Business Intelligence (BI) is a rapidly growing method for understanding data to make informed decisions regarding company performance. In the past, few people had access to information to make decisions. This limited the ability for non-management employees to improve business processes and improve performance. Even managers who had access to the information had to wait for a business intelligence analyst to compile the data and provide them with a report, which created delays and made it difficult to make timely decisions that affected daily operations.
But that was the past. Today, few companies fail to see the value in BI to reduce costs, increase revenues, and mitigate risk or, for that matter, the point in keeping the information held within a tight group of managers.
As BI capabilities increased, new methods for analyzing data were created. One such method is Online Analytic Processing (OLAP). The method was first performed in a product called Express in 1970, but it was not introduced as an actual term until 1993 by Edgar Codd. OLAP gained popularity in 1998 when Microsoft released the first OLAP server called Microsoft Analysis Services. OLAP is a very powerful method for processing data. In its simplest form it is a data structure designed to analyze multi-dimensional relationships to provide information. However, that definition does not explain the benefits of OLAP. There are a number of advantages achieved through implementing an OLAP structure. First, OLAP stores data in a unique way. An OLAP "cube" is created with many relationships between the different data sources. A rudimentary example would be a three-sided cube storing revenue, time, and region. While it would be possible to create multiple tables to store this information, it would require as many as six separate tables (if you wanted to sort by each available category) to analyze the data in every way possible. And this is a simple cube with only three dimensions. OLAP cubes are not limited to only three dimensions, and each additional dimension would add a factorial of new tables to store the data using tables. Adding in just one more category would require 24 tables using traditional methods, and five categories would require an astounding 120 tables!
Second, OLAP queries can be quickly processed. This is a vital aspect, especially for operational business intelligence. The value of BI is to make decisions, often in a real-time environment, that impact an organization. If a query takes a lengthy amount of time, the value of the data is reduced because it cannot be acted upon soon enough. For example, if a call center is updated on call volume daily it is most likely too late to make a change to affect your service level agreement. Whereas getting call volume every few minutes allows managers to call employees back from coffee breaks or encourage employees to wrap up calls faster to handle someone on hold.
OLAP additionally provides a way to make data available to a broader audience. This helps to decentralize the decision making of the organization and allows for more ideas to be presented to improve the organization. While internally OLAP data is much more complex to set up and store, from a user point of view it is much easier to use.
An important point to consider about advances in BI, and OLAP in particular, is that the information is accessed by more people. Due to improvements in the user interface, queries can be performed without requiring IT support or extensive programming knowledge. This means that executives, managers, and employees can all make decisions based on the data. However, many of the people who now have access are not business intelligence experts or analysts. While the data may be available, unless it is presented in a way that makes sense to the user there is limited value, or in a worst case scenario, a faulty decision may be made because the data is improperly interpreted. This is especially evident as business intelligence is pushed out to the entire organizational hierarchy. Visualization is important, but more importantly, proper visualization is vital to communicate the information rather than simply to impress the viewer with graphics.
Taking the example above of revenue by time by region, there are a number of ways that this information can be queried and portrayed. In one case, you might want total revenue by time, and then the ability to drill down into specific regions to understand how each area is performing. By creating an OLAP structure this query can be quickly and easily accomplished.
There is substantial value in being able to visualize the data in this manner. First, it is easy for the end user, who may not be aware of the complexity behind the data, to understand what is being portrayed and make informed decisions. Indicators, such as a green arrow up or a red arrow down, can clearly demonstrate changes in performance. Alerts can be created to let users know when certain thresholds are met, such as shortfalls in monthly or quarterly budget targets.
Second, the data can be used as a jumping point to perform further queries. Instead of being limited to the information and visualization that the database administrator or business intelligence expert provides, by using an OLAP structure more queries can be easily performed. For example, an executive may want to see total sales figures for the company. But the regional sales manager may want to see how a specific region is doing, and then more specifics on how each sales team in the region is performing to ensure individual targets are achieved. In this situation the initial information provided, total sales for the company, is a jumping point to gather further information. Previously this would require a specific query which would be created by a business intelligence expert. However, with the advent of OLAP, and drill down and drill across concepts, these questions can be quickly and easily answered by a non-technical user without intervention.
Finally, the user can compare the results to the key performance indicators (KPIs) that have been created. KPIs are a way to gauge the success of the activity and are what companies use to determine the success or failure of a project. By implementing easily understood visualizations, users can gauge their performance compared to the KPIs. For example, if the budget target for a region is set, a bar graph could be created to show where revenue is compared to the target.
There have been many advances in business intelligence capabilities over the past few years which have greatly expanded the value of BI within organizations. The addition of OLAP methods, with the ability to perform multi-dimensional queries and an interface that is easy to use without extensive IT knowledge, has proliferated BI throughout the enterprise and decentralized decision making power. But with the power in the hands of more individuals, it is more important than ever to ensure that data is visualized and understood; contextually, timely, and above all, profoundly simple.
About the Author:
Gregory A. Quirk is the marketing manager at Klipfolio Inc., an Ottawa enterprise dashboard software vendor that helps accelerate business decision-making through real-time awareness of key performance indicators.
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