By Clarity Insights,
Clarity Insights is a strategic partner to the nation's leading data-driven brands.


Transforming legacy data warehouse architecture to support real-time data processing is a massive undertaking, and it's not unusual for modernization efforts to hit some bumps along the way. To make creating a modern data architecture easier, learn from others' mistakes. Here are three of the most common mistakes companies make with their data warehouse modernization efforts:

1. Lack of coordination with other initiatives

One of the biggest mistakes a company can make with modernization is to treat it as an initiative wholly separate from other business objectives. Although architecture modernization will be driven by the IT department, it will coincide with and be impacted by other projects going on within the organization.

As TDWI director Philip Russom explained, these initiatives need to be closely aligned with dependent business goals to ensure they are laying the technological foundation to support the organization's broader objectives. This involves working with business stakeholders from the very outset to ascertain how data warehouses and applications can effectively support their goals.

Including a business-focused voice and mindset in the modern data architecture process will prevent modernization teams from working in a vacuum, and ensure their efforts provide true value to the organization.

All relevant stakeholders should be included in your modernization process.All relevant stakeholders should be included in your modernization process.

2. Making short-term choices

In many instances, modernization projects aim to bring a company's data architecture up to current standards but fail to account for future needs. A short-sighted approach to modernization minimizes the impact and value that the initiative can provide and often leaves companies ill-equipped to meet analytics demands down the road.

For instance, writing for IT Business Edge, Icreon Tech CEO Himanshu Sareen cautioned against creating a metadata layer that could facilitate certain data criteria, but not others. This could lead to poor documentation and data models, which in turn can prevent users from effectively leveraging available analytics resources.

Taking a longer view of your data and analytics needs during the design and modeling phases of your modernization project will help account for future challenges.

3. Continuing to use RDBMS in some capacity

Modernization teams know that one of the most important steps to take is converting data warehouse environments to a column-based setup. However, they may ultimately allow some row-oriented database management systems (RDBMS) to carry over from their legacy architecture.

"A columnar design can help streamline data queries."

RDBMS represents a major potential bottleneck in the data warehouse as queries are conducted by entire rows rather than by a single column. This increases the amount of time needed to search for relevant data and could introduce significant delays in analytic processes.

Although system engineers can do some workarounds to improve the performance and keep up with increasing data volume demands, these fixes represent a major investment of time, manpower and resources.

On the other hand, a columnar design can help streamline data queries and reduce search latency, without putting additional strain on data warehouse teams to manage and optimize database assets.

Modernization represents a significant investment and there's no room for error. Contact the experts at Clarity Insights today to discuss the best approach for your data architecture modernization strategy so you can avoid common mistakes.

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