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February 1, 2003

Where's Your Backbone?

Effective enterprise data delivery requires more than just a traditional data warehouse

by Kevin Jahn

Continued from Page 1

This engine relies on "smart" data constructors that have a specific and highly tuned purpose for servicing data connections to the requested data source. Overall, the enterprise data delivery engine serves as the primary mechanism for data integration and report delivery across the enterprise, across multiple companies, or across several departments.

Two fundamentally distinct strategies for enterprise data delivery are pervasive in the industry: A data consumer can request information expecting near immediate results or subscribe to information expecting results based on events or changes in data. A complete architecture of enterprise data delivery should offer both of these models; the primary technical goal is to get relevant information to end users regardless of the accessing device or application (such as portals, mobile devices, browsers, email, and so on).

On the Front End

Data visualization and reporting are the primary front-end components within the data cycling backbone. These components let you categorize data into manageable business content by working in coordination with the enterprise metadata and data cycling repository.

On the front end, data visualization supports many data manipulation techniques that are created based on the type of data or application. Some examples might be spatial data, 2D or 3D grids, charts, graphs, multidimensional pivoting, or formatted reports. These techniques allow you to cast reporting in a new light by easily viewing data through analytic tools, personalized digital dashboards, portals, mobile devices, Web browsers, or other digital means.

As with any enterprise system, a configuration utility has to tie everything together. Thus, an enterprise data cycling backbone should contain a cycling backbone repository that becomes the central reference data for all activities involving data delivery to data consumers and can assist in categorizing or tracking decision points.

Ultimately, data consumers who request or subscribe to information drive data delivery. There are many forms of data consumers as information flows through the front, mid, and back office. For example, a data consumer could be a business user who needs to monitor production control and inventory levels, a data analyst who examines information and builds reports using spreadsheets, or even other applications. The high volumes of available data should be refined and cycled based on the data consumer's precise needs. That's what transforms data into actionable information and allows decision makers to measure the key elements that really matter.

Regardless of the tool, specialized skills are still required. A critical core competency within any data management group is the ability to take an enterprise level view of business needs on a continual basis while building data systems iteratively to meet immediate needs. A good balance of operational support, IT optimization, and business process analysis is ideal, but usually difficult to attain.

The Data Remains the Same

A successful delivery strategy keeps the entire BI life cycle in mind at all times without getting hung up too long in any one stage. Don't underestimate the value of a good enterprise architecture process that clearly outlines data delivery, data management, analytics, and BI strategies and architectures.

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Handle the implementation of a production data cycling backbone in a structured manner, just as you'd approach any strategic IT investment or project in your company. (See the sidebar, "Critical Success Factors," on page 1) Customer requirements analysis and solution assessments are necessary to ensure the solution meets business needs. The low-hanging fruit — immediate opportunities for success within your organization — can be satisfied very quickly, but in many cases, they are overlooked due to the focus on immediate operational issues or software evaluation and selection within the IT department at an inappropriate time.

BI, analytics, decision management, business performance management, and dashboards — the buzzwords change, but one thing remains the same: the data. An important objective of a data cycling backbone architecture is to increase data consumer or user confidence in the data. After it's introduced and implemented, this cycling engine should be embedded into the everyday business process of decision makers. It should be able to deliver actionable business metrics, apply rules, and enable automated or faster decision-making that can help business users determine the best course of action for any given problem or opportunity.

You have to be smart about building a data solution, using building blocks that provide a strong set of reusable components. With the right approach, you can help business users access or receive data with confidence and achieve greater economies of scale and effectiveness.

Kevin Jahn [kevin.jahn@entouch.net] is an independent BI architect and strategist based in Houston. He assists clients in creating BI, enterprise data management, data delivery, and data warehousing solutions.