Data Warehousing Lessons Learned:
Leveraging Data Warehousing in a Corporate Merger
Heterogeneous data is often the state of the average IT organization. The merging of two separate enterprises presents a quantitatively greater and more complex data integration problem, but not one essentially different in kind. The data warehousing group will have cross-functional technical and organizational skills to offer to the entire merged, consolidated enterprise. These skills can be exploited for the good of the new, larger whole.
The merger of two firms presents challenges and will require strong executive leadership to reduce the risks of marching and counter-marching. If the executives of the firms believe they are merging to provide complementary operations and IT is working at consolidation (or vice versa), then serious misalignment will result. As a part of the IT infrastructure, the data warehouses will be subject to the same prioritization, triage and consolidation as the other parts of the IT organization. However, the one significant difference immediately evident is that the cross-functional data warehousing team will now include members from both of the organizations. In the instance where some systems (and people) are being eliminated, the utmost tact and professionalism are required to obtain detailed background on data warehousing design, technology and operations from those in a sunset situation.
Benefits to be obtained from leveraging a data warehouse in a merger are directly proportional to the overlap between the customer and product lists of the two enterprises. If the customer lists were complete duplicates, one system could be eliminated or greatly consolidated; and the products from company X's list could be cross-marketed to the customers at the combined firm. If the customer lists were mostly different, then an opportunity for seizing additional market share is represented by the data warehouse which, in turn, will make possible such a marketing initiative. If the merged firms were in service businesses (e.g., consulting, transportation or healthcare), the extra capacity would be represented in the data warehouse insofar as it represents the merged enterprise as a whole. This does not represent a benefit in itself, except that an accurate model of the enterprise is a powerful tool for management when making decisions and identifying marketing and revenue trade-offs. The benefits of data warehousing for firms that are merging are similar to those when undertaken by a single enterprise - cross-selling and up-selling - substituting information for inventory thanks to a consistent, unified view of customers, products and services.
Of course, this is an oversimplification and assumes what is usually not the case - that the two firms being merged have normalized their customer, product and service data structures against a single, unified, rule-governed representation of the information. Even if the data types are the same, the information is often grouped differently or described in overlapping and partially inconsistent ways or displays different levels of granularity. Much data engineering lies between the existing systems and the reconciliation of the two disparate data stores. In the likely event that such data engineering has not yet happened upon formalization of the merger, such a design task (building a consistent view of customer, products, etc.) can be expected to lie on the critical path just downstream from a readiness assessment and a prioritization of projects in the IT portfolio.
In the context of a merger, the existing data warehouse systems are just another group of systems to be inventoried. If the merged enterprises were already operating enterprise data warehouses, they would be able to compare customers, products, services, etc. across the board and perform an assessment of overlap and cross-sell opportunities. This would be relatively easy if both systems use industry-standard databases, such as DB2, Oracle, SQL Server or Teradata. Even then, it might be necessary to employ "workarounds" such as sequential extracts of customers and products, followed by transformation and load to a rule-governed format that represents the logically unified view of the data. This is easily within the realm of feasibility of one of many extract, transform and load (ETL) tools. If the merged enterprises do not have such data warehousing systems or have ones that are mostly incomplete, building one should be a priority. Such a project can serve as a focal point for constellating combined efforts to match and merge overlapping or inconsistent systems.
Ideally, an inventory of information assets should be conducted prior to the merger. Major incompatibilities can put the benefits of the merger at risk. If the point of the merger is to attain greater operational efficiency by closing or consolidating IT operations, etc., it is especially unpleasant to learn that platforms and business processes dependent on them do not mesh well. Assessment of compatibility of information technology systems is often a part of the due diligence that precedes the merger or combination of the larger firms. However, this is sometimes performed superficially, making surprises inevitable. If such an overview is not possible (or has not been imagined), then it is likely this will be done on a crash basis soon after the merger is formalized. If that's the case, fasten your seat belt!
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Lou Agosta is the lead industry analyst at Forrester Research, Inc. in data warehousing, data quality and predictive analytics (data mining), and the author of The Essential Guide to Data Warehousing (Prentice Hall PTR, 2000). Please send comments or questions to firstname.lastname@example.org.
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