The Data Strategy Advisor:
Aligning the Data with Business Strategy Requires Having a Strategy
The formula repeated by data management consultants without exception is: Align enterprise data with the business strategy. Well and good. Yet IT professionals continue to toil in isolation using trial and error to align with something. To line up with the business strategy, the enterprise must have one. The enterprise must also be able to communicate, operationalize and implement the strategy based on real world business dynamics. This requires looking at customer-facing activities as well as operational efficiencies. If the strategy is known only to a few executives, then it is likely to be an idle wheel, not moving any part of the enterprise information supply chain or data architecture. Because software and systems are essential parts of every business process, it is essential to discuss the two together. Therefore, successful enterprises will form a cross function committee composed of business and IT leaders to engage the issues around the interaction of data architecture, structures, processes and the business strategy.
Here is where an example will be useful. In health insurance, strategy must distinguish between those who pay for insurance and those who use it. Employers often buy group insurance for their employees who are the consumers of healthcare services. One possible strategy is to build brand awareness and customer satisfaction among consumers while providing payers with products that deliver cost reductions or at least cost containment. The latter requires operational efficiencies and excellence every bit as demanding in terms of automation and optimization as consumer service and client satisfaction. This often points to centralized data management and architecture to reduce coordination costs and overhead. This supports management's requirements for a timely picture of administrative costs, utilization of healthcare services ("experience"), cash flow and statutory reserves against losses.
For a health insurance company, a business strategy can be implemented as an identity process that expresses a commitment to take care of you when you are sick and helps to keep you well when you are healthy. A centralized network of physicians and hospitals as represented in the healthcare provider databases is critical to this end. The availability of timely, accurate provider data to support the process of enrolling, managing and paying providers is a key part of the data strategy underlying this business process. Making this accessible on the Web for consumer self-service and for the contact center associates is an extension of this strategy that adds value and enhances the service provided to both consumers and payers.
In health insurance, the claim adjudication engine is a key strategic asset rather like the billing system in telecommunications. The adjudication of health claims is a priority process that goes to the heart of what health insurance is all about. Here, the strategy emphasizes operational efficiency. Adjudication is to healthcare what overnight delivery is to UPS - the essence of operating the business. Adjudicate quickly, pay on time (not early).
Even a simplified process of adjudicating a health claim consists of numerous steps and complexities. Capture the data from a variety of mutually exclusive and overlapping channels - submission by the physician's office, by the insured (member) or even by the patient. The individual must be validated for timely membership. This often involves a lookup in a database of covered individuals with coverage extending from some point in time. The claim must then be adjudicated. That means the diagnosis and procedure code must be compared against covered and excluded conditions and the pricing checked for reasonableness. These data points are often stored in a database of policy and coverage options. Deductibles and out-of-pocket expenses must be applied and accumulated. The necessary documents, such as the explanation of benefits (EOB) as well as payments to doctors and hospitals or individual insured, must be generated.
All of these subprocesses involve decision points, and all of them are time sensitive. They imply operational decisions such as approving or disapproving claims as well as management decisions such as requiring staff to work overtime to reduce an open inventory of claims that require manual follow-up due to member appeals or errors in paperwork submitted by members. In this example, aligning the data strategy with the business consists of building consistent, unified representations of membership, coverage, diagnosis/procedure and claims databases. A data warehouse to support the operational decisions would be separate for performance reasons.
The marketing database will have to be sourced and operated separately from the actual membership or claims data structure. It would be quite a scandal to promote new membership or renewals based on case outcomes; though in a certain sense, it would be a marketer's dream. Here a coherent data strategy would invite centralization; but business reality, including statutory regulations, requires separation.
Get started with aligning data strategy by adhering to the maxim: follow the money. Data structures that support revenue generation, revenue renewal or cost reduction are strong candidates for enhancement, extension or routine preventative maintenance. In finance, this will often include a customer data hub in support of cross-selling or up-selling. In consumer packaged goods, this will include a data store of shipments or sales in support of demand planning to reduce inventory. In telecommunications, it will involve a database to reduce churn or improve fraud detection.
The implications of surfacing the business strategy and really aligning the data with it are significant. Data puts the performance back in business process improvement. After all, business performance management aims at process improvement, not just compliance with mandated reporting and controls. The resonant ambiguity in business performance management hints at both performance and the underlying process that is being measured. The process without the performance is empty, whereas the performance without the process is unguided. Strategy is what harmonizes the two.
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Lou Agosta, Ph.D., joined IBM WorldWide Business Intelligence Solutions in August 2005 as a BI strategist focusing on competitive dynamics. He is a former industry analyst with Giga Information Group, has served as an enterprise consultant with Greenbrier & Russel and has worked in the trenches as a database administrator in prior careers. His book The Essential Guide to Data Warehousing is published by Prentice Hall. Agosta may be reached at LoAgosta@us.ibm.com.
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