Meta Data ROI: A Competitive Advantage to Your Business
By David Marco
This article is adapted from the book “Building and Managing the Meta Data Repository“, John Wiley & Sons, ISBN # 0471-355232
Chapter 1 “Introducing Meta Data and it’s Return on Investment” provides more detail on this topic. This article marks the second in a series of articles on specifically defining the value that meta data provides to a corporation. In this month’s column I will discuss the value that meta data can provide to the business users of your company. We will discuss the following uses that meta data can be used to provide value to the business:
- Meta Data Driven Business User Interface
- Data Quality Tracking
Meta Data Driven Business User Interface
The reason we exist as IT (information technology) professionals is to meet the informational needs of our business users, unfortunately our current systems are falling well short of meeting the needs of the business. One of the reasons this is occurring is that instead of designing systems that speak to our business users in the business terms that they are familiar with, we have built systems that communicate to them in IT terms. Meta data holds the key to resolving this challenge. Meta data addresses this situation as it looks to provide a semantic layer between our IT systems and our business users. In simple terms meta data looks to translate the systems technical terminology into business terms that the business users are familiar with. Figure 1 illustrates a web-enabled decision support system. This web front-end is designed with the business user in mind. One thing that we need to understand is that the business users of our systems do not care whether the information they are looking at comes from a data warehouse, data marts, operational data store, or meta data repository. They just want to be able to find the information they want quickly, and in a manner that they understand. Let’s suppose that the business user wants to see the numbers on monthly product sales. The user would go to the decision support web site, named “Corporate Information Access” (see Figure 1). At this web site they would have the ability to search for this flavor of report.
Figure 1: Meta Data Driven Business User Interface – Decision Support System Web Site
Once the business user gets to the “search” page of the decision support website (see Figure 2) the user could search for any decision support reports that have “monthly product sales”.
At this point is where meta data comes into play. In the meta data repository there will be meta data that has business definitions for each of the decision support reports. Therefore the business user can search through the meta data business report definitions for the reports that have the words “monthly product sales” in their meta data definitions. The results of this meta data search appear in Figure 2.
Figure 2: Meta Data Driven Business User Interface – Search Results
As we can see the user can select from the reports that are returned or enter a new query. For our example it turns out that our business user wants to see global, summarized product sales, by category, on a monthly basis by region. As a result, the second report returned looks exactly like what our user is looking for. As the business user looks at this report (shown in Figure 3) they may want to know exactly how U.S. sales dollars is being calculated. The user could merely “right” click or hit a “hot” key (F1, F2, etc.) on the “U.S. Sales $” field and see the business meta data definition for it. This type of business definition for U.S. sales dollars is stored in the meta data repository. By integrating this business meta data into the decision support report the business user will understand exactly what goes into U.S. sales dollars. As we can see in Figure 3 U.S. sales dollars includes sales from Canada and Mexico, but does not subtract sales dollars from returned product orders. This type of information make the data in the decision support system much more valuable and improves the accuracy of decision making.
Figure 3: Meta Data Driven Business User Interface – Business Meta Data Definitions
As we can see meta data has taken this decision support system and vastly improved its value to the business user utilizing a meta data driven access. In addition, the value of the actual information in the decision support reports is vastly upgraded through the use of business meta data. Table 1 summarizes the value that business meta data provides.
Table 1: Meta Data ROI – Business Meta Data Benefits
Data Quality Tracking
Data quality is a significant issue impacting many, if not all corporations competing in today’s marketplace. Companies realize that IT systems are a strategic weapon that can provide a significant advantage over their competition. However, if the data in these systems is redundant, inaccurate, missing, or incomplete than the corporation is placed at a severe and distinct disadvantage. In addition, many companies have “mission critical” initiatives like e-business, customer relationship management, and decision support. All of these initiatives will typically require data to come from the company’s existing legacy systems. If the quality of the data in these systems is poor it will directly impact the reliability, accuracy, and effectiveness of any of these initiatives. The old IT saying of “garbage in, garbage out” illustrates that data quality or the lack thereof is critical to any enterprise.
Table 2: Meta Data ROI – Data Quality Tracking Benefits
In addition, all of these decision support system’s data quality metrics should be stored in the meta data repository and kept over the history of the decision support system. This allows corporations to monitor and see if that are improving their data quality over time.
In decision support systems it is common to compare field values from different time periods. Figure 4 displays a decision support report showing global corporate sales on a monthly basis for a consumer electronics manufacturer. A business user could use this report to compare U.S. sales from October 1999 to November 1999 for the holiday buying season. As the business user compares these numbers they may feel that the sales amount for November seem to be a little low. They could check the data quality statistics and see that 8.4% of the records in the November decision support load run erred out and were not loaded. This would let them know their margin for error when making decisions based on this report.
Figure 4: Meta Data ROI – Data Quality Tracking What
What Happens When Data Quality is Skipped
Unfortunately, quite often companies do not want to spend the money or the time to uncover, evaluate and resolve their data quality issues. I had one such client that was a very large international, insurance company that had multiple decision support projects going on simultaneously. In my initial work proposal I allocated time and resources to conduct a data quality study to gauge the quality of their source system’s data during the feasibility phase of the decision support initiative. However, the client did not want to spend the time or the money on an activity that they felt had minimum value and despite my urgings would not conduct the evaluation. In their minds they had no data quality issues so why spend valuable time and money evaluating the data. As the project continued, we were well down the design path of one of the data marts and our development team discovered that the quality of the data in the source system was of such poor quality that the reports the business needed would not have accurate computations. Moreover, the data in this system was so bad that it did not have the information necessary to clean it. To make a bad situation even worse our project sponsor did not have the authority to go back to the IT team maintaining the system and ask them to change it. As a result, I was left with a task that every consultant dreads. I recommended to senior management that the project be stopped. Because of the severity of the data quality problem, senior management supported my recommendation. Our one saving grace was that our other decision support projects met with much better success than this particular effort, however this client lost approximately $225,000 in consulting fees and employee salaries, above and beyond what it would have cost to evaluate their data, up front in the development process.