Whoa! Hold your horses. If this is indeed a key challenge that you’ve tried to address in the past without much success, consider switching jobs. This is not a joke. Business intelligence (BI) is an employee market right now; a key challenge for most BI employers is finding, recruiting, and retaining top — or actually any, for that matter — BI talent. Consider that IBM BAO alone added more than 4,000 (!) BI positions in just over a year! Every other major, midsize, and boutique BI consultancy I talk to is struggling to find BI resources. So if you’ve been fighting this uphill Sisyphean battle for a while, consider new channels for your noble efforts.
Now, some more practical advice — albeit not as exciting. Start from the top down. In a few minutes I am getting ready to talk to yet another large client whose CEO does not “get” BI. Can you rightfully blame him/her? Yes and no. Yes, because how can you manage any business without measurement and insight into your internal and external processes? So if your CEO didn’t learn that in his/her MBA 101, suggest that he/she look for another job. And if you’re still standing after that and have suffered only a mild concussion, consider that many BI projects have been less than successful, and ROI on BI — one of the most expensive enterprise apps — is extremely difficult to show. So can you really blame your CEO?
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.
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
Continue to be mostly project-based
Focus too much on functional reporting capabilities but ignore the data
On my Q3 research agenda is a document reviewing typical BI software pricing configurations. Unfortunately, I find that just asking vendors whether they have this or that pricing policy (by number of named users, number of concurrent users, server type, etc.) usually just gets me “Yes, we have it all” or “It depends” answers. Not really useful. So this time I plan to nail down the vendors to three specific quotes given three very specific configurations. Here’s my first cut at the RFQ. I plan to send it out to:
All of the large BI vendors covered in our BI Wave
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.
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?
Scope. What is the scope of your Big Data initiative?
Status. What is the status of your Big Data initiative?
Industry. Are the questions you are trying to address with your Big Data initiative general or industry-specific?
Domains. What enterprise areas does your Big Data initiative address?
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?
Velocity of change and scope/requirements unpredictability
Analysis-driven requirements (Big Data) vs. requirements-driven analysis (traditional BI/DW)
Cost. Big Data solutions are less expensive than traditional ETL/DW/BI solutions
SAP BusinessObjects (BO) 4.0 suite is here. It’s been in the ramp-up phase since last fall; according to our sources, SAP plans to announce its general availability sometime in May, possibly at Sapphire. It’s about a year late (SAP first told Forrester that it planned to roll it out in the spring of 2010, so I wanted to include it in the latest edition of the Forrester Wave™ for enterprise BI platforms but couldn’t), and the big question is: Was it worth the wait? In my humble opinion, yes, it was! Here are seven major reasons to upgrade or to consider SAP BI if you haven’t done so before:
BO Universe (semantic layer) can now be sourced from multiple databases, overcoming a major obstacle of previous versions.
Universe can now access MOLAP (cubes from Microsoft Analysis Services, Essbase, Mondrian, etc.) data sources directly via MDX without having to “flatten them out” first. In prior versions, Universe could only access SQL sources.
There’s now a more common look and feel to individual BI products, including Crystal, WebI, Explorer, and Analysis (former BEx). This is another step in the right direction to unify SAP BI products, but it’s still not a complete solution. It will be a while before all SAP BI products are fully and seamlessly integrated, as well as other BI tools/platforms that grew more organically.
All SAP BI tools, including Xcelsius (Dashboards in 4.0), that did not have access to BO Universe now do.
There’s now a tighter integration with BW via direct exposure of BW metadata (BEx queries and InfoProviders) to all BO tools.
Forrester continues to see ever-increasing levels of interest in and adoption of business intelligence (BI) platforms, applications, and processes. But while BI maturity in enterprises continues to grow, and BI tools have become more function-rich and robust, the promise of efficient and effective BI solutions remains challenging at best and elusive at worst. Why? Two main reasons: First, BI is all about best practices and lessons learned, which only come with years of experience; and second, earlier-generation BI approaches cannot easily keep up with ever-changing business and regulatory requirements. In the attached research document, Forrester reviews the top best practices for BI and predicts what the next-generation BI technologies will be. We summarize all of this in a single über-trend and best practice: agility. IT and business pros should adopt Agile BI processes, technologies, and architectures to improve their chances of delivering successful BI initiatives.
Business intelligence (BI) software has emerged as a hot topic in the past few years; in 2011, most companies will again focus their software investment plans on BI. More than 49% of the companies that responded to our most recent Forrsights Software Survey have concrete plans to implement or expand their use of BI software within the next 24 months. But being interested in BI software and spending money to adopt BI tools and processes do not necessarily translate into successful implementations: Forrester’s most recent BI maturity survey indicated that enterprise BI maturity levels are still below average (2.75 on a scale of 5, a modest 6% increase over 2009). Why are BI maturity levels so low, given the amount of money firms spend on it? Three factors contribute to this rift and can lead to less-than-successful BI initiatives:
Implementing BI requires using best practices and building upon lessons learned.
Why, oh, why is it that every time I hear about a BI project from an IT person, or from a business stakeholder describing how IT delivered it, with few exceptions, these are the stories plagued with multiple challenges? And why is it that when I hear a BI story about an application that was installed, built, and used by a business user, with little or no support from IT, it’s almost always a success story?
I think we all know the answer to that question. It’s all about IT/business misalignment. A business user wants flexibility, while an IT person is charged with keeping order and controlling data, applications, scope, and projects. A business user wants to react to ever-changing requirements, but an IT person needs to have a formal planning process. A businessperson wants to have a tool best-suited for the business requirements, and an IT person wants to leverage enterprise standard platforms.
Who’s right and who’s wrong? Both. The only real answer is somewhere in the middle. There’s also a new emerging alternative, especially when applied to specific domains, like customer analytics. As I have repeatedly written in multiple research documents, front-office processes are especially poorly-suited for traditional analytics. Front office processes like sales and marketing need to be infinitely more agile and reactive, as their back office cousins from finance and HR for obvious reasons.