve. 

Poor Communication between departments:  If the communication and cooperation between departments is negligible or poor, the entire initiative to use Business Analytics for evaluation will end up being a non-starter. For the success of implementing a Business Analytics strategy all parties, whether IT or non IT personnel need to be on the same page. Any failure or inability to do so would doom the implementation of a Business Analytics based initiative.

Dedication Missing: Often times as is the case with the marketing end of any product, miracles are promised and this is not always the case. Additionally Business Analytics as a tool does not come cheap and this can sometimes be the first stumbling block for many organisations. Another concern with implementing Business Analytics, however is that the product is presented as a solution that is easy to implement. 

Something that needs to be kept in mind, is that Analytical models develop over a period of time. It is important that the teams are sensitised and made aware of the time it takes to actually start getting accurate predictions. This initial stage in the implementation of Business Analytics is very crucial and any lack of interest and belief in the system could stop the initiative dead in its tracks with departments refusing to use and or believe the models developing. So in the initial stage of implementation it is important to have everyone on board to see a 100% success rate eventually. 

Quality of Data Available: Most often Business Analytics fail due to the fact that the quality of data available is poor. So to avoid this from happening it is important to evaluate the organisations existing infrastructure’s ability to support new data sources. Also it is important to when starting the initiative to build in a leeway on time. This would be to help collect new data and analyse it.

One of the key differences between Business Intelligence and Business Analytics is the questions they answer. 

For starters, Business Intelligence and Business Analytics tend to be used inter changeably. However, if they are used effectively as a combination they can result in maximising the efforts being made to achieve the organisational goals. While Business Intelligence focuses on descriptive analytics, Business Analytics focuses on Predictive Analytics. 

  • Business Intelligence uses descriptive analytics to provide a summary of past and current data to display what happened in the past or what is happening currently. The role of Business Intelligence is to answer the “what and how” so you can change what does not work and keep what works. 

Business Analytics uses predictive analytics which is based on data mining and machine learning to determine the chances of future results. Business Analytics answers the “why” so that we can make better informed predictions of future events. With the implementation of Business Analytics, companies can predict developments and make the necessary changes to come out on top as a result. 

  • Business Intelligence depends on collection of data, its normal focus is on bringing on immediate productive development. In the case of Business Analytics, it is a constant process. In Business Analytics data is being constantly reviewed. The data is generated by the Business Intelligence platform of the organisation. Therefore Business Analytics is more long term in its focus.
  • Business Intelligence faces limitations; especially when dealing with raw data which may be unstructured or semi structured data.  Unstructured data is data that contains a higher percentage of irrelevant data. Semi structured data is data that does not have a set mould to be easily translated. This is a hurdle that Business Intelligence faces most often. This is because there is no standardisation when it comes to accessing unstructured and semi structured data.  However in the case of Business Analytics, this is not a concern as Business Analytics works with its own calculations and strategy building tools. Making Business Analytics, less prone to the limitations that plague Business Intelligence.
  • Another difference between Business Intelligence and Business Analytics is that when making decisions the role of Business Analytics is greater than that of Business Intelligence. While Business Intelligence is the backbone, by beginning the data engine, it is the Business Analytics that help a company/ organisation understand how and when to deal with upcoming challenges and how best to circumvent a challenge. So in the arena of Business Intelligence and Business Analytics it is Business Analytics that trumps.
  • A Key difference between Business Intelligence and Business Analytics that needs to be mentioned is that while Business Intelligence helps to run organisations to stay ahead of the game be it in improving their performance or matching the competition. However, it is the inputs of Business Analytics that would help the organisation to institute change/s and it is this change which could well mean that the organisation is surging ahead of the competition or is it floundering.          

While for the most part, the concept of Business Intelligence and Business Analytics is interchangeable in the mind of lay persons. The fact is that if Business Intelligence is the Bus then Business Analytics is the Driver. One cannot function without the other and this is something that organisations are learning sometimes at their own expense. 

Smart organisations are embracing Business Intelligence and Business Analytics and to mention one company that has used Business Intelligence + Business Analytics very effectively is Royal Dutch Shell. It has minimised it turnaround time for replacement of parts and servicing of their drilling operations globally from 3-4 days to 45 minutes. Saving time, energy and money in the 100’s of thousand dollars. 

 In the ever changing world of Business, with increasing competition and globalisation the way ahead is Business Intelligence and Business Analytics, so it is important that one is informed, aware and certified to work in these emerging arenas. 

Some of the trends in Business Analytics are as follows:

  • Big Data
  • Internet of Things (IoT)
  • Artificial Intelligence( AI)
  • Micro-segmentation
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