Page 253 - Data Architecture

Chapter 6.4: Introduction to Data Vault Methodology
• Customer-focused
• Total employee involvement (within the reach of the enterprise BI team and business users)
• Process-centered
• Integrated system
• Strategic and systematic approach
• Continual improvement
• Fact-based decision-making
• Communications
http://asq.org/learn-about-quality/total-quality-management/overview/overview.html

It is clear by now that TQM plays a vital role in the success of the data warehousing and
BI projects. TQM is aligned (as previously described) with the desired outcomes of

CMMI, Six Sigma, Agile/Scrum, and DAD.

The Data Vault 2.0 methodology is process-centered, provides for an integrated system,

is a strategic and systematic approach, requires total employee involvement, is customer-
focused, and relies on transparency and communications. The Data Vault 2.0 model
brings fact-based decision-making to the table, rather than “truth” or subjective-based
decision-making. The other part of the fact-based decision-making is impacted by the
collected KPAs and KPIs in the enterprise BI project (don’t forget, these are a part of the
optimization steps in CMMI level 5).

As it turns out, accountability (both for the system as a whole and the data living in the
data warehouse) is a necessary part of TQM as well. How is this possible? TQM is
customer-focused; the customer (in this case, the business user) needs to stand up and
take ownership of their data (no not their information but their data).

The only place in the organization that these data exist in raw form integrated by business
key is in the Data Vault 2.0 data warehouse. It is precisely this understanding of facts that

draws the business users’ attention to Six Sigma metrics—demonstrating quantitatively,
the gaps between business perception of operation, and business reality of data capture
over time.

Addressing these gaps by filing change requests to the source systems or renegotiating the
SLAs with the source data provider is part of the TQM process and part of reducing TCO
and improving data quality across the enterprise. TQM plays a role in enriching the BI
ecosystem, if and only if the business users are forced to be accountable for their own
data and decide to engage in gap analysis (the old-fashioned way) by leveraging statistics
that show where and what percentage of their current business perception (business
requirements) are broken. The DV2 methodology provides pathways in the project that
the teams and business users can follow to achieve these results.
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