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BI Strategy:
Understanding the Scope of Data Management - Addressing Enterprise Challenges and Delivering Quality Data

online columnist Rich Cohen     Column published in DMReview.com
September 7, 2006
 
  By Rich Cohen

The enterprise data management (EDM) bandwagon is getting pretty crowded. The concept of synchronizing and managing data at the enterprise level is increasingly being viewed as a panacea that will solve most - if not all - corporate data problems. While I do not believe it is a silver bullet that will cure all corporate ills, an EDM initiative can help solve a host of data problems, if it is implemented correctly. However, that's often a big "if." Too often, EDM initiatives - despite their name - are deployed using a disjointed approach that doesn't truly incorporate the enterprise-wide implications of the data management process.

To be sure, there are an ever-growing number of EDM methodologies that are marketed by software vendors and consulting companies alike. Most of the methodologies are perfectly capable of providing an approach to developing a comprehensive EDM solution. However, the solution to any problem is only as good as the clarity of definition of the problem allows it to be. If companies do not understand their needs before their solution is implemented, the solution is unlikely to solve the problem.

This month and next month, in an effort to facilitate a better understanding of the true, enterprise-wide nature of data management, I will discuss some of those implications and provide you with some strategies you should consider to help guide your EDM project toward fulfilling the lofty expectations that are surely set for it.

In this column, I'm going to focus on analyzing information needs, results metrics and information sources; key areas to focus on to measure results; and challenges that must be overcome on the path to an effective implementation. Next month, I'll discuss some technical innovations that are making EDM implementations easier, business functions and processes most impacted by an EDM initiative, and the components of a robust EDM solution.

Figure 1 represents which components of an EDM solution must be analyzed to truly understand data management implications across the enterprise.

Figure 1: Key EDM Analysis Areas

Let's start by looking at the enterprise implications of data itself. Data that is critical to understanding, managing and growing the business is resident in all corporate information systems such as data warehouses/marts, enterprise resource planning (ERP) and customer relationship management (CRM) systems, and myriad other systems that serve as sources of record for financial and operational data. Much data is also located in stand-alone departmental databases and applications and even in spreadsheets used for analysis. It is absolutely essential to determine what information users need to do their jobs and where (and in what form) that information resides.

Speaking of information users, it is also important to truly understand the needs of the user community - both in terms of what information they need and how they use information in the course of doing their jobs. Information needs and usage requirements will differ markedly based on job function. Managers will have different needs than analysts, who will have different needs than customer-facing workers. All of these potentially contradictory needs must be accounted for in the design of the EDM strategy and architecture as well as their implementation.

Once the scope of enterprise data and the needs of the user community have been defined, the next step is to understand how the data is used to monitor and manage business operations. Is the data used for research purposes? Modeling? Operational/financial analysis? Performance management? All these uses are different, and they must be accounted for when designing the EDM strategy and architecture. It is not enough to know about them, you must understand them and accommodate them.

In the actual design of the EDM strategy and architecture, there are three key areas that will play an important role in determining how effective your EDM project is at implementation and beyond. They are design, modeling, and delivery. Your concern for the design area should first center on how the actual EDM solution will be deployed - in other words how (and how well) the different software packages and technologies will be installed and integrated to provide reliable, consistent, structured data.

Figure 2 represents the key focus areas for EDM design.

Figure 2: Key EDM Focus Areas

You should also determine the comprehensiveness of any analytics component that has been designed. Will it meet the needs of all users? Is it flexible? Can it grow as the company grows? All these questions should be answered before the EDM solution is deployed.

The next focus area is the modeling aspect of the solution. Modeling capabilities are a subset of an overall analytics solution, but I'm breaking it out as a separate focus area because effective modeling/analytics is almost impossible without an effective EDM solution. It is essential in the design of the EDM strategy and architecture that you consider all present and any anticipated future needs that your company may have for modeling capabilities.

For example, do you have a neural network and predictive modeling initiative in place (or are you thinking about one)? Do your analytics capabilities employ association and affinity grouping or clustering and classification models? These are fairly sophisticated modeling techniques that rely on accurate information to make accurate predictions. It is vital to have the right information in the most accurate form available to supply these functions.

The final focus area, data delivery, is perhaps the most critical, because this is where all your work will be on public display. How, and how well, users are able to access the data they need and the format in which that data is delivered to them will play a large role in determining how the EDM solution is perceived throughout the company. If people can't access the data they need, in a user-friendly manner, your EDM solution will be perceived as ineffectual at best.

Therefore, it is very important early in the design process to look at future reporting needs and how they will be met with "slice and dice" technologies such as online analytical processing (OLAP) and relational online analytical processing (ROLAP). It is also important to determine what types of data visualization tools (such as exploration, outcome evaluation, hypothesis generation, etc.) you will need. Next, you'll need to look at user access (interfaces, reporting formats, and data access methods for both structured and unstructured content) and how that access will be provided and maintained while balancing needs, security and ease of use.

Once you understand the scope of the EDM solution, what data you will need, and how you will need to present it to users, you can implement, right? Not quite yet. Implementation challenges are inherent in an IT project. Some are technical; some are political. In my experience, there have been three challenges that companies consistently face and must overcome to have an effective EDM implementation. They are: privacy versus information needs, interoperability issues and data quality problems.

Figure 3 represents the challenges that are inherent in most EDM projects.

Figure 3: EDM Challenges and Key Objectives

One key goal of effective EDM is to enable the company to provide better customer service. For instance, customization of services is high on many marketing/salespeople's wish lists. However, it is important to balance the use of technology to provide more personalized services against making customers feel that their privacy has been violated. For example, it is probably ethically okay to drop Internet cookies to remember customers' names and Web site preferences. However, using the information to send them unwanted advertising and emails gets a little hazy and risky.

The key to overcoming this challenge and achieving the privacy versus information needs balance is to first stay aware of legislative limits - e.g. the CAN-SPAM Act of 2005. Then, before you deploy a new technology, weigh the benefits of business benefits versus potential loss of business and/or liability issues due to perceived privacy loss.

With the flood of new technologies on the market, interoperability issues also often plague EDM projects. However, most analytical platforms and solutions are making strides toward open architectures and interoperability. Industry leaders are also beginning to call for, and actually set interoperability standards so that solutions from multiple vendors will more likely be compatible with each other, thus facilitating more sophisticated business intelligence/analysis capabilities. The challenge is to make effective use of the burgeoning standards to build the most appropriate solution from all the available components.

To address this challenge, it is crucial to build an open analytics platform into the design of the overall EDM architecture. It is also essential to develop an integrated data quality program that will enforce data quality rules across platforms and applications. Finally, with all the disparate platforms you will be working with, it is critical to develop detailed data security requirements and a plan to meet those requirements. The last thing you need is a security breach caused by holes left by cobbling different platforms together.

The third challenge that I've seen wreak havoc on EDM projects is data quality. Poor data quality will bring an EDM solution to its knees very quickly. The number one data quality problem that many companies have is inaccurate data which provides inconsistent answers and no single version of the "truth" about organizational information. This leads to a loss of confidence in overall data quality.

To address this challenge, it is important to standardize business and data processes across the enterprise. This will help provide the necessary confidence and speed for the enterprise-wide use of business intelligence/analytical technologies. It is also essential to develop (or streamline your existing) data model to enhance solution scalability and performance.

I know this column has been a tome! But, its length is simply reflective of the wide-ranging scope of issues you will be dealing with in an EDM initiative. Because an EDM initiative is truly enterprise-wide in scope, its implications ripple throughout just about every department and function of the company. To increase the likelihood that you will have an effective implementation, it is important to not only understand the issues and challenges inherent in an EDM initiative, but also to develop sound strategies to overcome them. I hope I've provided you with a helpful starting point!

This publication contains general information only and Deloitte Consulting LLP is not, by means of this publication, rendering business, financial, investment, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte Consulting LLP, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication.

...............................................................................

For more information on related topics visit the following related portals...
Data Management and Enterprise Achitecture.

Rich Cohen is a principal in Deloitte Consulting LLP's Information Dynamics practice where he is responsible for the strategy, development and implementation of data governance, data warehousing, decision support and data mining engagements to support the emergence of world-class business intelligence applications. Cohen has more than 27 years of experience in the design, development, implementation and support of information technology in a variety of industries. Over the last 18 years, he has had extensive experience in the creation of technology strategies, implementations and deployment of CRM and business intelligence solutions to drive improved business performance.

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