The following is a brief overview of several of the hot topics in data management today and discuss how a high-level data model can help with these initiatives, many of which are siloed projects or programs. Programs are large ongoing efforts that have a begin date and, if they are successful, no end date. They require long-term participation from many different sections of the business. Each program contains many bite-sized pieces called projects. A project is a well-defined effort with a begin date, end date and solid deliverables that contribute toward the goal of the program.
Due to the broad scope of program initiatives, very few people (if any) understand the big picture – how the initiative will fully impact the business and how each section of the business, such as accounting or sales, will need to be modified to deliver the objectives of the program. What’s needed is a simple, yet broad and complete picture showing how concepts cross departments so that the full impact of these large programs can be planned for and prioritized.
One benefit of using a high-level data model that spans all of these initiatives is integration. The high-level data model, with its cross-department focus, is a perfect communication tool for planning, scoping, impact analysis and project status for each of these large initiatives.
Business Intelligence and Data Warehousing
As organizations realize how data can be a strategic differentiator, more BI initiatives are born. The Data Warehousing Institute defines BI as the processes, technologies and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business action. Because of its business focus, BI is one of the initiatives with the greatest involvement of business personnel in the organization. Many businesspeople create their own BI reports.
Included in BI are the software, hardware, network, data models, process models, reports and code to take raw data and turn them into something on which people can base decisions. For example, a manufacturing company can take millions of sales transactions and summarize them into a monthly sales figure. This turns data into information. Monthly sales this year can then be compared against monthly sales from last year for the same month, providing knowledge to the user of the information. This knowledge can lead to plans for how much to produce next year, which will hopefully lead to more money, less waste or a better world with more ice cream.
The backbone of any BI initiative is a data warehouse. If a BI report is a flashy sports car, the data warehouse is the engine. A data warehouse is the central storage point for all of the relevant information that is needed for BI reports; and turning data into information is no small feat. A single piece of information on a report, such as total sales, can involve the aggregation of hundreds of database tables from multiple geographic and functional areas. And each of the data sources can have different business definitions and physical structures. A major part of the effort of creating a data warehouse is obtaining the big picture of what data exists, how it is defined and what the end result should look like. This is where a high-level data model can come in handy.
Common language. A high-level data model provides a single agreed-upon set of concepts, definitions and business rules. This language must be consistent across the scope of the model. If the scope of the high-level data model is the data warehouse itself, concepts such as Customer and Gross Sales need to be defined consistently. Customer, as defined by accounting, must be consistent with the sales department definition of Customer.
Common language enables the reader of the model to understand where the organization is striving to go, as well as conceptually how to get there from the existing environment. This common language has a direct benefit for the many users of the data warehouse, who can confidently interpret the concepts the same way.
Impact analysis. The high-level data model can be an effective tool for determining overlap and touchpoints. An overlap is when two or more different development teams are impacting the same concept, such as when two different development teams are both updating customer information. A touchpoint is when two or more different development teams need to connect with each others’ work. For example, when one development team is working on Product and another development team is working on Order, which has a dependency on Product. The link from Order to Product must be managed successfully.
A high-level data model can represent the entire data warehouse. Color is one technique for effective model layout that could be used to indicate to management where the touchpoints and overlaps exist for a particular phase of the data warehouse program. For example, a red concept Customer could indicate that at least two development teams are working on this same area for the same release date. A large bold relationship line between Customer and Order could indicate that in this release, there is a touchpoint that exists between these two concepts. The high-level data model can be updated during the lifecycle of the development effort to indicate successfully managed touchpoints and overlaps, as well as those that are experiencing problems.
Scoping and prioritization. Color can also be used to indicate which part of the BI program will be developed first, second and so on. For example, those concepts shown in green will be implemented as part of Customer Profitability by first quarter next year. In every project status meeting, this BI high-level data model can be presented to show progress since the last status meeting at a concept level. The model, therefore, becomes a part of every status document.
