Business intelligence nowadays is not about mining data to get questions answered. It has pushed its way to a mechanism which actually helps businesses ask the right questions. In order to make the most of a valuable business achievement, the company must be in control of the factors that made that achievement happen, and do so monitoring progress while it is gaining momentum. As small or basic as the business may be, this is something managers can’t allow to be performed manually, which makes BI systems the most important tools they will ever purchase for day to day usage.
Another current trend ascribed to business intelligence software systems is that they make complex analytics simple, bringing them down to intuitive charts that don’t require statistic experts to command the wheel. Hosted in the cloud and updated automatically, good BI system tools also becoming the most secure hub to store sensitive corporate information, from where each employee can access it without necessarily using third-party services.
But how did BI software development arrive at such perfection? For the sake of truth, it took the BI industry approximately 150 years to become what it is today, 1865 being the year when corporate entities first realized the possibility to make more informed decisions based on current performance. Many things have changed since, but the pursuit of a competitive edge in the corporate environment certainly has’t.
In this article we’ll be ‘traveling’ back in time, and tracing the highlights of the business intelligence industry to help today’s performers appreciate the value of data they have at their disposal. We invite you too to join us on this trip.
The emergence of Business Intelligence
If we have to point out a specific year that marked the beginning of BI development, that would certainly be 1865, and the definition of ‘business intelligence’ introduced by Richard Miller Devens in his book ‘Cyclopaedia of Commercial and Business Anecdotes’. What Davens planned there was to depict common reminiscents between bankers’ decision making, one of which was using existent data for more informed business decisions.
It took almost a century to wrap up the idea of business intelligence into an individual scientific process. Namely, it was only in 1958 that the basics of this analytic concept were laid down in a single piece of work, in this case IBM’s Journal Article titled ‘A Business Intelligence System’. The article was composed by Hans Peter Luhn, the IBM researcher who also invented KWIC (Key Words In Context) indexing, and is officially considered to be the ‘father’ of digitized business intelligence. His biggest contribution is describing business analytics as a multi-tier process that is easy to understand for users other than statistic business expects.
The advent of BI-exclusive software
One could not expect business intelligence to note some particular success in the middle of the previous century, as companies were still pulling off most data work manually, and had not computerized almost any of their operations. There was still no computer program that could execute analytics digitally, but scientists were more eager to develop one than ever before. As expected, the growing need to facilitate data visualization was headed by IBM and Siebel (currently acquired by Oracle) which were the first big names to join the race for the first smart data management system.
In the years between 1970 and 1990 the first prominent providers of streamlined database management systems appear. The most important event that actually defined how a good BI software system should look like was the Multiway Data Analysis Consortium held in Rome in 1988, where most interested vendors learnt how to streamline data processing in order to promote a product that is going to be attractive to businesses. During the Consortium, experts considered mechanisms that would respond to companies’ need for more time efficient and less expensive data management. As a result, the years up to 2000 revealed numerous professional analytic systems and reporting apps, some of which are still the most prestigious providers of such services around the world.
The transfer of business intelligence into the cloud
It was only after 2001 that business intelligence software systems started looking the way they do today. On premise data analytics were confirmed as a successful decision making asset, but they were far too expensive and effort-demanding for small businesses to afford them. What the IT technicians were looking to do at that point was to enable users to extract data from their systems independently, store it in an easily accessible location, and make sure all team members disposed of self-service tools necessary to visualize and customize that data to their needs.
Cloud storage made this possible. Ever since 2006, BI vendors are offering cloud-hosted data management service for remote teams that costs less and performs faster, emphasizing foremost on mobile-empowered systems for users to gain full access to corporate information wherever they are. From 2010 on, it doesn’t matter which type of device you’re using – you can modify business data and monitor progress on your phone just the way you do in front of a computer.
The arrival of Big Data
Once data was transferred and manipulated in the cloud, it was no longer difficult to maximize the potential of business intelligence. With more and more powerful systems arriving on the scene, prominent vendors thought of developing solutions that could handle large volumes of data, in particular unstructured one companies had no clue how to categorize it. First endorsed by big names in the niche, this trend led to almost unlimited data visualization, and more and more companies were turning to them to streamline decision making, and stand out from their competitors.
The real breakthrough, however, happened only in 2010 with the introduction of the In-Chip Technology, which followed the development of 64bit PCs and made use of their capacity to create scalable databases and analyze data 10 times faster than the traditional pioneer apps that are still working with conventional methods. The founder and most prominent user of this technology is Sisense, a company that revolutionized modern business intelligence, and was award and recognized in many occasions.
The sophisticated data analysis of our time
It is 2016, and the BI market is rolling more billions than anyone could ever imagine. Digitized data intelligence is currently worth more than $90 billion dollars, and the trend doesn’t seem to go nowhere below that. BI systems of today are not only analyzing data, but catering to a variety of corporate needs, including problem identification, improved marketing strategies, sales, and streamlined communication. The best part of such sophisticated software is that the business health diagnoses carried out by it help manages forecast progress long-term, and on the basis of all available information.
What to expect from BI systems in the future?
The rapid business intelligence systems expansion in the last decade made it clear that we should expect the unexpected. We can only guess the direction in which BI capacity is about to expand, driven by concepts such as big data analytics, mobile visualization, and predictive analytics.
A practice that seems very probable is the preservation of large data warehouses, and processing their content using the advanced, faster and deeper apps of the future. Enterprises, in particular, should think of new functions and initiatives they would like to see embedded in these systems, or base even more of their processes and technical operations on conclusions extracted through data analysis.
A trend that is pretty self-explanatory and doesn’t take software expertise to guess is wide availability. More and more prominent providers are tweaking the functionality of their systems to make them suitable for startups and small businesses, or reform their pricing schemes to introduce quote-base packages where users choose their own features. This is one of the reasons why there is almost no company that doesn’t use a business intelligence system to improve the quality of its decisions.