So your company has a data warehouse; or perhaps a large number of data marts and the need to integrate these into a common data warehouse. The data warehouse or marts were most likely implemented to address one or more specific business issues or opportunities, perhaps with the expectation to expand and integrate into an integrated data warehouse in the future. In fact, everybody has to start somewhere. The next step is to evolve and grow the data warehouse to support other uses, toward an integrated data warehouse, spanning multiple areas of the business.
The business must obtain value from the data warehouse, and what makes an integrated data warehouse extremely valuable to the business is when the users start gaining new and actionable insight – information, knowledge, or wisdom – not previously available. Identifying this insight ahead of time and identifying the potential value of the data warehouse can be very difficult. How can we know what the insight would be beforehand? Why is it so difficult to predict and quantify benefits? But more importantly, what can the business do to get smarter in this area?
Teradata has explored this question. The result is a method of visual modeling to support understanding and planning for the integrated data warehouse. All this is based on our years of experience in implementing data warehouses across many industries, including transportation and logistics. Good planning will facilitate evolution of the data warehouse as it travels the route to endless new and actionable insight for the business. This visual planning model is called the Data Integration Roadmap. There are many industry Data Integration Roadmaps currently developed that support the retail, communications, financial services, healthcare, insurance, travel and hospitality, transportation and logistics, and manufacturing industries.
What is a Data Integration Roadmap?
Let's discuss the two key elements: data integration and a roadmap. The first term refers to an integrated data warehouse that is a central repository for significant parts of an enterprise’s data. Its use extends beyond a specific department or single group of users. Also, its construction tends to be iterative and constantly evolving for new uses.
The other term is the roadmap. It is borrowed from our love of the automobile and the open highways. Most of us are well aware of the time and money that can be saved by having a resource that can assist in getting us to our destination, while leaving the exact route flexible to change.
"Store once and use many" is a concept integrated data warehousing strongly supports. The intent is that once data, also called business facts, are stored in a central repository, they can be made available for multiple uses. Two benefits come from that action. One is that the cost of harvesting and cleansing the data is only incurred once. The second is that a single view of the enterprise can be supported. Once a critical mass of data from various sections of the business is combined, new insights (information) about the business also become available. These insights tend to be in addition to the initially planned uses; they are freebies. And they lead to the new and actionable insight that fuels competitive advantage and great value.
A problem with all of this is that building an integrated data warehouse can be expensive and challenging. The business value analysis becomes complex, as most projects have one business case and, therefore, one associated return on investment (ROI). How can we take the many projects targeted to produce actionable information and then combine and align them with the goals and objectives of the enterprise? And if that could be done, how can we separate that value from the additional value of previously unidentified uses, based on the enterprise approach?
While it is complex, expensive, and challenging, it seems reasonable that the rewards justify the challenge. But how can we get smarter about aligning the challenge with the payback? This is the focus of the Data Integration Roadmap. The balance of this paper describes a model built to illustrate the concepts and values that an integrated roadmap can address. The roadmap contains the routes and associated food chain interconnections. The path chosen is based on goals and perceived business values. And, of course, change is inevitable. Therefore the ability to use a roadmap and then model the impact of change will help you maximize return on your integrated data warehouse.
Figure 1. Data Integration Roadmap
Can it be done? A few brief years ago, probably not. Today, yes.
The Data Integration Roadmap Model
How is the Data Integration Roadmap constructed, and how can it show the linkage from the highest strategic levels of a company down to the basic business facts as captured in operational data? The following description is a high-level overview of the roadmap and primarily describes the linkages. The actual model carries much more detail within the model object properties and business rule relationship definitions.
In Figure 2, the upper top block captures a typical customer’s vision, the linkages to the goals for achieving that vision, and strategic objectives linked to the goals they support. A relative weight is identified for each goal. The weight of the goal is then used to calculate a weight for the strategic objectives. The strategic objectives are decomposed into more
granular Business Improvement Opportunities (BIOs), which identify specific areas of the business where additional business value or business impact can be achieved from the integrated data warehouse.
Figure 2. Business Directions linked to Business Improvement Opportunities
The strategic objectives are linked to the appropriate BIO Objective(lower right block) in Figure 2. The weighted value is carried to the BIO Objective, showing alignment to the company’s strategic objectives. The BIO Objective also captures the business value from the Results component of the BIO. The Results component is based on a Business Impact Model(s) as appropriate. Knowing the strategic value and business value of a BIO leads to more in-depth understanding of its impact to the business and more informed planning for the integrated data warehouse.
Figure 3. Business Improvement Opportunities
The models identify areas within a company that can be imporved by getting actionable information from an integrated data warehouse. These areas are called BIOs (Business Improvement Opportunities). A BIO consists of four parts as shown in Figure 3:
- The Objective (left side of figure) identifies a specific business problem or opportunity to be addressed by the warehouse.
- The Analysis (lower right of figure) describes the information from the data warehouse that must be evaluated to address the issue or opportunity.
- Actions (middle right of figure) describe the types of business changes to be implemented based on the insights gained doing the analysis.
- Results (upper right of figure) describe the business value (quantitative and qualitative) expected or achieved from implementing the actions. These results help drive the business justification for the data warehouse.
The model contains pre-defined BIOs applicable to an industry based on Teradata’s best practices in implementing data warehouses in many companies in the industry. These BIOs can be used as reference for building your roadmap and plan.
A primary assumption of this model is that incomplete sources of data will cause less than perfect production of actionable information. So incomplete analysis leads to less than perfect actions, and, therefore generates less than perfect business value results. As the data sources are enriched, the information improves, supporting higher value/lower cost actions, generating higher value return. This is referred to as 'Information-Enabled Value'.
Figure 4. Information-enabled Value
The Data Integration Roadmap model determines information-enabled value by calculating the total potential business value (shown in thousands) identified in the Results component of the BIO and comparing that to the amount of information available to do the analysis. The model shows what is available based on linkages from the Analysis to the required Metrics and KPIs to the required data elements to the data sources as shown in the figure below. Data sources are identified as being available or not available.
Business Questions state, in business terms, what the analysis should accomplish. They also help communicate the business requirements to the IT community. The Business Questions are linked to the Metrics and KPIs needed to answer the questions and do the analysis. KPIs and Metrics can also be linked to Dimensions that specify how the KPI and Metric results should be reported.
KPIs, Metrics, and Dimensions are linked to the specific required data elements in the Teradata industry Logical Data Model (LDM) as shown in figure 5. From the business perspective, the logical data model is structured as a set of subject areas, which encompass functional areas across the business enterprise. Further decomposition of the subject area exposes the entity and attributes that the IT staff needs to associate with the physical data model and the related operational systems supplying the data.
The data sources indicate if the data are available in the warehouse. That information gets carried up through the model to show which KPIs and Metrics can be calculated and which Dimensions are available. In the model, KPIs, Metrics, and Dimensions that are fully sourced are shown as dark green, almost sourced is yellow-green, more than half sourced, yellow, and less than half, red. Availability of KPIs and Metrics determines what Analysis can be performed; enabling actions to be taken that generate higher value return from the warehouse.
We have traversed the model from the enterprise goals and objectives to the associated BIOs to the supported Business Questions and Key Performance Indicators and the specific data attributes needed to enable them. Another way to look at the model is from the bottom up. By understanding what data are readily available from the enterprise operational support systems, you can identify what actionable information can be created. The concept of a roadmap is based on incrementally sourcing (loading) operational data into the integrated data model that has been developed for the integrated data warehouse. The roadmap allows you to make informed decisions on the proper priority and sequence of the candidate source data. As a critical mass of integrated data evolves, exponential return can be expected as multiple departments begin to leverage the business facts, applying the concept of 'store once and use many.'
If you're traveling the integrated data warehouse route, a good starting point for learning and understanding the ay is with a Data Integration Roadmap from Teradata.