Top 5 mistakes in Data Warehousing

Top 5 reasons why many data warehouse managers fail to deliver successful data warehouse initiatives:

Data Quality: Quality of source system data that is to be integrated into the data warehouse is “overrated” and thus time to resolve is “underestimated”

• Bad information in means bad information out. The CPM applications that will source data from the warehouse will suffer diminishing adoption if not addressed upstream
• Data integration strategy must include methodology to address erroneous data
• Significant level of involvement from business and IT to help resolve (decision and execution of) challenges

Data Integration: Lack of robust data integration design results in incomplete and erroneous data and unacceptable load times

• What happens when you are the process of loading data and you start receiving exceptions to what is expected? Is data rejected and you are now faced with the dilemma of partial data loads? How do you avoid manual intervention?
• What checks and balances do you have in place that ensure what you are extracting from source systems is being populated into the target? Can you audit your data movement processes to ensure completeness as well as satisfy regulatory obligations?
• Your processes can handle the data volumes you are dealing with today but can they handle the data volumes of tomorrow? How easy is it to reuse existing processes when adding additional source systems/subject areas to your Warehouse?

Data Architecture: Creating a solution that is not able to scale after an initial success will result in a redesign of the architecture

• After the first success the business will quickly want to extend the usage of the solution to a greater number of users, will the performance continue to live up to expectations?
• As users mature and adoption improves so will the complexity of information usage, i.e. more advanced queries, can the design continue to perform as expected?
• Increased usage and maturity results in the demand to integrate into the solution additional data sources/subject areas. Is the architecture easily extensible?

Data Governance & Stewardship: With no controls established around data usage, its management and adherence to definitions, data silos and erroneous reporting begin to reappear

• Stakeholders must be identified and give decision rights to help improve the quality and accuracy of your common data
• Practices around the managing of standard definitions of common data and business rules applied must be established
• Understand who is responsible for the data and hold them accountable

Change Management: Not preparing an organization to utilize what is being built results in the investment in data warehouse not being fully realized and thus deemed a failure due to low user adoption

• “Build it and they will come”; providing information access does not necessarily equate to information usage.
• Helping the business understand how they can leverage these newly available data often results in changes to the way that they work. “Day in the life of” today vs. “day in the life of” tomorrow
• Education and training programs are required
• Integrated project teams (business and IT) are essential to the success of data warehouse initiatives, with individuals becoming champions within the organization for change and adoption