Introduction – Is Lack of Data Management Impacting Your Business Performance?

Almost all executives who manage a business or part of a business are tasked with trying to improve business performance and keep customers loyal. In fact, most are compensated for improving profitability, driving revenue growth and improving the quality of customer service. All of these are key metrics closely monitored by senior business managers and they often want to find and eliminate defects that impact on performance. It is strange, therefore, that although business performance is deemed critical, few business managers take time to understand how performance is impacted by bad data that enters and spreads though across parts of their business operation. This might seem a minor point on the surface but let us consider two examples of businesses from financial services and from manufacturing severely impacted by data not managed and kept in a high quality state.

In a retail bank, risk management is absolutely key. In fact, risk management is the number one issue for many retail banking executives as they strive to reduce the cost of losses and widen profit margins. Although all banks would claim publicly that they have risk management under control, many are also dissatisfied with the losses they incur.

This problem is often rooted in the way many banks have historically managed risk which is at a product level and not at a customer level. Product-level risk management means that a bank may have many different product-level risk management systems, each of which is limited to monitoring risk exposure of customers of single product. This means they can see customer risk exposure for mortgages, loans or credit cards.

However, understanding full risk exposure at a customer level for all products that a customer owns has often not yet been achieved. The limitations of “stand-alone” product-level risk systems are well-known and these limitations can have serious implications on business performance especially when customer data across such systems conflicts. It is often also the case that each product-level risk management system also feeds a corresponding product-level marketing system. Given that customer data in different product-level risk management and marketing systems often conflicts, inconsistent customer treatment can occur.

Also, customer data is not integrated across such systems making it difficult to fully uncover the true picture of a customer’s exposure, payment behavior, etc. The result can be increasing losses due to an incomplete risk picture and inaccurate, conflicting marketing campaigns caused by stand-alone product risk systems that pass conflicting and overlapping customer data on to different marketing systems. These end up recruiting bad-risk customers while the best customers are enticed away by better deals offered by competitors. Essentially, data ownership and management of data quality are critical to getting risk management under control. Data defects and incomplete data can quickly result in mounting losses and inaccurate marketing. One bank in particular lost 16% of its mortgage business in the last 18 months due to these problems while losses mount in its credit card business.

As a second example, consider manufacturing. Many manufacturers are at the mercy of large powerful retailers, which have a much better understanding of demand than manufacturing. With increasingly larger amounts of manufacturing business coming from large retailers, many manufacturers see it as critical that they can align their processes with that of their large retail customers to keep them happy. This includes the ability for a retailer to order centrally for all or a subset of their stores, and also the ability for a specific retailer store to order locally from a specific manufacturing site.

Supporting both central and local ordering imposes added problems on manufacturers. For example, each manufacturing site then has to collect order data from central ordering and local ordering systems to get a complete picture on what to manufacture at each site. If data is not well managed in this scenario, the business performance can be severely impacted. In one such case, a manufacturer that attempted to implement central and local ordering to keep a retailer happy later found it difficult to process orders correctly at each manufacturing site due to data being in multiple places not flowing between systems.

The business impact from lack of data ownership and control of how order data flowed in business operations was considerable. Conflicting and duplicate business processes at each manufacturing site were causing data errors leading to mistakes in manufacturing, packing and shipments. All of this resulted in low customer satisfaction as deliveries turned up late, with incorrect products being delivered. This finally led to the loss of their biggest customer and a lucrative €200 million contract. Integrated common processes and formal enterprise data management were both critical to reducing costs and preventing process defects across all manufacturing sites. Realizing this after loss of business was a bitter pill to swallow.

These are just two examples of a lack of data management, data ownership and data quality having a major impact on business performance. Looking at the impact of loosely-managed data in this context puts the importance of data ownership and accurate, timely integrated data much further up the priority list in helping improve business performance. Data has a significant impact on a business, and IT has a duty to the business to manage it well and uphold data quality to avoid business problems caused by data defects in business operations.

Issues of data integrity also complicate the issue of compliance. Compliance relies on rock-solid data and trusted metrics produced in regulatory reporting. Therefore, data ownership, data quality and formally-managed data is high on the agenda of CFOs and CEOs who are held personally accountable if their company is found to be in violation of regulations.

This paper is the first of a three-part series that attempts to address the problem of data ownership and enterprise data management. It will define data ownership and the importance of managing data quality as data flows throughout the enterprise. The other two papers in the series will raise awareness of technology that allows companies to delegate the management of enterprise data quality and also what companies need to consider when implementing a common data quality strategy across the enterprise.