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Assessing the Need for Data Warehouse Environment Modernization

At what point does it make sense to pursue a DW renovation program, and how can you effectively communicate its value?

From a business intelligence, reporting, and data warehousing perspective, every organization balances two (at times) conflicting sets of demands. On the one hand, an enterprise must ensure that its existing system applications (upon which its business relies) continue to operate without obstruction or delay. On the other hand, and enterprise wants to embrace the right technologies to support emerging expectations for reporting and analysis. The challenge is that unless your enterprise is going to develop a new data warehousing environment from the bottom up, you are most likely going to have to modernize your existing environment.

There are efforts and costs associated with a modernization project, and senior managers might raise questions about the wisdom of any plan for modernization unless you can demonstrate clear value associated with its outcomes. Therefore, at what point does it make sense to pursue a renovation program, and how can you effectively communicate its value?

In my last article, I discussed three different perspectives to understanding the motivations for modernization. We can adopt those three perspectives – the business, technical, and strategic perspectives – to articulate more precise, measurable criteria for triggering the design and development of a modernization plan. In turn, specifying levels of acceptability for clearly defined, measurable factors enables the development of a coherent and persuasive argument for funding the resources necessary to renovate the BI and analytics environment.

From the business perspective, there are two operating principles: satisfying existing business needs and satisfying anticipated business needs. The most conventional approach has been that BI/analytics consumers rely on reports for descriptive analytics, shedding light on the past (what has happened), and a growing number of users are adopting predictive and prescriptive analytics techniques to develop models that can help enterprises anticipate opportunities and guide their future actions.

Therefore, business expectations can be grouped into four categories. The first is the sets of descriptive analytics expectations, including operational, regulatory, and financial reports for existing business requirements and for the anticipated future business requirements. The second includes predictive analytics expectations for existing business requirements and those for anticipated business requirements. Engage the business users to identify, list, and categorize these four types of requirements.

For each of these listed requirements, indicate whether the existing BI/data warehousing platforms can meet those requirements using a scale of “Currently meets the needs,” “Can easily be adapted to meet the needs,” “Can be adapted with difficulty to meet the needs,” or “Cannot be adapted to meet the needs.” This assessment will highlight gaps in the existing platform architecture’s ability to address the business’s expectations. Each of these assessments can also be aligned with the corresponding costs and resource requirements to adapt the environment to support business needs.

In some ways it is much easier to perform the assessment from the technical perspective because we can focus on aspects of capability and performance. Capability encompasses the features of the technology; performance incorporates an array of dimensions such as query response time, processing load, data volume capacity, and scalability.

As with the business perspective, it is worth enumerating a list of the desired technical capabilities and indicating whether the existing BI/data warehousing platforms provide those capabilities. Then, list the critical performance dimensions and measure the existing system’s corresponding levels.

Finally, the strategic perspective can be assessed in terms of alignment with the way emerging and maturing technology innovations could be used to improve business value. In some cases, the opportunities are obvious. For example, you could use Hadoop as a low-cost way to expand data availability through data warehouse augmentation. However, you must balance your plan to adopt new technologies with the organization’s ability to execute your plan. Consider the process of developing the business justification, gaining approval, and then scoping, staffing, and implementing the plan for technology adoption, and make sure your schedules can be properly aligned with your business needs. This is more a qualitative assessment than the quantitative approach we explored for the two other perspectives.

A review of the assessment should go beyond indicating whether the existing environment needs to be renovated. It also provides the basis for developing the business justification and simultaneously sets expectations for the features, capabilities, and performance levels of a proposed modernized environment.

[Editor's note: The discussion continues here.]

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader, TDWI instructor, and expert consultant in the areas of data management and business intelligence. You can contact the author at [email protected].

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