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IBM announced<\/a> recently that it would purchase statistics vendor SPSS for approximately $1.2 billion. Various commentators have been saying this is a very big deal:<\/p>\n I personally*<\/em><\/strong> think it\u2019s probably a good move for SPSS and IBM, but I\u2019m a little more skeptical. Yes, I believe that predictive analytic technology can provide great value, and that it\u2019s an important part of the BI future. But here\u2019s three reasons why the reality \u2013 inevitably \u2013 can\u2019t quite live up the expectations.<\/p>\n \u201c\u2026who wants to \u201cdrive looking out of the rear view mirror\u201d?\u2026 Despite the obvious uses of this type of predictive analytics in organizations\u2026 it has not been implemented widely.\u201d<\/p><\/blockquote>\n The post generated a comment from Malcolm Ryder<\/a>:<\/p>\n \u201cSo who wants predictive analytics? \u2026 *everyone* wants predictive analytics; a subset of everyone wants to pay for it; a subset of that payers\u2019 group is willing to trust it; and a subset of that trusting group is willing to depend on it. And the difference between what makes predictive analytics successfully useful versus successfully marketable is a gap just as large as the miniscule percent of dependents versus the whopping percentage of enthusiasts\u2026\u201d<\/p><\/blockquote>\n Predictive analytics, statistical modeling, data mining, etc. are extremely valuable technologies, and have had great business success in a wide variety of applications including fraud detection, cross-selling, and direct marketing<\/a>.<\/p>\n But they\u2019ve been around for decades, and readily available to anybody who wanted to use them. Even IBM has already sold the technology in the form of Intelligent Miner for many years (here\u2019s a fun press release from 10 years ago, IBM Launches Major e-Business Initiative Emphasizing Business Intelligence<\/a>).<\/p>\n And there are clear limitations to anything that claims to provide \u201ctrue foresight\u201d. Bill Inmon talked about the difficulties of Predicting the Future<\/a> last month on the BeyeNetwork<\/a>, and the Wall Street Journal published an article a couple of years ago called Now, It’s Business By Data, but Numbers Still Can’t Tell Future<\/a> pointing out that even the most detailed statistical analysis has limitations since conditions may change, rendering the analysis misleading, and that while analysis can lead to incremental improvement in current markets, it can\u2019t provide the creativity required to take the next big leap and adapt to tomorrow\u2019s markets.<\/p>\n So why so much professed excitement now? It\u2019s true that the advanced analytics market is growing faster than the rest of BI, and that IBM\u2019s acquisition might accelerate it. But it\u2019s also growing off a much smaller base, and according to IDC, the current growth differential isn\u2019t that large: 12.1% growth for last year compared to the rest of BI growing at 10.3%.<\/p>\n If those rates stay the same, the market won\u2019t be too shaken up in the near future. To put things in perspective, the advanced analytics makes up currently only 19.5% of the market, and in 10 years time, at the growth rates above, it would be 22.2% of the market. This is hardly \u201credrawing the map\u201d.<\/p>\n As Neil Raden puts it in his blog post: SPSS Is Not the Story; IBM’s Vision for Analytics Is<\/a>. IBM\u2019s vision for BI started many years ago with the notion of \u201cinformation warehouses\u201d (although Digital Equipment Corporation was first with the idea, according to Rick Sherman<\/a>), and took a big leap forward with the purchase of Cognos. SPSS is just an add-on to that strategy, not a \u201cseminal moment\u201d.<\/p>\n Predictive analytics requires a lot of expertise to implement and interpret, and IBM intends to provide services to help organizations do this.<\/p>\n But even more so than other forms of business intelligence, the easiest way to make this powerful technology available to a wider audience is to embed predefined advanced analytics deeply into a business processes. This has been the approach of the advanced analytics market leader, SAS<\/a>).<\/p>\n I helped launch a \u201cdata mining for the masses\u201d product called BusinessObjects Data Miner many years ago. The product never took off as a standalone offer and was eventually canned. But I still believe that there\u2019s a great opportunity to use advanced analytic technologies with traditional BI, to augment the analysis skills of users \u2013 and all of these techniques are much easier if you already have the business context defined:<\/p>\n I predict that over the long term, organizations will tend to purchase this technology from an application vendor like SAP or Oracle rather than a platform-and-services vendor like IBM.<\/p>\n Some of the coverage implies that IBM scooped their competitors. But it\u2019s the nature of the IT business that there\u2019s a more or less permanent discussion about possible acquisitions, and any company up for sale naturally tries to get a few bidders<\/a>. So I assume that the other large vendors in the space decided that the deal wasn\u2019t right for them, in terms of strategy, price and opportunity cost, rather than being caught out by surprise.<\/p>\n It may just be that IBM needed more of a BI boost than the other large vendors — as Mary Hayes Weier put it in InformationWeek:<\/p>\n \u201cIBM and its Cognos division had 10% of the total BI market in 2008 with revenues of $800 million, up 5% since the previous year. Compared with competitors, that’s sub-par performance. SAP, SAS, and Oracle — ranked No. 1, No. 2, and No. 4 — all had double-digit revenue gains, with the total BI market growing 10.6% to reach $7.8 billion, according to an IDC report released in June.\u201d<\/p><\/blockquote>\n If it is indeed about embedding powerful technology in applications, rather than trying to grab existing market share, there are other technologies available in the market.<\/p>\n SAP currently has a long-term agreement to offer SPSS PASW Modeler as part of the BusinessObjects Predictive Workbench<\/a> product, but is also a long-term partner of IBM<\/a>, and has stated that:<\/p>\n \u201cSAP’s partnership with SPSS is working well and we do not expect IBM\u2019s intended acquisition to have an impact on this relationship. We have a growing number of customers as a result of this agreement and we will continue to partner with IBM and evolve our predictive services portfolio to meet the needs of our customers.\u201d<\/p><\/blockquote>\n And that:<\/p>\n \u201c[SAP\u2019s] strategy is to provide predictive analysis features for all of us and not just expert statisticians and analysts who have traditionally used more complex tool sets. This requires new and innovative approaches in a market that is still evolving, and as the market leader in Business Intelligence we are committed to delivering the solutions our customers need to have clear line of sight, particularly in the current challenging business environment.<\/p>\n Some examples of our investments in the area of predictive and statistical analysis across our product portfolio include:<\/p>\n But as Boris Evelson of Forrester says<\/a>,<\/p>\n \u201c'[there are] Plenty of choices out there, here\u2019s one sample short list: Accelrys, Angoss, Applied Predictive Technologies, DataInfoCom, Genalytics, KXEN, Megaputer, Partek, Psydex, ThinkAnalytics, Xeligence.\u201d<\/p><\/blockquote>\n SAS<\/a> is the obvious prize in the market, but Dr. Jim Goodnight<\/a>, the company\u2019s founder, CEO, and controlling shareholder has so far not shown any interest in selling \u2013 and SAS\u2019s out-of-the-mainstream corporate culture<\/a> and sales model means that any acquisition by a publicly-traded company would probably be a painful process.<\/p>\n It\u2019s clear, however, that IBM has now set the bar for the others, and we will probably see more acquisitions and integrations in the coming year\u2026<\/p>\n * Everything on this blog is my personal opinion, but just to emphasize again: these thoughts are my own, not the official or unofficial position of SAP.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Various commentators have been saying that IBM’s acqusition is a”seminal” moment for the company and the industry. I’m personally a little more skeptical — here’s why.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3],"tags":[70,79,160,204,213,585,836,911,1009],"class_list":["post-12071","post","type-post","status-publish","format-standard","hentry","category-bi-20","tag-acquisition","tag-advanced-analytics","tag-bi","tag-business-intelligence","tag-businessobjects","tag-ibm","tag-predictive-analytics","tag-sap","tag-spss"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-38H","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/12071","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/comments?post=12071"}],"version-history":[{"count":0,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/12071\/revisions"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=12071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=12071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=12071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Predictive analytics isn\u2019t a miracle new technology<\/h3>\n
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As I wrote back in 2007 (so no, this isn\u2019t just sour grapes) in a post called Who Really Wants Predictive Analytics<\/a>:<\/p>\nPredictive should be aligned with business applications<\/h3>\n
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Predictive analytics is still a fragmented market<\/h3>\n
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Links<\/h3>\n
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