I enjoyed reading Nenshad Bardoliwalla's blog The Top 10 Trends for 2010 in Analytics, Business Intelligence, and Performance Management. I have to agree with most of the points, but I am a little skeptical of a few of them. For instance, 2010 is only a few weeks away. Some things here feel like they are, at best, 3-5 years away. Developments aren't like a tsunami that happens all at once. We may already see evidence for some of these things, but when will they reach fluorescence? At what point are they a "trend?" For example:

We will witness the emergence of packaged strategy-driven execution applications

Is an emergence a trend? Nevertheless, how different is that from packaged analytical apps? Strategic planning is still an oxymoron in most organizations. Granted, some people may have expressed a strategy, and its circulated in beautiful PowerPoint slides, but the nitty-gritty of putting numbers to a strategy is still a joke, an agonizing iteration of best guess forecasts combined with mandated goals. I'm not sure how this can connect to anything. So to imply that we are on the cusp of a smooth strategy-to-execution through packaged software products is, at best, a little optimistic. The following quote appeared in Harvard Business Review in an article entitled, "Who Needs Budgets".  It reinforces the need for fundamental changes to planning and budgeting processes to address business challenges. So long as the budget dominates business planning, a self motivated workforce is a fantasy, however many cutting edge techniques a company embraces.

Nenshad goes on to cite as an example that Oracle's Fusion technology "clearly portend(s) the increasing fusion of analytic and transactional capability in the context of business processes and this will only increase." There has been a lot of portent in this area for years, but I still can't see that 2010 is the year it will become a trend.

The holy grail of the predictive, real-time enterprise will start to deliver on its promises

Again, is a "start" a trend? I don't think so. Besides, predictive real-time organizations already exist, and have for some time, particularly in financial services and customer service applications. Business rules engines have been around for more than a decade and are usually primed with scored data from predictive models, but this is a niche. It represents a tiny proportion of operational decisions in an enterprise.

There is also danger in predictive models. Suppose a PM indicates that only the top 20% of your customers are profitable and the rest lose money. The "real-time enterprise" might close the accounts of the 80%. Suppose, however, that the PM was unable to understand WHY they were unprofitable, but it turned out to be excessive waste and poor quality caused by you and customer profitability was incorrectly measured? Quantitative methods are only as good as the data, methodologies employed and skill of the modelers. You'd have to be crazy to run your company on algorithms.

There is a lot of talk about CEP, but keep in mind that the domain of CEP is exceedingly narrow and driven by the discernible and codified rules that drive it. It doesn't run a company, just a few decisions. Likewise, in decision management, which I wrote about in Smart (Enough) Systems with James Taylor, we were extremely careful to point out that decision management as a technique only applied to a very small subset of operational decision types, that's why we called smart "enough." Though lots of small decisions add up, making some mistakes are acceptable, such as denying credit to an otherwise creditworthy consumer. But in those cases where even a single mistake can have severe consequences, decision automation approaches, whether decision management, CEP or other point solutions are clearly not acceptable. There are no solutions yet for "sensing and responding" approaches for those kinds of decisions.

This leads me to the next point. I believe that the distinction between exploratory BI (OLAP, reporting, visualization, etc.) and predictive analysis is rather artificial. To the greatest extent, they are used to understand things, not predict them. The predictive process is a very small part of the use of statistics in businesses. At the end of a statistical model is often the same process of BI - discussing the findings and deciding what to do. They just represent different methods. The exception is scoring models, a pretty widely used approach where large volumes of data are scored by a model such as a neural net and programmatic decisions are made without humans, such as mailing lists, next-best-offer, etc. But it's a real stretch to characterize this as a predictive, real-time enterprise.

SaaS / Cloud BI Tools will steal significant revenue from on-premise vendors but also fight for limited oxygen amongst themselves.

This already a trend. Well, maybe not if you use the word "significant." It is not clear to me that large enterprises are about to adopt SaaS / Cloud BI. Customers of Salesforce.com are way ahead in this regard, but only for applications derived of Salesforce.com data which is already in the cloud, so to speak. It's also not clear how the SMB's, whatever they are, are going to adopt this. Sure, cost is a major issue, but so is staff, attention, priorities, etc.

Open Source offerings will continue to make in-roads against on-premise offerings.

That's like saying I'll cut some calories from my diet or I'll work more diligently at blogging. How many? How much? You can't deny the claim, the question is, how significant is it?

So, with these small exceptions, I am agreement about these 10 points.