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Keep Your Eyes on the Enterprise

*_Avoid Common Data Management Pitfalls by Putting Your Enterprise-Related Information to Work_*


Every time financial institutions interact with customers on the phone or through other channels, receive payments, or engage in any number of business-related activities, they capture tremendous amounts of data. They also invest millions of dollars each year on new systems and databases to manage, store, and report on this data. However, despite these large investments, most enterprises lose their financial footing, failing to fully leverage the benefits of their data assets.

Using data for tactical business reporting is simply not enough to drive significant decision-making capability. The true value of the data appears when the right questions are asked in the right way. This makes the data far more impactful than the typical “how many” or “how long” tactical reports that are the mainstay of the industry. When properly managed and operationalized, data can provide crucial insight into the answers that financial institutions need in order to make critical business and market decisions. It can help organizations uncover competitive opportunities that lie beneath the surface and, most important, can provide institutions with visibility into changing or emerging market conditions that could have a dramatic impact on the dynamics of their businessâ€"long before it becomes obvious to competitors.

Unfortunately, if you ask financial services executives how satisfied they are with their enterprise reporting, most answers will range between “it’s adequate” to “it doesn’t help me run my business.” This failure to produce more effective reporting results is certainly not due to a lack of effort or investment; rather, it is often related to a lack of understanding about how to maximize an enterprise’s most valuable assetâ€"its dataâ€"to produce actionable intelligence for decision making. Here, we present several viable ways to sidestep the data-related pitfalls that are tripping up reporting efforts.


Institutions gather data through various business applications, store the data in databases, and then use any number of readily available tools to create reports. Producing meaningful, insightful reports that allow business executives to make innovative, proactive decisions can be easy tooâ€"when the right questions are asked and the common pitfalls are avoided.


The most widespread data-related pitfall in the financial services industry is over-reliance on reports that fail to provide any real insight into business operations. Today, many organizations still struggle to answer important operational questions such as:

* What is the cost of originating or servicing a loan?
* Does the organization have adequate loss reserves?
* How much risk is in a specific portfolio (credit risk, default risk)?
* Is the operation properly staffed to handle expected call volumes?

The answers to questions like these are critical to high-quality, on-target decision making. However, without the ability to produce and evaluate key operating metrics, business executives must make decisions based on partial or confusing data and rely on historical experience to help them navigate today’s tough choices. Unfortunately, without a clear understanding of the underlying realities of their operation, executives can make suboptimal decisions that may impact the success of the organization.

This situation can arise for a variety of reasons. Oftentimes, business executives do not have access to the right data or have not fully articulated the questions they need answered. In a successful organization, there must be a strong partnership between line of business and information technology professionals to ensure that business needs are clearly and correctly articulated and that the enterprise’s systems and infrastructure are aligned to meet those needs.

Another key reason for this issue is when adequate technical resources are not deployed to manage and report on the data. All too often, enterprises rely on tactical or departmental databases developed in Microsoft Access when the reality of the situation demands a more robust enterprise database or data warehouse. It’s hard to have adequate visibility into business operations if you are only able to look at one month of data at a time. The solution is to deploy the industrial-strength technology assets required to fully support industrial-strength reporting.

It is imperative for organizations to ask the right questions, capture the necessary data, develop meaningful metrics, and put data to work as actionable intelligence for decision making. With these elements in place, financial institutions begin to see things in an entirely new way.


Unreliable reporting occurs when reports are produced that appear to provide valuable information but actually


contain false or misleading data. Executives who rely on these reports make decisions based upon incorrect assumptions, potentially leading to severe implicationsâ€"often with a negative financial impact for their institutions.

The leading cause of unreliable reporting is pulling data from multiple sources but failing to conduct the necessary normalization process. A simple example of this problem is combining financial data that involves multiple monetary units without normalizing to a single currency. Another more subtle example is combining what appears to be normalized data that has been captured at different points in time. The point at which a source system ships its data to a reporting platform can be crucial in avoiding data timing errors. In a perfect world, institutions would have a consistent view of their data based upon some universal “cutoff” point, where the daily, weekly, or monthly “snapshot” of the enterprise is taken. In reality, however, many institutions are impacted by the inaccuracies that are INSERT IGNORE ed into the process when normalized data is mismatched from differing time frames. Sometimes the inaccuracies are subtle, but other times they can be dramatic both in scope and consequence.

Data quality is also a significant contributor to this problem. As enterprise applications are capturing data, care must be taken to ensure that the data itself is high quality and normalized. Enterprise applications must enforce business rules and edits that ensure that the data being captured is well defined, accurate, and meaningful. Simple approaches like defining valid values for fields or ensuring that dates, phone numbers, and e-mail addresses are properly formatted will provide tremendous value in ensuring the accuracy of reporting. Freeform text entry should also be limited to the greatest extent possible since it is very difficult to harvest meaningful information from it.

The solution is to ensure that the type and time reference for each piece of data in the enterprise is documented, well understood, and communicated. An enterprise data dictionary is a simple, yet foundational, solution to ensuring that the data assets are well understood. While it may take some time and effort, creating the data dictionary is well worth the investment and will ultimately result in more reliable reporting and more accurate decision making.


Another common pitfall is what can be called data overloadâ€"simply too much unorganized data is presented to business executives to be helpful. Unfortunately, reporting is an area where more is not necessarily better. Reports with hundreds of columns or rows are rarely valuableâ€"even to executives who “want all the detail.” In reality, these busy executives don’t have time to conduct the analysis needed to identify important facts or trends. What they need are reports that clearly and concisely answer specific questions without requiring additional analysis.

Data overload tends to occur when business metrics are not well defined or readily understood by an organization. Having well-defined business and operational metrics that consistently describe the operational behavior of the business is critical to reducing the amount of data involved in reporting. Visualization of data is also a very effective way of reducing data overload. Charts and graphics that winnow down large amounts of data to show averages, provide a summation, or clearly reflect the trending of key metrics over time, by product or geography, will ultimately better serve the needs of business.


All too often, data is viewed by executives as something that exists within the information technology realm, shrouded behind firewalls, servers, and databases and referred to in techno-speak like “data warehouse,” “logical model,” “ETL,” “XML,” and the like. However, successful enterprises view and treat data as a strategic corporate asset. The technology curtain is removed, and the data becomes a part of the business vernacular. It is essential to create an environment where data is readily understood and available to executives, so they can use it to make informed decisions and drive the business forward. Of course, they can’t make use of resources they don’t know are available, and this creates an unfortunate but easily resolvable pitfall.

Transparency is a fundamental requirement for a strong partnership between business and information technology professionals so all involved understand what data is available and which assets can be deployed to solve specific business problems. A well-defined enterprise data dictionary is extremely valuable in helping to promote this transparency and provides a shared sense of ownership and accountability.

Enterprise data is one of the most valuable assets an organization possesses. Successful companies use data to answer their most critical questionsâ€"the types of questions that can provide insight into the true performance of the organization. Data can provide executives with the ability to make informed, high-quality decisions and can make the difference between success and failure in a dynamic, highly competitive marketplace.

At its core, putting data to work as a strategic corporate asset is easy. The complete set of available data must be cataloged, normalized, and made visible to decision makers. This requires a strong partnership between business and information technology professionals in order to turn the data into a business asset. With time and investment, financial institutions can put their data to work to drive strategy, win competitive advantages, and deliver better overall performance.

About Author: James Owens


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