The need for improved decision-making capabilities, using increasing computing power, led to the emergence of business intelligence. Organizations began building data warehouses to take advantage of business intelligence concepts and technologies. With that, decision support started impacting every level of an organization. Standard reports and spreadsheet analysis were converted to sophisticated reports and alerts using OLAP capabilities. Still, most of the development took place in an era where the organizations were very eager to spend on information technology in general.

Today, organizations no longer spend money on technology that is “nice to have.” Any technology that doesn’t promise a quick impact on the bottom line is considered nice to have. Organizations today are clearly in a cost-cutting mode. In the old days (a couple of years ago), it was a good idea to spend money on IT products and services without really calculating and validating estimated return on investment. In those days, industry leaders continuously emphasized the importance of going through the ROI calculation phase. As the profits kept shrinking, organizations realized the importance of gaining a positive return on investment that will impact the bottom line. Today, it is all about managing the bottom line.

We are all looking for a framework by which we can accomplish our simplest goals. This is the time when business intelligence is needed the most.The transactions tell the story behind revenue, cost and profit. Well, maybe half of the story. It is half of the story because what we have today has already happened. At the end of the day, if we have a well-built data warehouse, we know the story behind the transactions that take place every day. The business insight we need is for more complex decisions that are associated with different levels of probability measures.

The core of this solution is the business model. The business model represents the ins and outs of business activities that occur. Simply put, one would look at the components of the business model, try to increase the revenue items and decrease the cost amounts by focusing on key business activities. As the business activities get complicated, the business model becomes more complex. There should be a way to get the full picture of the business regardless of the level of complexity and identify the detailed elements. Even if all the components were identified, one would still have to make assumptions and assign probability levels to the assumptions made. Traditionally, the statistical component of such an analysis has not been a part of the business intelligence solutions.

With the introduction of a statistical component to a data warehousing solution, organizations are able to optimize the revenue and cost levels which help them position themselves better in the marketplace, according to their core strategy. Two firms in the same business may reach totally different conclusions because one may have the goal to capture market share and the other to maximize the profits. Without complex analytics, neither would reach the desired optimization levels. Although the efforts to incorporate concepts such as modeling and optimization in the existing decision support capabilities has traditionally been expensive, with a data warehousing implementation more than half of the equation is solved. The business is modeled, the data collected and stored in an organized format and the decision-makers presented with the powerful analysis tools. The complex analytics layer introduces new data, which is mainly used for hypothesis testing and statistical analysis of all data. Then the results are brought back in the data model to visualize metrics that are calculated vs. modeled vs. estimated.

With complex analysis, the results may be sparse initially, since the decision-makers didn’t have the insight previous to building the complex analytics layer. With the statistical modeling of the data, they will have better guidelines in terms of how to price and where to save. The new decisions will be based on the results of the complex analysis. After a few business cycles, the ripples in the data become smaller and smaller since the business is now being run based on the probability guidelines presented from the complex analytics layer. Business intelligence and complex analytics result in increased business insight and higher decision-making accuracy. For instance a decision to cut costs across all layers of the organization might be less effective than cutting costs on units that provide less revenue and more expenses. A business unit that achieves higher margins would actually be hurt from the decision to cut costs across the board, and this decision could lead the organization into an even worse financial state.

These decisions are all complex decisions because there are several unknowns and assumptions. It takes less effort today to incorporate a complex data analysis component to the existing data warehousing solutions. In times of financial uncertainty, it becomes more important to create an intelligent organization to survive in the marketplace. When the turbulent waters calm down, those organizations that implemented complex analytics will naturally become the leaders and enjoy the financial benefits of achieving strategic intelligence and the head start they took advantage of.

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