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.
In another token that the movement toward converged infrastructures and vertically integrated solutions is becoming ever more mainstream, HP and Microsoft recently announced a line of specialized appliances that combine integrated hardware, software and pre-packaged software targeting Exchange email, business analytics with Microsoft SharePoint and PowerPivot, and data warehousing with SQL Server. The offerings include:
HP E5000 Messaging System – Microsoft Exchange mailboxes in standard sizes of 500 – 3000 mailboxes. This product incorporates a pair of servers derived from HP's blade family in a new 3U rack enclosure plus storage and Microsoft Exchange software. The product is installed as a turnkey system from HP.
HP Business Decision Appliance – Integrated servers and SQL Server PowerPivot software targeting analytics in midmarket and enterprise groups, tuned for 80 concurrent users. This offering is based on standard HP rack servers and integrated Microsoft software.
HP Enterprise Data Warehouse Appliance – Intended to compete with Oracle Exadata, at least for data warehouse applications, this is targeted at enterprise data warehouses in the 100s of Terabyte range. Like Exadata, it is a massive stack of integrated servers and software, including 13 HP rack servers, 10 of their MSA storage units and integrated Ethernet, Infiniband and FC networking, along with Microsoft SQL Server 2008 R2 Parallel Data Warehouse software.
First of all, congratulations, SAS AR team, for one of the most efficiently and effectively run events.
SAS needs to make up its mind whether it wants to be in the BI game or not. Despite what SAS’s senior executives have been heard saying occasionally, that “BI is dead,” SAS is not quite done with BI. After all, BI makes up 11% of SAS’s very impressive $2.4 billion annual revenue (with uninterrupted 35-year growth!). Additionally BI contributed 22% to SAS 2010 growth, just below analytics at 26%.
Even though some organizations are looking at and implementing advanced analytics such as statistical analysis, predictive modeling, and — most important — model-based decisions, there are only a handful of them. As our BI maturity survey shows year after year, BI — even basic BI — maturity is still below average in most enterprises. Add these numbers to the abysmal enterprise BI applications penetration levels in most large organizations, and you get continued, huge, and ever-expanding opportunity that no vendor in its right mind, especially a vendor with leading BI tools, should miss.
Mobile devices and mobile Internet are everywhere. Over the past few years, Forrester has tracked continuously increasing levels of adoption and maturity for mobile business applications, but not so for mobile business intelligence (BI) applications. The adoption and maturity of mobile BI fall behind other mobile enterprise applications for multiple reasons, mainly the lack of specific business use cases and tangible ROI, as well as inadequate smartphone screen and keyboard form factors. However, larger form factor devices such as tablets and innovative approaches to online/offline BI technical architecture will boost mobile BI adoption and maturity in the near future. BP professionals must start evaluating and prototyping mobile BI platforms and applications to make sure that all key business processes and relevant information are available to knowledge workers wherever they are.
But mobile BI adoption levels are still low. Why? We see three major reasons.
Smartphones still lack the form factor appropriate for BI
The business case for mobile BI remains tough to build
Mobile device security is still a concern
Now, mobile tablet devices are a different story. Just like Baby Bear's porridge in the "Goldilocks And The Three Bears" fairy tale, tablet PCs are "just right" for mobile BI end users. So what can you do with mobile BI? Plenty!
Improve customer and partner engagement
Deliver BI in the right place, at the right time
Introduce BI for the workers without access to traditional BI applications
Improve BI efficiency via query relevance
Improve "elevator pitch" effectiveness
Give away mobile devices as an incentive to cross-sell and upsell analytic applications
I get tons of questions about "how much it costs to develop an analytical application." Alas, as most of us unfortunately know, the only real answer to that question is “it depends.” It depends on the scope, requirements, technology used, corporate culture and at least a few dozen of more dimensions. However, at the risk of a huge oversimplification, in many cases we can often apply the good old 80/20 rule as follows:
~20% for software, hardware, and other data center and communications infrastructure