Demands by users of business intelligence (BI) applications to "just get it done" are turning typical BI relationships, such as business/IT alignment and the roles that traditional and next-generation BI technologies play, upside down. As business users demand more control over BI applications, IT is losing its once-exclusive control over BI platforms, tools, and applications. It's no longer business as usual: For example, organizations are supplementing previously unshakable pillars of BI, such as tightly controlled relational databases, with alternative platforms. Forrester recommends that business and IT professionals responsible for BI understand and start embracing some of the latest BI trends — or risk falling behind.

Traditional BI approaches often fall short for the two following reasons (among many others):

• BI hasn't fully empowered information workers, who still largely depend on IT
• BI platforms, tools and applications aren't agile enough

This is a very smart move by Oracle. Until the Siebel and Hyperion acquisitions, Oracle was not a leader in the BI and analytics space. Those acquisitions put them squarely in the top three together with IBM and SAP. However, until this morning, Oracle played mostly in the traditional BI space: reporting, querying, and analytics based on relational databases. But these mainstream relational databases are an awkward fit for BI. You can use them, but it requires lots of tuning and customization and constant optimization — which is difficult, time-consuming, and costly. Unfortunately, row-based RDBMSes like IBM DB2, Microsoft SQL Server, Oracle, and Sybase ASE were originally designed and architected for transaction processing, not reporting and analysis. In order to tune such a RDBMS for BI usage, specifically data warehousing, architects 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.

I need your help. I am conducting research into business intelligence (BI) software prices: averages, differences between license and subscription deals, differences between small and large vendor offerings, etc. In order to help our clients look beyond just the software pricese and consider the fully loaded total cost of ownership, I also want to throw in service and hardware costs (I already have data on annual maintenance and initial training costs). I’ve been in this market long enough to understand that the only correct answer is “It depends” — on the levels of data complexity, data cleanliness, use cases, and many other factors. But, if I could pin you down to a ballpark formula for budgeting and estimation purposes, what would that be? Here are my initial thoughts — based on experience, other relevant research, etc.

• Initial hardware as a percentage of software cost = 33% to 50%
• Ongoing hardware maintenance = 20% of the initial hardware cost
• Initial design, build, implementation of services. Our rule of thumb has always been 300% to 700%, but that obviously varies by deal sizes. So here’s what I came up with:
  • Less than $100,000 in software = 100% in services
  • $100,000 to $500,000 in software = 300% in services
  • $500,000 to $2 million in software = 200% in services
  • $2 million to $10 million in software = 50% in services
  • More than $10 million in software = 25% in services
• Then 20% of the initial software cost for ongoing maintenance, enhancements, and support

Thoughts? Again, I am  not looking for “it depends” answers, but rather for some numbers and ranges based on your experience.

By Boris Evelson and Rob Karel

Our latest BI solution center (BISC, which in our definition is more than a BICC/BI COE) report is now live on the Forrester website. Here’s a brief summary.

Forrester firmly believes that tried and true best practices for enterprise software development and support just don’t work for business intelligence (BI). Earlier-generation BI support centers — organized along the same lines as support centers for all other enterprise software — fall short when it comes to taking BI’s peculiarities into account. These unique BI requirements include less reliance on the traditional software development life cycle (SDLC) and project planning and more emphasis on reacting to the constant change of business requirements. Forrester recommends structuring your BISC along somewhat different lines than traditional technical support organizations.

Earlier-generation BI support organizations are less than effective because they often

• Put IT in charge
• Remain IT-centric
• Continue to be mostly project-based
• Focus too much on functional reporting capabilities but ignore the data

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.