A prototype Methodology for creating Unified Business Intelligence
The following is only a skeleton outline of issues necessary to navigate across
BI domains and achieve critical and valuable symmetries. The facilitation of these
methodologies and meetings should be in the hands of an experienced Business Intelligence
consultant who understands the enterprise IT paradigms in play, issues of data governance
and turf protection, and can adroitly develop consensus around mutual benefits from
discovering that an information whole is much greater than the sum of its data parts.
This integration is done model by model. This means we don't just merge data hoping
someone will use it for insights. It means that we analyze in detail what data exists
in specific Warehouse domains and specific data mart domains to solve problems and
find competitive or profitable opportunities presently impossible.
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• Contextual meeting
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◦ Although merging metrics across marts and data warehouses happens one model at
a time, it is important to hold an orientation session with high level stakeholders
just as in every consulting engagement to build consensus, objectives, and set expectations,
before looking at specific models.
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◦ This meeting introduces contexts, looks at other industry examples, brainstorms
about possible successes of cross domain data sharing in this company, etc.
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◦ Data governance is discussed with the highest level executives available. It is
important to get agreement in principle that data will be shared among lines of
business and and within and across marts and warehouses.
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• Once principles are agreed upon and a decision is made to look at specific opportunities,
sessions are convened with LOB and Mart and Warehouse operating personnel. We begin
to look at specific opportunities and specific repositories for synergistic metrics.
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◦ Audience for
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◦ Important questions to guide Brainstorming sessions:
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▪ What are you doing right?
▪ How are you doing it? What are the data sources used?
▪ How can we do more of it?
▪ What could you know if we looked at metrics across the gap?
▪ What you don't know can hurt you.
▪ What specific opportunities could you address that you can't now because you are
told the data is not available?
▪ What specific problems can you not now address because you are told the data to
do so is not available?
▪ What do you now not know that you now need to know for increased
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• Profitability
• Competitiveness
• Efficiency
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▪ What totally new businesses opportunities are there for
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• New business initiatives
• Competitive advantages
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• Sourcing metadata for model meetings. The role of data owners becomes important.
Who knows what data exists and where to answer the specific opportunities and problems
identified above. We are looking for data to be shared across warehouse and mart
domains, since synergies across these domains can be such powerful and unique solutions.
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◦ You are paying the price of maintaining these isolated data pools
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◦ Sharing model by model enhances the profitability of these stores.
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◦ What are opportunities for wins by merging metrics across these domains?
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• Discuss tooling
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• Discuss resources
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• Discuss project ownership, prototyping, proof statements, stakeholders, vendor
relationships and cooperation and related matters of implementation.
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• Create a high level project plan. The rest is standard project management, executive
commitment, stakeholders communications, and the like.
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