The topic of this month's column was inspired by a request from one of our readers for a column on how to measure the ROI of a data warehouse. Much has been written on this topic both at the DMReview.com Web site and in other publications (an entry of "ROI, data warehouse" on Google yields more than 80,000 links). There is a wealth of information about data warehouse ROI - some of which is highly informative. As a result of this and also because we believe the real value from a data warehouse is actually realized through the applications and solutions that it enables, we have taken a slightly different course to address the question that will hopefully not repeat what has already been written.
The Cornerstone for Business Intelligence and Performance Management Solutions
Just as we pointed out in the May column, where we discussed the measurement of ROI for IT consultants, the business impact of a data warehouse results not so much from its implementation as from the analytic solutions and business processes that it supports and enables. At a high level a data warehouse can be likened to a super-powered database where information from a variety of disparate data sources is integrated, transformed, loaded and organized for future access according to sets of business rules. The main purpose of a data warehouse is to support and drive analytic and reporting applications that layer on top of it.
The DM Review Readership Survey, reported in 2003, showed that 64 percent of respondents were using a data warehouse and 29 percent planned to purchase one or upgrade in the next 12 months. The growing popularity of data warehouses is actually being driven by needs for the business intelligence and performance management solutions that are an application layer above the data warehouse. According to Claudia Imhoff, president of Intelligent Solutions, "Organizations need an enterprise-wide, coherent infrastructure that brings every producer and consumer of information together in a reliable, consistent, timely manner." As the foundational centerpiece of a BI or performance management solution, the data warehouse can make or break the success of an enterprise business process management solution.
The real economic impact of a data warehouse, therefore, is inseparable from the impact of the total business solution of which it is a key component. Because the range and scope of business solutions that can be deployed from a data warehouse is so wide, a discussion about ROI value drivers is difficult; each type of solution category will have its own ROI value drivers. Furthermore, the extent and complexity of the data warehouse itself can vary widely across enterprises. For example, one company may have installed a CRM data warehouse while another may have an implementation that includes supply chain, marketing and HR. Finally - just to make things even more complicated - different industries implement data warehouse applications according to their specific business and operational needs (a data warehouse for a managed care organization for example, will be substantially different from one deployed by a company in the consumer retail space).
The point here is that there is no such thing as a data warehouse ROI model that "fits all" data warehouse implementations. When it comes to ROI, a data warehouse ROI is likened to a camelean; its ROI is completely flavored and influenced by the top level enterprise application that relies upon it. While there may be some common drivers of ROI at a high level (such as increased revenues or reduced labor costs), it is at the business process level that such performance metrics manifest. At the business process level, the economic value of a solution is particular to the scope of the data warehouse, type of analytics solution it supports, and industry in which it operates. Clearly there is no single set of granular ROI value drivers that represent all possible combinations.
Generic ROI Drivers
Having said that, however, it is possible to assess the ROI of a data warehouse solution implementation at a higher level which, although not as comprehesive, is better than nothing at all.
According to a study of the Total Economic Impact of the PeopleSoft Enterprise Performance Management solution (which includes their enteprise data warehouse) conducted by Giga Information Group in 2003, key drivers of data warehouse ROI include:
- Improved decision making from a single reliable source of information
- Reduced IT and business unit costs of data management and reporting
- Increased efficiency for information access
- Improved operational performance due to integration of analytics into business processes
The concept of having near real-time business data being cleansed and loaded into one centralized data warehouse according to customized business rules can obviously have a major impact on the ability of enterprise management to make informed decisions. How this decision-making improvement impacts the top or bottom line, however, is highly dependent upon numerous workflows, information transfer and people. Quantifying this benefit will therefore require systemaic drill down into multiple business processes across the enterprise.
Another generic benefit of a data warehouse and analytics solution is the ability to reduce the IT resources that would otherwise be required to manage many separate databases dedicated to certain functional areas. Again, it makes "sense" that fewer databases will require fewer IT resources. Closely related to this benefit is the added reduction in IT requests due to greater access to information and automated reporting that are attributes of most data warehouse and analytic business solutions. This savings in IT costs might be able to be tangibly quantified, but can only be tracked to the bottom line if the number of IT FTEs can be reduced or help steady as a result.
Finally, the improvement in operational performance due to refined and automated business processes is potentially a major key ROI driver. If perceived as more than just time savings, real operational performance through new and improved business processes (enabled by the data warehouse business solution) may bring significant impact to both the top and bottom line. Tracking this value to revenues and profits requires a disciplined and systematic approach to discover credible tangible benefits that exceed the total cost of ownership.
Unfortunately, simply knowing that a data warehouse business solution can impact your enterprise in the four areas listed above is insufficient to demonstrate expected economic value. The following section suggests a best-practice approach to assessing the quantitative ROI of a data warehouse-based business solution, no matter its enterprise extent, scope of supported analytics and reporting, or industry.
In order to quantify the ROI of your data warehouse business solution, you must first know what you will need to measure in order to do so.
- The place to start the analysis is with your corporate performance goals and objectives. These include revenue and profitability or margin goals, market share, customer retention (repeat customer business), new product opportunity, and so on. Focus on the corporate performance goals that are associated with tangible metrics for which data can be reliably collected.
- Having identified the relevant performance metrics, turn to the functional areas that house the processes that must be executed to meet those goals. These workflows and business processes are what drive the performance metrics. For example, in the functional area of customer service, business processes associated with problem resolution are directly related to a corporate goal of improving customer retention. In the functional area of product development, business processes associated with quality assurance are directly related to meeting time to market windows and reducing development cost. In the functional area of sales, business processes associated with real-time territory optimization are directly related to increasing sales to targeted prospects.
- Once you have identified your corporate goals and performance metrics and the functional business processes that drive them, you are ready to examine the extent to which these business processes are helping you achieve your goals and performance objectives. This examination is central to the success of your implementation and crucial to the economic impact it may have on your business. This step will reveal the deficiencies in existing workflows and processes which will help you to identify the changes that would address these deficiencies. The outcome of this part of the exercise is an inventory of business process changes necessary to achieve your corporate goals.
- Knowledge of the exact areas where time delays and additional expense is incurred should drive your plans for the scope of the solution - both the data warehouse itself and the analytics or business intelligence solution it will support. You will know what functional areas it will touch, who will use it, and what data sources are required to drive it. This, in turn, will drive the cost (along with other factors). The combined solution should play a role in refining or replacing all the buinsess processes (including both workflows and people) that are driving your costs and reducing your revenues.
- The actual measurement of net ROI benefit begins by capturing the business performance metrics of each business process that will be affected - prior to implementation. These metrics may include number of FTEs, time to payment, vendor performance, or time to resolution of issues received by the call center. It all depends on what functional areas are included in the scope of your data warehouse. How many employees are currently in accounts receivable? What is the average delivery time of my suppliers? How many inventory turns do we have per year? What are my days sales outstanding? What is the average sales cycle? How much of our total revenues come from repeat business?
- Once you develop business processes to capture these business performance metrics going forward, you should harvest performance data regularly post implementation - every quarter, semi-annually and annually. The difference between these business metrics before and after solution implementation (less the total cost of the solution investment) will generate a positive or negative tangible return.
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