A Revolution in Enterprise Business Intelligence
Enterprise business intelligence describes a wide variety of applications, techniques and processes that deal with use of data to discover patterns and trends that can impact a business. Enterprise business intelligence is typically associated with large organizations (hence the word "enterprise") but many of the issues and challenges it faces can be equally applied to the management and analysis of data at any level of business size. The past few years have seen remarkable changes in the world of business intelligence as market pressures and rapid changes in data growth have required a complete re-thinking of the approach to business intelligence for the modern enterprise. Through the changes at hand, enterprise business intelligence is likely to never be the same again.
Old School Enterprise Business Intelligence
Before we can understand what "modern" enterprise business intelligence looks like, we must first look to the past. The term "business intelligence" is actually rather old, and the practice of analyzing one's data to discover patterns and trends and to help business users make more informed decisions goes back several decades. Business intelligence as we know it today, however, truly began to take its foothold in the 1990s, as large organizations leveraged their IT resources to use the vast amounts of data they were generating in "warehouses" - custom built databases whose only purpose was to enable the reasonably fast and accurate reporting and analysis of data. This traditional model of business intelligence called for a simple, yet rather costly, three step process: First, aggregate data through what are known as Extract Transform and Load processes (ETL) into central repositories of data (data warehouses). This was done with a simple goal in mind: take data from back-end systems (ERP, accounting, salesforce management etc.) and "normalize" the data into a single location designed for reporting. Second: pre-aggregate the data into views of information that were likely to be required by business users, again with the goal of accelerating and standardizing the reporting process. Finally, the third step involved attaching a charting (dashboarding) engine to the warehouse and building out dashboards and reports necessary for the business.
New Problems for Enterprise Business Intelligence
For enterprise business intelligence the problem, of course, is that the process described can be slow, extremely resource intensive and rather costly. Over the course of the past two decades two critical changes have forced a complete rethinking of how enterprise business intelligence is done. The first radical change was the rapid acceleration of the pace of business. The rapid reduction in the power-to-price ratio for high technology products meant that businesses could suddenly communicate faster, make decisions quicker and simply react in near real-time. Decisions that in the 1970s took months were now made in days and the ones that took days were now done in hours and minutes. In addition to an accelerated business process, the rapid changes in technologies of the past two decades have also created a second, even bigger problem for organizations: with the cost of data storage plummeting it has become increasingly simple and cost effective to store massive amounts of data. In fact, a recent study by IDC found that in 2011 over 1.8 "Zetabytes" of data were created on a global basis, a number so large that it defies explanation. The vast amount of data now stored by the average enterprise, coupled with the need for rapid decision-making, means that the old three-step approach to enterprise business intelligence is beginning to break.
The Dawn of Modern Enterprise Business Intelligence
In order to understand how modern enterprise business intelligence is different, we must first understand the deficiencies of the traditional approach. Because of the accelerated decision processes and the growing amounts of data being analyzed, traditional enterprise business intelligence models become too slow. Modern enterprises need reporting and dashboarding platforms that can offload a significant amount of work from the IT staff and reduce the complexity and length of time necessary to bring accurate information in front of decision makers. This "near real time" need for business intelligence is driving the adoption of two new critical technologies that are making it easier and significantly faster for business users to leverage data in their decision making. First, the advent of the browser-based application has created a revolution in how enterprise applications are deployed through the form of Software as a Service (SaaS) applications. While the SaaS model has made significant inroads into how modern businesses use and deploy applications, the business intelligence market has seen a reluctance on the part of organizations to place their vital data into the hands of third-party web sites. By leveraging the same core technologies of SaaS platforms, however, an entirely new set of enterprise BI platforms are providing the same rapid deployment and ease of management architectures of a 100% browser-based system, without the security fears. The second change to revolutionize the way enterprise business intelligence is done is the user interface. The consumerization of enterprise software is driving the adoption of new, easy to use, interfaces that are 100% drag-and-drop and are finally putting the power of chart and dashboard creation into the hands of business users, lessening the load on the IT staff and accelerating the analysis and decision-support process. The modern enterprise business intelligence platform is only one step away from completely altering the traditional three-step methodology.
Looking Into the Future of Enterprise Business Intelligence
The third leg of the the enterprise business intelligence software industry is the data warehouse. The consolidation, cleansing, aggregation and preparation of data for reporting is not only still challenging, it's being excacerbated by the massive growth in data and the propensity for organizations to want to analyze "unstructured" data - information that is no longer held in a clean, well structured data repository - like web log files or video metadata information. The challenges for data preparation and consolidation will challenge the development of new, revolutionary, technologies that will finally enable the rapid collection and analysis of information, changing the enterprise business intelligence model forever.
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