Some issues in business intelligence
In November I wrote two parts of a planned multi-post series on issues in analytic technology. Then I got caught up in year-end things and didn’t blog for a month. Well … Happy New Year! I’m back. Let’s survey a few BI-related topics.
Mobile business intelligence — real business value or just a snazzy demo?
I discussed some mobile BI use cases in July 2010, but I’m still not convinced the whole area is a legitimate big deal. BI has a long history of snazzy, senior-exec-pleasing demos that have little to do with substantive business value. For now, I think mobile BI is another of those; few people will gain deep analytic insights staring into their iPhones. I don’t see anything coming that’s going to change the situation soon.
BI-centric collaboration — real business value or just a snazzy demo?
I’m more optimistic about collaborative business intelligence. QlikView’s direct sharing of dashboards will, I think, be a feature competitors must and will imitate. Social media BI collaboration is still in the “mainly a demo” phase, but I think it meets a broader and deeper need than does mobile BI. Over the next few years, I expect numerous enterprises to establish strong cultures of analytic chatter (and then give frequent talks about same at industry conferences).
Business intelligence for mid-market enterprises is problematic
Given the saturation of the large-enterprise BI market with supposed enterprise-standard BI systems, it would seem that smaller enterprises comprise a large part of the BI growth opportunity. However, the large-enterprise and mid-range BI markets are very different. For example:
- Large enterprises typically have tough challenges in data integration; smaller enterprises may truly start out with their data in only a few systems.
- There are many reasons for large enterprises not to do their BI in the cloud, such as bandwidth, internal politics, or the unsuitability of most cloud infrastructure for analytic DBMS scale-out. Smaller enterprises, however, may prefer SaaS (Software as a Service) BI.
- The BI market for smaller enterprises is heavily OEM. But unless you’re buying some kind of data/analytics bundle, the large enterprise BI market still seems overwhelmingly standalone.
- Large-enterprise BI tools incorporate much of a DBMS-like technology stack; at smaller enterprises, BI can often stick to its specialized-application-development-tool knitting. But on the other hand …
- … large enterprises almost always already have a data warehousing infrastructure. Mid-range BI buyers may not have a separate analytic DBMS. Therefore …
- … BI/DBMS bundles make more sense in the mid-market than they do at large enterprises.
- Each large enterprise has a unique infrastructure, and commonly a unique competitive situation as well. Thus, the idea that you’ll pre-build most of an analytic application for a large enterprises — because you know what data model they need to do their BI — usually turns out to be silly. But smaller enterprises can be more homogeneous, and so for them pre-built analytic applications can actually work.
I don’t know of anybody who’s really cracked the code on mid-market BI. Crystal Reports (long owned by SAP Business Objects) has huge OEM share, but somehow hasn’t parlayed that into a comprehensive mid-market BI presence. Various SaaS or on-premise vendors have cool product ideas — e.g. Gooddata, Endeca, or my clients at PivotLink — but none seems to have set the world on fire to this point.
Departmental BI is doing better
The news is happier in a related market — business intelligence for departments of larger enterprises. However, this is a hard market to analyze, for at least two reasons. First — as is often the case — the distinction among large-enterprise-wise, smaller-enterprise-wide, and departmental BI is not a clear one.* Second, “departmental BI” has at least two major strains:
- Simple, pedestrian BI, implemented quickly.
- Investigative analytics.
*In particular, it has been the case since the 1990s that BI tools first get sold to departments, hopefully for fast implementations — think 4-6 weeks as a base case — and then spread out internally after their initial successes. I am frequently amused by vendors who claim to have pioneered that sales model sometime over the past decade, or even within the past few years.
That said, there are two main kinds of reason to do your BI departmentally, at arm’s length from central IT.
- Perhaps, for good reason or bad, IT is being insufficiently helpful at managing the data.
- This can be a straightforward matter of politics and priorities — IT controls the data, but is slow about giving you access.
- Also, you may want to include data that’s outside IT’s purview, be it third-party or just purely departmental.
- Further, you may want functionality that corporate-standard BI doesn’t offer.Potential examples include:
- Cool analytic visualization.
- “Real-time” data visualization.
- The ability to play nicely with particular kinds of data sets.
I have a lot more to say about those points — but not in a post that’s already as long as this one. 🙂
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10 Responses to “Some issues in business intelligence”
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Happy New Year Curt! Enjoyed the blog, agree on many points. Wondering if you will address the impact of the success of departmental BI on enterprise integration ? What’s your opinion on how do we deal with a more decentralized world in regards to governance and security? Cheers, Nancy
Nancy,
I don’t believe things are getting MORE decentralized, because I don’t believe they were ever centralized in the first place. 🙂
Curt-hahah good point but the very things that drove us on that journey to consolidate are still challenges- agree ?
I’d throw in one more point for Departmental BI – Serendipity. Its a small – but growing – category of “Huh, I didnt realize *that* was happening” which you only discover once you start playing with the data – what you didn’t know you didn’t know. The “cool analytics/visualization” category is maybe the best lead in to this…
Business intelligence is like higher education… or education in general… or gambling… or love… what you put down on the table is what you might pick up with some return on investment. I’m specifically referring to adoption of new social methodologies as they intersect new business methodologies and even current business process adoption in some cases at some shops. Not every kid goes to harvard in the beginning.
BI will be much more relevant, I think, when it is optimized for integration with big data analytics and cloud information brokering and channeling.
Very interesting and straight talk.
As it relates to Mobile BI, I would agree that mobile BI may be not right path to get “deep analytic insights” ; but after all, isn’t it the same for web even today: a lot of companies adopted the web as a foundations for their BI deployment but still rely on desktop BI or Excel integrated BI for some of their “deep” use cases.
But I think that the value may be elsewhere, to reach what I call the last mile of BI, i.e the use cases where BI still has to deliver on its promises, despite the progress that the web helped to achieve :
– Dashboards : I do not feel that top level executive today use BI to the same extent that knowledge workers do (eg : controlers, product managers, marketers…), even though they generally have access to the BI environment. Dashboards are high in the agenda of BI initiatives today, and I feel that the tablets are a good fit for those (easy to consume anywhere and to share in meeting rooms). See environments such as RoamBI Flow as an example of what mobile can do to publish and share business results.
– Front office and operations : those people are mobile by nature, and BI has strugled to reach them by now. They need to access to information rapidly, be alerted in case of events…
– Customer facing applications : take Personal Finance Management in the banking industry, for example. Banks have tried since years to provide to their customers some information to help them manage their money. But the applications proposed where boring, complex to install and use. Mobile helps to “consumerizes” those kind of applications (see the mint.com home page : mobile BI is cleraly the center piece) . Indeed, this is not BI as we know it, rather Mobile Apps with Embedded BI and Real time decisioning in it. But, I feel every consumer facing applications would need those kind of embedded analytics.
Jean-Michel,
Great call on calling out “stakeholder-facing” BI as important, where stakeholders can be, for example, customers. I’ve addressed that subject in, e.g., http://www.dbms2.com/2010/05/15/stakeholder-facing-analytics/, and should have mentioned it year again.
You may further be right that it’s a good fit for mobile, in that very simple-minded BI can often do the job for stakeholders, and very simple-minded BI is what fits well on mobile devices at this time.
Curt,
Just to add to Jean-Michel’s point, mobile BI is about “right data, right place” — in particular, it’s very useful for adding real-time data about exceptions to the predictions made by “traditional” BI — i.e. when what’s actually happening is suddenly trending sharply away from what was expected. Mobile devices are a good platform to alert/inform front-line, in-the-moment proactive behavior to fix the problem (road routing problems, retail stock-outs, manufacturing quality exceptions, etc.)
Timo,
I agree that mobile devices could be great venues for alerting. But I’m not convinced that that’s been the focus of mobile BI development or marketing to date.