‘Business intelligence’ is a term that has been around for a very long time now. Originally coined back in 1865 by Richard Millar Devens in the Cyclopedia of Commercial and Business Anecdotes; today it is so widely accepted a concept that it is commonly referred to as ‘BI’.

Business intelligence generally refers to the art of using technology to collect and analyze data in order to make sense of and act upon it ahead of the competition. It shares similarities with the term ‘business acumen’. However, where business acumen refers to smart business sense in general, modern business intelligence tends to focus more on the technological aspects. That is, the timely use of technology (in conjunction with data) to make the smartest business decisions.

The first database management system to spark a rise in business intelligence was DSS (Decision Support Systems). With the rapid advancement of technology, genuine business intelligence continues to grow and is a very real prospect for those wishing to utilize technology and data for a commercial advantage.

1988 was the year when Business Intelligence really took off

The 1980s saw a huge spike in the application of BI. It was during this period that some of the most useful tools of today were developed to work with DSS, in order to simplify BI processes. After the 1988 Multiway Data Analysis Consortium in Rome, the simplification of BI became a major focus. As a result, many more BI businesses were born and each began to develop powerful tools that could produce both data and reports; they also allowed users to effectively organise and make sense of that data.

By 2000, tools were streamlined to such an extent that users were able to make far better decisions, and BI was taking on a whole new meaning. BI tools were more user friendly than ever and the gathering and interpretation of data was finally highly efficient and reliable. In short, BI was now facilitating unprecedented results.

In this article we will take a look at the tools that work with DSS to support business intelligence, and the types of analytics that constitute the practice:

Business Intelligence Tools

OLAP (Online Analytical Processing)

OLAP systems give users the ability to analyse data from various sources and perspectives (otherwise known as dimensions – hence the reference to such systems as ‘multidimensional’). It is an SQL based program, which means it is not as popular as it once was now that NoSQL has taken the driving seat.

However, if your database is configured for OLAP, it can handle complex queries and allows you to perform deep analysis of data. It is mainly used for applications like reporting, including business process, financial, management and business (for sales); it is also commonly used for marketing, budgeting and forecasting. The three main operations it supports are slicing and dicing, drill-down and data consolidation.

EIS (Executive Information Systems)

The term ‘Executive Information Systems’ came about because the CEOs of the 70s needed research systems for their business’ information. Software was created to cater for this need, and this is the name it was given. From then onwards senior management had the ability to make informed decisions for their businesses.

EIS was always about galvanising information and streamlining decision-making for fast and reliable results. It enabled executives to manage the details of their day, from appointments to research, emails and report analysis; they could finally do this without the assistance of lower management, making them more self-reliant and saving a lot of precious time.

However, the system had various limitations that eventually reduced its popularity, and now other tools have (to a large extent) left it behind.

Data Warehouses

When the BI growth spurt started in the 80’s, data warehouses first became a thing. In-house data analysis was the name of the game, but there was one problem. Computers weren’t what they are today and didn’t have the capability to facilitate the necessary analysis while running the general day-to-day programs. Therefore weekends and evenings were taken up with data analysis, which obviously wasn’t ideal for company employees.

That is where data warehousing came to the rescue. Data warehousing had the effect of seriously speeding up data accessing time, as it meant that data could now be housed in one location. It also meant that massive amounts of data could be stored, in many different forms.

This saved users a significant amount of time and reduced overall costs dramatically. As a result, data analysis was so much easier, and when looking back to the days of archaic storage systems, insights and profits were comparatively abundant.

Analytics related to BI

Analytics refers to the data processing tools and methods that support business intelligence. There are several types of analytics: descriptive, predictive, prescriptive and streaming:

Descriptive Analytics

Descriptive analytics relates mainly to historical information and is about the summary of data. By describing the past, it is possible to analyze its effect on current behaviors.

Predictive Analytics

Predictive analytics focuses on future projections based on the data at hand. Statistics enable accurate predictions on trends such as sales, buyers’ behavioral patterns and buying patterns, and more. Companies can use this to anticipate end of year sales and to keep inventories in check.

Prescriptive Analytics

This one is the newest of the bunch, so it hasn’t yet been mastered. The ‘prescriptive’ element refers to the prescribing of different ways of dealing with business issues, offering insights into probable outcomes for such things as scheduling, inventory and revenue streams; it can even offer reasons for the probable outcomes.

Prescriptive analytics are essentially concerned with customer satisfaction; they are advisory in nature and assist businesses in selecting the best possible course of action. However, given that there may be so many variables, it is not an exact science.

Streaming Analytics

Streaming analytics relate to management of statistical information, via calculations, monitoring and preempting competitor moves. It focuses on market activities and events and aids in quick action according to those. Sources of streaming analytics data are typically:

  •    Market data
  •    Mobile phones, tablets and laptop computers
  •    The Internet of Things (IoT)
  •    Transactions

Using streaming analytics, management is able to connect to external sources of data. Data can be merged through the combination of applications, resulting in a steady flow; databases can be quickly updated with new, processed information, which means that issues are less likely to arise within areas such as security, social media, traffic control, and even stock exchange crashes. Businesses are able to make fast decisions based on real-time data, thus allowing them to distribute resources more effectively.