Posted by Boris Evelson on July 28, 2009
Whoever says that Business Intelligence (BI) market is commoditizing is smoking something funny. From where we sit, it remains active, vibrant and full of opportunities both on the buyer and the seller side. On the buyer side the market is far from mature with multiple architecture, implementation, governance and organizational challenges. On the seller side we track over 20 “next generation” BI features that vendors are just beginning to address.
With today’s IBM announced acquisition of SPSS, BI world moved a step closer to addressing one of the gaps in traditional BI product lines – integrated advanced analytics. Traditional business intelligence applications (reporting, ad-hoc querying, OLAP) are great at answering questions like “what happened”, “why it happened”, etc, but they do not help you with answers to questions like “what may happen”. This is the realm of advanced analytics, predictive data modeling, statistical analysis, etc – provided by vendors like SAS, SPSS and some other, including a popular open source package “R”.
Here’s where the world of traditional BI and advanced analytics stood until this morning:
- SAS was a clear leader in the space having provided integrated solutions for the past 10 years
- MicroStrategy also built in their own advanced analytics into its BI package
- Microsoft delivered a few basic data mining routines available in SQLServer DBMS engine via Excel
- TIBCO Spotfire acquired Insightful
- IBM Cognos and SAP Business Objects partnered with SPSS
- Information Builders integrated open source “R” package
The combination of traditional BI and advanced analytics was a real differentiator for vendors in this category. While most other traditional BI vendors partnered with advanced analytics vendors, a simple partnership carries certain implications:
- There’s no common metadata – so all definitions of metrics, measures, indicators, etc need to be build and replicated in two places
Advanced analytics typically require their own DBMS (typically proprietary) to churn large data sets through multiple complex calculation iterations. Moving these huge data sets and results from one DBMS to another is always a throughput challenge
So what are the implications of IBM SPSS acquisition? Plenty!
- IBM can compete more effectively with SAS, TIBCO Spotfire, MicroStrategy, Microsoft and Information Builders in deals that require both traditional and advanced analytics
IBM has to reconcile some overlap of its DB2 Intelligent Miner and SPSS for data mining and IBM OmniFind and SPSS for text mining. Since it’s still business as usual at Cognos and IBM (most products are being developed separately) I do not expect that this will be a priority in the next 12 months.
SAP Business Objects may need to rethink their advanced analytics strategy. They may need to go the open source route or acquire another advanced analytics vendor. Plenty of choices out there, here’s one sample short list: Accelrys, Angoss, Applied Predictive Technologies, DataInfoCom, Genalytics, KXEN, Megaputer, Partek, Psydex, ThinkAnalytics, Xeligence.
A big unknown is what Oracle may do to address this opportunity. Just like IBM Oracle also has Oracle Data Mining (ODM) product built into its 11g DBMS. It also has a Real Time Decisions (RTD) engine that it acquired from Sigma Dynamics a few years ago – although this product is somewhat geared towards marketing analytics, specifically real time offer optimization. Will Oracle combine these two products and integrate them with their traditional BI suite (Enterprise Edition – OBIEE), or will they acquire a 3rd advanced analytics product? With Oracle, we never know.
What’s next? After we go through what is sure to be a wave of advanced analytics acquisitions, we are on to the next stages. Plenty of opportunities left for all large BI vendors to round out their offerings with in memory analytics, text analytics, process analytics, information post-discovery, and many many more. Have fun, everyone!
Search Forrester's Blogs
Four Citizen-Driven Imperatives Governments Must Embrace »
Free Webinar Series
How Can You Master Big Data? »
- Anjali Yakkundi (24)
- Boris Evelson (139)
- Claire Schooley (2)
- Clay Richardson (1)
- Diego Lo Giudice (16)
- Gene Cao (1)
- George Lawrie (17)
- Holger Kisker (38)
- Ian Jacobs (1)
- James Staten (7)
- Jeffrey Hammond (27)
- John R. Rymer (45)
- Jost Hoppermann (33)
- Kate Leggett (118)
- Kurt Bittner (3)
- Kyle McNabb (12)
- Margo Visitacion (9)
- Mark Grannan (9)
- Martha Bennett (12)
- Michael Barnes (21)
- Michael Facemire (14)
- Mike Gualtieri (114)
- Noel Yuhanna (10)
- Paul Hamerman (2)
- Phil Murphy (24)
- Randy Heffner (15)
- Rob Koplowitz (1)
- Stephen Powers (23)
- Ted Schadler (4)