March 19, 2007
Three ways to market analytics-related technology
“Decision support”, “information centers”, “business intelligence”, “analytic technology”, and “information services” have been around, in one form or other, for 35+ years. For most of that time, there have been two fundamental ways to sell, market, and position them:
- Access to information
- Application software
More recently – especially the past five years – there’s been a third way:
- Infrastructure upgrade
as early-generation implementations get replaced by newer ones.
At the 50,000 foot level, here’s some of what I see going on:
- Classical BI marketing is floundering. BI vendors don’t know whether they’re in the business of quick/easy information access, analytic apps, or better-enterprise-system-software.
- A few areas of analytic application are being packaged and marketed well, with solid business-process stories and good customer acceptance of same. The biggies are budgeting/planning and CRM analytics. On the whole, however, analytic apps are floundering, or else are little more than reporting front-ends on operational systems (e.g., in network management).
- Data warehouse software startups are on a roll. Especially at the high end, this is a pure infrastructure-upgrade business. There’s plenty of room still for improvement, but multiple vendors each are doing good jobs of marketing on the basis of:
- Speeds and feeds
- Ease of deployment
- Ease of administration
- Price
- Credibility
- Data integration is mainly an infrastructure improvement play. After all, that integration COULD be hand-coded. Automating the process is usually a better-infrastructure story.
- Text search is still an information-access story. There are multiple niches where search is booming. But in all cases the story is information access. Evidently the technology and/or market aren’t mature enough yet for strong infrastructure stories. And in the limited cases where text search gets integrated into general application software packages, it’s usually just for information access rather than a real business process.
- Data mining and predictive analytics are mainly information access plays. Yes, the information being accessed is calculated rather than raw. Yes, I believe that the heart of the data mining market is continuous process improvement. Even so, what users buy from the vendors is usually little more than information toolkits.
- Text analytics is mainly an information access play. Text mining and information extraction have two main uses right now. Either they resemble – and indeed often feed into — data mining, or else they are used to enhance search and search-like document access.
- Information services have always been an information access play. When you think about it, the financial-quote-machine business is a huge part of the whole decision support market. Lexis/Nexis is no slouch either. And they’re all about providing information access.
Related links
- This three-headed taxonomy of strategies is similar to one I previously postulated for Microsoft, SAP, and IBMOracle.
- I covered analytic business processes at length in a November, 2004 white paper. Unfortunately, industry progress since then has been relatively slow.
- I’ve written voluminously about data warehouse software startups on DBMS2.
- One example of infrastructure focus is the ease-of-deployment trend.
- Web search and generic enterprise search aren’t the only search areas to focus on information access. (And yes, they’re most definitely separate areas.) Even customer-facing structured search does; the information is just tailored according to different criteria. 😉
Categories: Analytic technologies, Business intelligence, Data mining
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4 Responses to “Three ways to market analytics-related technology”
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There are pockets of more sophisicated systems out there – fraud detection, for example – where the analytics are deeply embedded in an application that relies on those analytics for its success. I do agree, though, that many “analytic applications” are nothing of the sort. An analytic application, to me, must be driven by an analytic model and must make some kind of decision as a result.
JT
To your comment that vendors do little more than visual window dressing for mined data. We are finding that our customers who make use of their historians have a treasure trove of process information that they are not taking advantage of. We have designed and implemented a root cause analysis package that makes use of that historical data and gives the customer the correlation and CV of the variables involved in their process either upstream or downstream. There is no “silver bullet” to show the process engineer the exact reason. Half of the problem is that the “grey hair” experience is gone and the engineers on the floor now cannot tell you with complete confidence exactly how their process runs or exactly what variables affect it. I do believe that mining manufacturing historians will be a large market in the next several years.
[…] Good launching points for my other research on these subjects are this recent post on analytic technology marketing strategies and two high-concept white papers available here. […]
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