Banks are clamoring to incorporate data warehousing into their information technology operations.
The overriding reason is that the technology offers people at all levels - in the front office and back office, from the teller line to the executive suite - the power to make mission-critical decisions more quickly, more accurately, and more easily.
The 1994 Ernst & Young and American Banker Special Report on Technology in Banking estimated discretionary spending on modernization and new technologies would rise 30%, of which 30% was expected to be directed at improving the quality of decision-making.
Data warehousing directly targets decision-making for virtually all critical paths to profitability, starting with profitability analysis itself and extending to risk management (both credit and interest rate risk), improved customer support service, and target marketing of new products.
The demand for data warehousing stems directly from the increasingly competitive landscape of the financial industry, in which banks are relying on technology advances to differentiate themselves and to improve productivity and profitability.
Also spurring the demand is the onslaught of mergers and acquisitions that is bringing together disparate systems and growing volumes of data to be churned and manipulated for decision analysis. The M&A phenomenon alone has challenged all aspects of the information technology infrastructure, from hardware capacity to software capability.
The promise of data warehousing for most organizations reads as follows: Data warehousing consolidates information into a single view from disparate systems throughout the financial institution for analysis and optimized decision-making.
Though most banks have tried for years to bring together information, an overwhelming obstacle has been technology and the costs involved. Information in disparate systems throughout the institution has remained grouped as "islands" for isolated decision-making.
Accurate and timely decision-making has suffered immeasurably. As mergers and acquisitions have become more commonplace, so has the number of disparate systems within the combined institutions. This compounds the problem of bringing together information.
The costs associated with computing and storing large volumes of data have dropped tremendously, putting data warehousing within reach of more bank budgets. It's the consensus that hardware prices drop by half every 18 months in the open systems market and data storage costs drop even more significantly.
The Gartner Group estimates that 10 years ago it cost about $28 million to store one terabyte of data. By the end of the decade, the same of amount of data cost $1,000 to store. It is no wonder we are seeing an increase in the integration of advanced open systems platforms with presiding mainframes and, in many cases, the adoption of replacements for mainframe systems.
But multiprocessing - computers using several processors for parallel processing - has had the greatest impact on the technological merits of data warehousing.
Data base companies have revolutionized the use of this technology by radically improving the cost-effectiveness and performance of processing information.
Parallel processing allows the breakdown of a single query into components, and each component is assigned to a different processor that can operate on its portion in parallel with the others.
This is revolutionary. A bank can now reduce the performance time to answer a query in a linear relationship by the number of processors working on it. For example, if a bank has 20 processors, response time can be reduced 20 times.
Combined with improvements in the cost performance of hardware in the last couple of years, the overall cost-effectiveness of querying has improved by a factor of nearly 100.
In essence, data base technology has delivered a way to cut the time it takes to perform a complex query from 1.5 hour to about two to three minutes, while keeping costs constant.
Imagine the impact on cost/performance, customer service, and profitability if a teller can instantaneously cross-sell at the time of transaction and an inquiry can be responded to at the time of the call rather than the following day.
One of the greatest obstacles to adopting a data warehouse is integrating disparate systems. Advances in data base technology now deliver the essential ability to interoperate with all prevailing systems and platforms. This means that a bank can easily use its data base management system as the point of integration for information sources throughout the institution.
To secure customer relationships, many banks are seeking a data warehousing solution to improve the management of their information assets. According to the Ernst & Young/American Banker study, customer-focused spending is consuming the largest portion - 40% - of banks' discretionary technology budgets.
This begins with more effective use of the vast quantity of historical customer transaction and behavioral information that is captured and stored during normal processing.
The data warehouse collects information from throughout the enterprise and offers an easily accessible and accurate bankwide view of its customer base for marketing and relationship management. By consolidating and reporting marketing information of current and prospective customers on a timely basis and in a single view, institutions can determine the makeup of their target customer base.
Data warehousing enables institutions to cross-sell products aggressively to established customers and new market segments. Since the number of relationships per customer is a key indicator of a bank's profitability, banks are looking to data warehousing to give them a cross- selling mechanism, thus increasing the volume of customer relationships.
Marketing and sales opportunities are also heightened by the data warehousing model. By better understanding its current customers and, thus, its market, a bank can target the best segments of the market with the most appropriate products. New products and services can then be brought to market more quickly and effectively.
Identifying relationships is essential to reviewing an institution's exposure to various credit risks. A bank needs to evaluate not only its customer relationships but also relationships between the accounts held by each customer and, more importantly, relationships between its customers.
For example, if a bank offers credit to many companies that are dependent on a very narrow economic sector or if all of a bank's customers are suppliers to a single large bank customer, the institution may have a high concentration of risk and dependency on a single company, although its assets are spread among many companies.
Thus, it becomes critical for a bank to integrate the information from different sources via a data warehouse and to structure the data to clarify the bank's risk situation.
To better control interest rate risk, banks must analyze how, for example, loans, deposits, securities, and derivatives will behave in a wide range of potential future rate environments. Earlier systems used highly aggregated or summarized data that obscured embedded risks.
Modern asset-liability management systems draw from a comprehensive data warehouse that contains, for each instrument individually, all the parameters needed to calculate cash flows in any future environment.
For complex instruments, this may require dozens of different fields in each record. A large bank may hold millions of instruments in a data warehouse, requiring several gigabytes per periodic "snapshot." By analyzing instrument-level data, banks can accurately learn how they would be affected by future environments.
With such knowledge, banks can act to avoid or minimize negative outcomes or to test new strategies to maximize financial return within the risk tolerance established for the institution.
Historically perceived as a fixed-cost business, banks did not focus on profitability. As times have changed, banks need to offer many products and be more sensitive to their profitability. Profitability systems, as a result, are becoming exceedingly popular.
Data warehousing for profitability integrates data from a variety of transaction-based systems regarding both the products offered and the many relationships the bank has with each customer.
For example, a customer might have a few loans, a credit card, a checking account, and a savings account. All these data must be aggregated, summarized, and stored for profitability analysis. Using profitability systems, the bank gains a single, consolidated view of each product, service, or customer and can then evaluate it for its contribution to the bottom line.
