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A New Class of Operational Data Store

Intelligent Solutions

The operational data store (ODS) has had an interesting evolution over the past five years. It was originally developed to supply organizations with current, integrated management reports. Executives and upper-level managers used these daily reports to determine the current health of their enterprises. The reports consisted of such topics as total sales for the day, total new customers, total orders fulfilled and total product available. The common theme was the consolidated, enterprise view of relatively current data, updated every 24 hours. These operational data stores are examples of Class III ODSs.

The next breakthrough for the ODS came when organizations discovered the need for integrated enterprise-wide reference data such as product codes, location codes, etc. Here there is a need to update the ODS from the operational systems more often than once a day, perhaps every hour or so, with the changes occurring in those operational systems. This is an example of the Class II ODS.

With the advent of customer relationship management (CRM), the purpose for the ODS evolved again. CRM requires an enterprise to create a customer-focused ODS with synchronous or near-synchronous updates occurring (Class I). Changes collected in the various operational systems, such as a new or changed customer address, phone number, status, product activations, etc., must be sent to the ODS immediately so that the customer touches occurring throughout the organization are coordinated and consistent from the customer's perspective.

The Latest Evolution

Most of us thought that the evolution of the ODS had ended ­ but we were wrong! The ODS has changed again to accommodate a very important and difficult problem to overcome ­ the need for instantaneous access to small amounts of strategic information. This column describes this new twist in the maturation of this important component of the Corporate Information Factory.

The Need for Instant Access to Strategic Information

One aspect of the Corporate Information Factory often overlooked or ignored is the interaction between the data warehouse/data marts and the operational data store. This interaction calls for small amounts of pre-aggregated or pre- analyzed data to flow from the strategic decision support environment into the ODS for use with more tactical applications. By placing the results of a strategic analysis in the ODS, we can rapidly access key strategic information while performing operational tasks. This new addition to the ODS evolution is called a Class IV ODS.

Once the strategic results are stored in the ODS, online, real-time support of important strategic information is possible. In doing so, the data warehouse can support online, high-performance access of data when that access is needed.

This is a valuable new concept ­ an indirect approach to strategic information. The data warehouse with its associated data marts contains historical data. To support a conclusion about a customer, you may have to analyze thousands or hundreds of thousands of historical records. If that is the case, it is much better to do the analysis when there are plenty of machine cycles available and no rush, that is, within your strategic decision support environment. After the data is analyzed, though, the result of that analysis can then be placed in the ODS. Once there, this strategic information is easily and quickly available.

Examples of Class IV Operational Data Stores

As a simple example of the interaction between the strategic environment and our ODS, suppose a financial institution wants to create an online environment for loan acceptance for the regular customers of the bank. The requirement is for instantaneous loan approval for valuable and credit-worthy individuals. The analysis to determine the individual instant acceptance limit for each consumer known to the bank must be performed in the appropriate data mart. To determine an individual's credit worthiness, the analysis uses individual historical transaction data, information about each customer's assets and liabilities, demographic data and other integrated information to determine what the bank is willing to risk in the way of an instantaneous loan to each individual. The net result of this analysis is a simple list (see Figure 1).

Customer ID Instantaneous Loan Amount
0010199 $100,000
0010200 $150,000
028971 $75,000
0190432 $25,000
2159801 $200,000
Figure 1: Results of Instant Loan Amount Analysis

The results of the pre-aggregated, analyzed data are loaded into the ODS. In other words, the bank is willing to instantly approve Customer 0010199 for a loan of up to $100,000 and Customer 0010200 up to $150,000, etc. The problem comes in accessing this critical information. Certainly, the loan officer does not want to run this lengthy analysis every time someone asks for a loan. Even if the results set is stored in an easily accessible report, he still does not know who each ID refers to. He has little information about these results other than the customer's ID number. Obviously, he would prefer to go to one place to look up the current, integrated data about his customer and get immediate access to this strategic piece of information.

Therefore, this small amount of data from the strategic analysis is matched with the same customer IDs in the ODS, and the results are stored with each customer's record (see Figure 2).

Customer ID Name Address Pre-Approved
Loan Amount		 Phone Number
0010199 Ron Powell 101 Maple St. $100,000 555-2342
0010200 Jean Schauer 53 Maple Ave. $150,000 555-7896
0028971 Jon Geiger 23 Center St. $75,000 555-4326
0190432 Claudia Imhoff 10 D. Court St. $25,000 555-7654
2159801 Joyce Norris 1908 Phillips St. $200,000 555-3452
Figure 2: Strategic Data Stored in the ODS

Now the loan officer can simply look up a particular customer in the ODS to determine their individual loan limit. If the customer requires more than the pre-approved amount, then the bank must go through its normal loan routine to ensure her credit worthiness for the higher amount.

This is only one example of how critical strategic information can be fed to the ODS. We can think of numerous other examples of strategic information that would be good candidates for this new class of ODS. Figure 3 has a few of these situations.

Strategic Result Definition
Customer LTV Score The analysis determining the customer's overall lifetime value to the organization.
Segmentation Score The analysis determining the customer's market segment score.
VIP Indicator A determination of VIP status based no a customer's other relationships (CEO of an important commercial account), household status (child of important investor), and so on.
Figure 3: Strategic Results Populated into the ODS

As you can see, the operational data store has evolved many times to new and critical roles within the operational world. This latest evolution (but not last?) is one in which the need to access small amounts of strategic information drives the incorporation of this information into the ODS from the strategic decision support environment. The ability to access this strategic information in a real- time mode has proven to be an appreciated and highly successful new twist in a well-established component of the Corporate Information Factory.


Claudia Imhoff, Ph.D., is the president and founder of Intelligent Solutions, a leading consultancy on CRM and business intelligence technologies and strategies. She is a popular speaker and internationally recognized expert and serves as an advisor to many corporations, universities and leading technology companies. She has coauthored five books and more than 50 articles on these topics. Imhoff may be reached at cimhoff@intelsols.com.

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