It's The Dawning Of The Age Of BI DBMS

Over the years we’ve learned how to address the key business intelligence (BI) challenges of the past 20 years, such as stability, robustness, and rich functionality. Agility and flexibility challenges now represent BI’s next big opportunity. BI pros now realize that earlier-generation BI technologies and architecture, while still useful for more stable BI applications, fall short in the ever-faster race of changing business requirements. Forrester recommends embracing Agile BI methodology, best practices, and technologies (which we’ve covered in previous research)  to tackle agility and flexibility opportunities. Alternative database management system (DBMS) engines architected specifically for Agile BI will emerge as one of the compelling Agile BI technologies BI pros should closely evaluate and consider for specific use cases.

Why? Because fitting BI into a row-oriented RDBMS is often like putting a square peg into a round hole. In order to tune such a RDBMS for BI usage, specifically data warehousing, BI pros usually:

  • Denormalize data models to optimize reporting and analysis.
  • Build indexes to optimize queries.
  • Build aggregate tables to optimize summary queries.
  • Build OLAP cubes to further optimize analytic queries.
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Big Data Survey

Forrester is in the middle of a major research effort on various Big Data-related topics. As part of this research, we’ll be kicking off a client survey shortly. I’d like to solicit everyone’s input on the survey questions and answer options. Here’s the first draft. What am I missing?

  1. Scope. What is the scope of your Big Data initiative?
    1. Enterprise
    2. LOB
    3. Departmental
    4. Regional
    5. Project-based
  2. Status. What is the status of your Big Data initiative?
    1. In production
    2. Piloting
    3. Testing
    4. Evaluating
  3. Industry. Are the questions you are trying to address with your Big Data initiative general or industry-specific?
    1. General
    2. Industry-specific
    3. Both
  4. Domains. What enterprise areas does your Big Data initiative address?
    1. Sales
    2. Marketing
    3. Customer service
    4. Finance
    5. HR
    6. Product development
    7. Operations
    8. Logistics
    9. Brand management
    10. IT analytics
    11. Risk management
  5. Why BigData? What are the main business requirements or inadequacies of earlier-generation BI/DW/ET technologies, applications, and architecture that are causing you to consider or implement Big Data?
    1. Data volume
      1. <10Tb
      2. 10-100Tb
      3. 100Tb-1Pb
      4. >1Pb
    2. Velocity of change and scope/requirements unpredictability
    3. Data diversity
    4. Analysis-driven requirements (Big Data) vs. requirements-driven analysis (traditional BI/DW)
    5. Cost. Big Data solutions are less expensive than traditional ETL/DW/BI solutions
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