Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 2


Click to learn more about author Wayne Yaddow.

In Part 1 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their user base for BI, analytics, and data discovery so that users will reach informed decisions.

This series of mistakes to avoid focuses on
helping organizations sidestep quality issues that are too common on DW
projects. Tips offered here will aid in assuring satisfaction as DW teams plan
and implement new functions and capabilities.

Falling Short in Reviews of DW/BI Project Requirements and Technical Designs

quality assurance is an approach to DW development where quality
assurance is performed early and frequently in the lifecycle — to the left of
its usual position on the overall project plan. “Shift left” refers
to dynamic Data Quality verifications and static testing: conducting reviews, inspections,
and unit and integration testing. However, many DW projects have not begun to
embrace this potentially important process.

Shifting the verification process to the left during
the project lifecycle is an agile practice that delivers a means to
verifications with (or in parallel to) design and development activities. That
is, development and QA teams collaborate to plan, manage, and execute tests that
accelerate feedback of issues to the business and developers.

When quality assurance assignments are practiced earlier in the project lifecycle, DW
architects, business and data analysts, and ETL and BI report programmers
assume “testing” roles to identify issues. When implemented early, validations
to focus on business rules and security — and even acceptance tests — can be a
tangible benefit for project quality and success.

Spending weeks or months at the end of a
release cycle to find and fix issues is inefficient. An IBM study reported that
“it is 100 times less expensive to fix a defect early than after release to production.” That calculation
alone should get most teams behind shift-left testing.

Another goal of shift-left testing is to fix
issues that might emerge in the future, even in production. Therefore, when
organizations adopt a shift-left strategy, they can test, analyze progress, and
pass judgment on the system, much better bit by bit rather than all at one time.

Neglecting to Identify Best Practice Test Processes That
Verify Data Transformations and BI Reports

The majority of testing for many (perhaps most) DW projects
is performed by running SQL scripts and then gathering the results in
spreadsheets for further analysis.

That approach to ETL testing can be slow and error-prone.
Performing DW tests manually without dedicated process-supported tools to
design and manage those tests may thwart test automation in the near and long term.

Repeated testing is vital to ensure a high level of ETL Data Quality. The more you test, the more bugs you will discover before going live. It’s crucial for business intelligence projects. When users can’t trust the data, the adoption of BI or analytics solutions will be in jeopardy. Implementing advanced ETL testing processes will support frequent regression and smoke testing.

The decision to implement cutting-edge processes and tools
for ETL testing depends on a budget that supports spending to meet advanced
testing requirements. Test tools built and maintained in-house are likely to be
better than no test tools at all. Many ETL tool providers offer test solutions
directly focused on the ETL tool (ex., Informatica’s Data Validation Option —

A few of many notable test processes include:

  • Source-to-Target Data Test Verifications:
    Record counts, duplicate data checks, data transformation tests, regression
    testing, smoke testing
  • Load-Testing Processes: Tests that
    realistically simulate an appropriate number of users to validate application
    infrastructure for performance, reliability, and scalability

Enlisting Third-Party and In-House Testers with Inadequate
DW Testing Skills

The impulse to cut DW project costs is often intense, especially in late project phases. A common error is to delegate numerous testing responsibilities to resources with limited business and data testing skills. DW project leads and other hands-on testers are frequently expected to demonstrate extensive experience designing, planning, and executing database and data warehouse test cases.

In recent years, DW
projects have experienced a trend toward business analysts, ETL developers,
outsourced testers — and even business users — for planning and conducting much
of the QA process. Doing so can be risky.
The following are among the skills frequently required for DW testing:

  • In-depth understandings of the business,
    DW technical requirements, business process, and business terminology
  • An ability to develop strategies, test
    plans, and test cases specific to data warehousing and the enterprise’s
  • Skills for deploying code to databases
  • Skills for troubleshooting ETL sessions
    and workflows
  • A firm understanding of DW and database
  • Advanced skills with SQL queries and

Data warehousing
projects can fail for many reasons: poor Data Architecture, inconsistent data definitions,
lack of features to combine data from various data sources, missing or inaccurate
data values, inconsistent use of data fields, unacceptable query performance, and
so on.

Most crucial project
risks and failures are reduced when
well-trained and experienced testers provide ongoing support from the earliest
phases of development through project completion.

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