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What Motivates Data Warehouse Environment Modernization?

What criteria can you use to determine when it’s time to renovate your environment and assess candidate technologies to integrate within the enterprise? Start by asking questions from three critical perspectives.

The concept of a segregated platform for data management to support business analysts’ querying, reporting, and analysis is no longer leading-edge technology. As early decision support platforms evolved into the modern enterprise data warehouse, the general reference architecture for reporting and analytics platforms has solidified to the point that automation tools can be used to simplify the design and development of newly minted data warehouses and data marts.

With the hoopla around emerging (and in some cases maturing) technologies such as data warehouse appliances, columnar and in-memory databases, and Hadoop (and its accompanying ecosystem of products), as well as growing interest in adopting more complex predictive and prescriptive analytics tools to facilitate deeper learning, you cannot help but question whether the existing data warehouse platform can continue to satisfy the business’s needs. This suggests the need for some concrete criteria that can be used to both determine when it’s time to renovate your environment as well as assess candidate technologies to integrate within the enterprise.

An approach to specifying those criteria involves examining three perspectives about the ways business intelligence (BI) and analytics applications are currently used within the organization along with the expectations for future needs. Any realistic motivations for modernizing the environment will be exposed as a result of introspection about these three perspectives:

1. The business perspective: What are the key business functions and goals that drive the need for reporting and analysis? What are the defined purposes for the existing data warehouse systems? How well does the current data warehouse platform adhere to those purposes? What are the anticipated needs over the near-term and longer-term horizons? As systems age, their ability to support emerging business requirements becomes more limited, which may frustrate business users seeking to expand their analytical investigations.

2. The technical perspective: Are there concerns about the “vintage” of the technology? Does the technology rely on a diminishing cohort of skilled practitioners? At what point are there system dependencies that are no longer easily maintained within an integrated environment? Does the current platform’s technology meet the performance expectations for current users? Is the platform capable of satisfying the anticipated reporting and analytics demand? As data volume growth continues to accelerate and interest in broadening the realm of data artifacts to be subjected to analysis increases, the existing platforms may be straining to maintain the expected level of performance.

3. The strategic/operational perspective: There is a delicate balance of platform strategy, technology adoption, and ensuring that the organization’s day-to-day processes continue to operate. Simultaneously, the adoption cycle for technology spans a number of years, particularly with the need to accommodate acquisition, system design and development, testing, and operational integration. Do the system owners and designers have a long-term strategy for meeting anticipated business needs? At the same time, do the system managers have a plan for integrating and operationalizing adopted technologies in ways that do not disrupt ongoing operations?

Asking and answering these questions will shed light on the guiding principles that can frame the discussion of information strategy and the need for system modernization. Specifically, it will help to specify the key business objectives for data warehousing and identify what technical requirements for BI and analytics should be discussed. This sets the stage for defining the metrics for alignment of business expectations with platform alternatives that can be used to establish the basis for making technology decisions.

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

About the Author

David Loshin is a recognized thought leader in the areas of data quality and governance, master data management, and business intelligence. David is a prolific author regarding BI best practices via the expert channel at BeyeNETWORK and numerous books on BI and data quality. His valuable MDM insights can be found in his book, Master Data Management, which has been endorsed by data management industry leaders.


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