Price-performance is everything in data warehousing (DW), and it’s become the leading battleground for competitive differentiation.

As I noted in a blog post last month, the price of a fully configured DW appliance solution has dropped by an order of magnitude over the past 2-3 years, and it’s likely to continue declining. In 2010, many DW vendors will lower the price of their basic appliance products to less than $20,000 per usable terabyte (TB), which constitutes the new industry threshold pioneered by Oracle, Netezza, and other leading DW vendors.

But that’s just a metric of price, not price-performance. Ideally, each DW appliance vendor should be able to provide you with a metric that tells you exactly how much performance “bang” you’re getting for all those bucks. In a perfect world, all vendors would use the same price-performance metric and you would be able to compare their solutions side by side.

But, as I noted a year ago in another blog post, truly comparable cross-vendor DW benchmarks have never existed and are unlikely to emerge in today’s intensively competitive arena. No two DW vendors provide performance numbers that are based on the same configurations, workloads, and benchmark metrics. And considering how sensitive these performance claims are to so many variables in the vendors’ solutions and in customers’ production environments, it can be quite difficult to verify every vendor performance claim in your specific environment.

Business processes can be incredibly hard to fathom. The more complex they are, the more difficult it is to find the magic blend of tasks, roles, flows, and other factors that distinguish a well-tuned process from a miserable flop. Even the people who’ve been part of the process for years may have little clue. It’s not just that they refuse to look beyond their job-specific perspectives, for fear of jeopardizing their careers. It’s often an issue of them being too close to the problem to see it clearly, even if they try very hard.

Process analytics is all about identifying what works and doesn’t work. It’s a key focus for us here at Forrester, and I’m collaborating with one of our leading business process management (BPM) analysts, Clay Richardson, on research into this important topic. The first order of business for us is to identify the full range of enabling infrastructure and tools for tracking, exploring, and analyzing a wide range of workflows. It’s clear that this must include, at the very least, business activity monitoring (BAM) tools, which roll up key process metrics into visual business intelligence (BI)-style dashboards for operational process managers. Likewise, historical process metrics should be available to the business analysts who design and optimize workflows. And each user should have access to whatever current key performance indicators are relevant to the roles they perform within one or more processes.

Ah, memories. I remember the late, great Eighties, early in my analyst career, when I had my first brush with what was later known as “groupware.” It was a LAN-based package, “The Coordinator,” from Action Technologies. The architecture of the software wasn’t as important as the linguistic theory on which it was built: the notion that groups cultivate intelligence by structuring their internal conversations to achieve common goals.

Essentially, the package required people to tag every e-mail they sent based on whether it constituted a discussion of possibilities, a request for clarification, or a request for action—and it tracked these threads so that everybody knew the goal-oriented status of every conversation. As you can probably guess, this was a heavy-handed way of getting people to come to agreement. Software shouldn’t dictate how people choose to interact: real-world conversation’s far too complex and convoluted for that. Most people don’t like being forced to rephrase or reconceptualize how they communicate with others. In fact, most of us users simply defaulted to sending messages that discussed open-ended possibilities, rather than engage in a fussy protocol of formal requests and offers.

People often use the end of a decade to say goodbye to trends that have played themselves out, or good riddance to things that have long since passed their cultural expiration dates. I like to use the beginnings of decades for that same purpose. What, we should ask ourselves, is not likely to last beyond the close of this new ten-year cycle?

In data warehousing, the most likely casualty of the Teens will be the very notion of a data warehouse. You can tell that a concept is on its last legs when its proponents spend more time on the defense, fighting definitional trench wars, than evolving it in useful new directions. Here’s a perfect case in point:a recent article by Bill Inmon, self-described “father of the data warehouse,” where he takes pains to specify what is not a data warehouse. Apparently, many of the approaches that we normally implement in our data warehousing architectures—such as subject-specific data marts, dimensional data structures, federated architectures, and real-time data integration—don’t pass muster in Inmon’s way of looking at things. Though he didn’t mention hybrid row-columnar and in-memory databases by name, one suspects that Inmon has a similarly jaundiced view of these leading-edge data warehousing technologies.

By James Kobielus

As we bid adieu to one decade and move into the next, it’s important to catch our collective breath and to take a quick look ahead. Here are some quick thoughts on the trends that will shape advanced analytics in the year to come. These trends will set the stage for thoroughgoing transformation of business intelligence (BI), data warehousing (DW), predictive analytics (PA), data mining (DM), business activity monitoring (BAM), complex event processing (CEP), and other key analytics technologies in the Teens:

• Self-service operational BI puts information workers in driver’s seat: Enterprises have begun to adopt self-service BI to cut costs, unclog the analytics development backlog, and improve the velocity of practical insights. Users are demanding tools to do interactive, deeply dimensional exploration of information pulled from enterprise data warehouses, data marts, transactional applications, and other systems.