Whoever said BI market is commoditizing, consolidating and getting very mature? Nothing can be farther from the truth. On the buy side, Forrester still sees tons of less-than-successful BI environments, applications and implementations as demonstrated by Forrester's recent BI Maturity survey. On the vendor/sell side, Forrester also sees a flurry of activity from the startups, small vendors and large, leading BI vendors constantly leapfrogging each other with every major and minor release.
In terms of the amount of BI activity that Forrester sees from our clients (from inquiries, advisories and consulting) there’s no question that SAP BusinessObjects and IBM Cognos continue to dominate client interest. Over the past couple of years Microsoft has typically taken the third place, SAS fourth place and Oracle the distant fifth. But ever since Siebel and Hyperion acquisitions, the landscape has been changing, and we now often see Oracle jumping into third place, sometimes leapfrogging even Microsoft in the levels of monthly interest from Forrester clients.
I recently asked my Twitter followers if they had good examples of queries, business questions that SQL can't do. It turns out a better question is "what SQL can't do easily", so I thought I'd share with everyone what I heard and found. Seth Grimes was the first one to provide an excellent answer with some informative examples - thank you, Seth! I also found very useful articles on typical SQL challenges such as avoiding multiple duplicate sets in your SQL results, and why NULLs create tons of headaches for SQL coders.
There's also a typical SQL challenge with ragged, sparse, unbalanced hierarchies and dimensions. For example, a retail store, a wholesaler or a distributor with thousands of products, and a manufacturer with thousands of parts often struggle with dissimilar data. A pencil in an office supply store does not have the same descriptive attributes (lead type, for example) as a calculator (scientific, financial, etc.) or an office chair (number of wheels, etc.). Or a tire in a car manufacturing supply chain does not have any common descriptive elements (rubber grade, width-to-height ratio) with gear boxes (automatic vs. manual, 4 or 5 speed, gear-to-gear ratios, etc). When looking for correlation between two entities (for example, what is a potential product quality issue that is making my sales go down?) in cases with disparate, dissimilar products (as in retail products or manufacturing parts), the same SQL query cannot work for all products or parts. One would be forced to write multiple SQL queries for each product or part type to find such a sales/quality relationship.
IBM announced its intentions to acquire Coremetrics, a leading Web analytics vendor, as BI megavendors continue to round out their BI portfolios (the other leading vendor in the space, Omniture, was recently picked up by Adobe). Good move, IBM. Web analytics can't really continue to exist in a silo. In order to get truly complete 360-degree view of customers, prospects and products, one needs to combine Web analytics data with ERP, CRM, HR, Financials and other transactional and analytical data sets. Currently, there are no off-the-shelf solutions that do that - it's pretty much the realm of customized offerings and systems integration. If IBM can indeed plug Web analytics into its data integration, data warehouse and BI products and solutions, it'd be quite a differentiated offering. Other large BI vendors, like Microsoft, Oracle and SAP will probably pick up one of the remaining Web analytics vendors Nedstat, Unica and Webtrends sometime soon.
I know many of you already know my position on this, but I thought I'd get it out in the open and challenge all of you with a controversial discussion. In my definition – and believe it, I am fighting and defending it every day – analytics has always been, and will always be, part of BI. What many of the vendors and analysts describe as "the new age of analytics" I built at Citibank in the early '80s and then built in about 50+ enterprises in the '90s at PwC. I think the effort of trying to differentiate analytics from BI is a vendor-invented hype, since many BI vendors are running out of ways to differentiate themselves (and incorrectly so: see the next paragraph, and many other next-gen BI trends). I also disagree with the “old BI = bad”, “new analytics = good” premise that I see in many analysts' papers. You and I know that you can’t build analytics (OLAP, advanced analytics, etc.) without basic ETL, DW, MDM, etc. So nothing’s really changed as far as I am concerned: we are still fighting the same battles – silos, data quality, etc.
Defining a successful BI strategy is a lot more than gathering requirements and selecting a vendor. While it’s been a subject of many books, I know few of you have time to read them, so here’s a short version.
First defining what BI is and what it is not. Is it just reporting, analytics and dashboards? Or does it involve ETL, DW, portal, MDM, etc., as well?
If the former, you then need to define linkages, dependencies, overlaps and integration with all of the latter (including - very importantly - integration and coordination with the higher level enterprise architecture efforts). If latter, it’s a whole different subject. You then really do need to read a few thick books.
Ensure senior business executive commitment and top down mandate. If you cannot get that, do not proceed until you do. Two ways to “sell BI” to them (even though that’s not a good position to be in):
Educate them on BI ROI. Here's where you'd build a high level BI business case.
I get this request almost on a weekly basis: "Boris, my BI vendor is offering me the following discount, is it a good deal or not?" The first question is what are you comparing it to? It reminds me of an old joke: Q. How much is 5 times 5. A. Depends on whether you're buying or selling. Many of the vendors do not publish or reveal list prices, or even if they do, they are revealed only under NDA to each client, so good luck comparing what the vendor told you and what they told another client. So what ARE you comparing it to?
Another problem, IMHO, is that many of the vendors muddy the waters with CPU based prices, clock speed based prices, etc. Yes, CPU, server, core based prices make sense if you are growing and want to lock in a good deal now, before you grow and expand. But in the end, you, the buyer, still need to figure out how much the software costs you per seat, per user. So with both of these challenges in mind I looked through my 20+ years of notes on BI contracts and per seat license costs and came up with the following. Notice, an interesting X-factor (obviously, I fixed the numbers a bit to have it look nicely like that):
BI output consumer, no interactivity $300
BI output consumer, with light (sort, filter, rank) interactivity $600 (or 2x)
BI output consumer with heavy interactivity (interactive dashboards, search, etc.) $1,200 (or 4x)
My friend and highly respected colleague, Wayne Eckerson from TDWI, posted a great article called “Purple BI People”. In the article he described some of the best practices for business and IT alignment, and cross-functional skills sets needed for successful and effective BI professionals. Wayne, I loved the blue cow analogy, you know that I always think in metaphors, analogies, similies and associations. But, while I completely agree with Wayne in his near term assessment, best practices and recommendations, I would like to suggest another long term point of view.
Can business and IT ever align on BI? Can business ever be satisfied with IT for delivering successful and effective BI applications? Is there such a thing as BT (Business Technology, the phrase that Forrester coined and promotes) in BI?
I used to think we could deliver on that promise. Not so sure it’s that straightforward now. Just look at some of the hopelessly diametrically opposing business and IT priorities. I hear the following complaints from my clients day in and day out:
Business is all about revenue generation. While IT can support that, much more often cost cutting is IT's highest priority.
Business wants solutions now. Not tomorrow. IT needs to go through due diligence of testing and approving BI applications. Right now, on demand does not sit well with IT.
Business wants to react to constantly changing BI requirements. IT has to plan.
Business sometimes is willing to do something “quick and dirty” – even at the expense of potentially jeopardizing accuracy and adherence, compliance with standards. IT is all about compliance and sticking with standards.
I have long resisted and will continue to resist for the foreseeable future any notions that the BI market is commoditizing. A single simple look at the BI maturity in enterprises and next gen BI technologies is a simple proof that we are far, very far, from any kinds of commoditization. Consolidation is quite a different story. Last week's SAP acquisition of Sybase and my roaming the exhibitor / partner floor at SAPPHIRE in Orlando are two more proofs. On a huge SAPPHIRE exhibition floor I could count software partners by the number of the fingers on my hands. Why? Because everyone who matters has been acquired by a competitor! Most of the exhibitors were management consultancies, systems integrators and other SAP implementation partners. Hence, a lesson to independent BI vendors: offer your own full BI stack or position yourself for an acquisition. No other long-term options in my mind.
But as always I welcome all and any comments and opposing views.
SAP gets its own relational (Sybase ASE) and analytical (Sybase IQ) DBMS. Why is this a positive since SAP already has tight partnerships with major DBMS and DW vendors such as Oracle, IBM, Microsoft, Teradata, and HP? Simple. First, SAP can now control the code. Second, SAP can now potentially reduce reliance on DBMS partners, most of whom (Oracle, IBM, Microsoft) have their own full software stacks and therefore compete, often putting a strain on partnership relationships. True, Sybase ASE has a rather low market penetration, other than on Wall St (see Stefan Ried's blog), but since SAP BW takes care of most of the traditional RDBMS design and implementation tasks, Sybase could be positioned as a black box engine under BW, that does not require separate design, administration and maintenance environment. *** Update. SAP just confirmed that each of its applications can run on an independent database, so having mixed DBMS platforms under ERP and BW will not be an issue.
SAP also gets highly relevant (for low latency BI) and currently missing CEP technolgy from the Sybase Aleri acquisition and an OEM version of Coral8.
SAP customers may also benefit from advanced analytics from Fuzzy Logix, integrated and embdded in SybaseIQ
Sybase gets a badly needed BI front end on top of its Sybase IQ analytical DBMS. While Sybase is leading the market in the columnar DBMS, it is somewhat challenged selling and positioning the product with the business buyers, since they can’t really see, feel, or touch it.
What is BI? There are two prevailing definitions out there – broad and narrow. The broad definition (using our own) is that BI is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insight and decision-making. But if we stick to this definition then shouldn’t we include data integration, data quality, master data management, data warehousing and portals in BI? I know lots of folks would disagree and fit these into data management or information management segments, but not BI.
Then, the narrow definition is used when referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards. But even there, as Jim Kobielus and I discovered as we were preparing to launch our BI TechRadar 2010 research, we could count over 20 (!) product categories such as Advanced Analytics, Analytical Performance Management, Scorecards, BI appliances and BI SaaS, BI specific DBMS, BI Workspaces, Dashboards, Geospatial analytics, Low Latency BI, Metadata Generated BI Apps, Non modeled exploration and In-memory analytics, OLAP, Open Source BI and SaaS BI, Packaged BI Apps, Process / Content Analytics, Production reports and ad-hoc query builders, Search UI for BI, Social Network / Media Analytics, Text analytics, Web Analytics.
To make matters worse, some folks out there are now trying to clearly separate BI and analytics, by trying to push a “core, traditional BI is commoditized, analytics is where differentiation is today” message. Hmmm, I thought I was building analytical apps using OLAP starting back in the early 80’s.