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An Introduction

Published
  • September 01 2003, 1:00am EDT

It's time to breathe a sigh of relief. You've deployed a successful data warehouse with terabytes of data available to hundreds of business users. You've implemented the reporting and analysis capabilities of your enterprise resource planning (ERP) tools and your business users are improving their productivity by using the customer relationship management (CRM) system you deployed. Your ROI has never been better.

Think you should pat yourself on the back? Think again. You might just need to kick yourself in the pants instead.

Take a look at these red flags before they reach out and trip you. Business users are still creating and using business applications in Microsoft Excel and Microsoft Access, inputting data from multiple databases or applications. They're creating their budgets, business plans, sales reports and profitability analysis from these homegrown applications. No one agrees as to what the "numbers" really are. Despite the terabytes of data stored on new databases with the latest reporting and analytical tools (that you worked so hard to create), the users are still pulling in data from other sources.

Why haven't the users found the "single version of the truth" in your enterprise data warehouse or data mart? Is it their fault or could it be your fault?

The answer lies in the successful deployment of data warehouses, data marts, ERP reporting, CRM and other business analytics packages. Each one of these business or IT initiatives made something better, but also made something worse: new data "stovepipes." It's not uncommon for companies to have multiple data warehouses developed at different times by different groups. Many companies have multiple data marts for tactical business solutions that increase business ROI, but the downside is that they also create more information silos. ERP, CRM, SCM and other business analytic applications improve business processes, but usually at the expense of an overall information strategy.

It's no secret to IT managers that enterprise information is not treated as a strategic asset. To add to the frustration, this means that no comprehensive information architecture and management strategy is designed and followed. It's not that IT isn't trying to convince senior management that information is a strategic asset; however, it can be difficult to quantify the business benefit. Additionally, sometimes it's impossible to incorporate the information architecture in your plans when deploying various business applications.

So how do you turn data into a strategic asset? How do you enable your business users to find the "single version of the truth?" Integrate data across the enterprise.

To do this, IT needs to understand the basics of data integration and needs to accomplish this integration quickly and completely.

Each month, I'll approach the topic of data integration and turning data into a strategic asset from a few perspectives.

Next month, I'll explain an approach to data integration efforts using a term I coined: Data Integration Framework (DIF). The DIF is a combination of processes, standards, people and tools used to transform enterprise data into information for tactical operations reporting and strategic analysis. The tendency is for people to equate data integration with an extract, transform and load (ETL) tool. Although ETL tools can automate functions and improve developer productivity, they are only one component within a DIF. Oversimplifying data integration creates the impression that it is quick and easy if your buy the right tool. This creates false business hopes. It makes it more difficult to explain why you have multiple information silos and no "single version of the truth."

The DIF is a blueprint and a set of guidelines to transform data into consistent, quality, timely information for your business people to use in measuring, monitoring and managing your enterprise.

In the subsequent months, I'll examine specific applications of data integration and how you might approach these opportunities. I'll review the data integration aspects of each of these topics: ERP business intelligence solutions and custom data warehousing; enterprise performance management (EPM) and analytical applications; data mart consolidation; meta data; and real-time data warehouses. I will also suggest ways to put those efforts into the context of your overall data integration efforts. In addition, I'll discuss the impact of enterprise application integration (EAI), Web services and enterprise information integration (EII) on your data integration efforts. Do they help or hinder those efforts?

Finally, we'll discuss how to justify, obtain business sponsorship and implement your data integration efforts. In today's economy, IT needs to deliver short-term wins and ROI to their business, but data integration is never a quick-hit project. How do we balance these apparently conflicting objectives? How do we explain the business benefits and justify data integration efforts? How do we deliver the short-term projects within the context of a data integration program?

Please feel free to e-mail me with your suggestions for topic areas and your feedback on these columns at rsherman@athena-solutions.com.

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