Recession-proofing your core analytics

Modern economies are hypochondriacs of the highest order. They check their own pulses at obsessive intervals, searching for symptoms of weakness, ever ready to fend off decline through stimulants that may or may not help the situation.

In the past few months, we’ve all been encouraged to scout the economic horizon for signs of the dreaded “R” word — a malady so dire, apparently, that its very name may not be uttered in polite company. Throughout the IT world, we’ve been scrambling to put together contingency plans for dealing with a down economy, if and when it materializes. Everyone — IT vendors and users alike — has been hedging their bets and watching their pocketbooks, just in case.

Recently, the IT industry has started to latch onto a curious notion: that business intelligence and performance management applications can help users weather whatever rainy day may or may not come. In other words, analytics applications are increasingly being positioned as tools for determining what to cut, trim and scale back from operations while, hopefully, minimizing adverse impacts on the business.

There is some validity to this viewpoint, though one can’t help thinking it has come into vogue — at least in part — through a self-serving push by vendors of analytic applications. What’s undeniable is that many enterprises are better prepared than ever to deal with economic uncertainty, having invested heavily in business intelligence and analytics over the past few years.

They are well-armed with reporting, scorecarding, dashboarding, forecasting, what-if modeling, interactive visualization, and other analytical tools for sifting through operational data and identifying promising areas for business optimization under tightening economic constraints.

Indeed, the business intelligence industry’s recent emphasis on financial analytics suites has given chief financial officers (CFO) an increasingly sophisticated tool for determining, with surgical precision, where to apply the budget scalpel. By the same token, many human resources directors have powerful human capital analytics for identifying which positions can safely be eliminated, and which hiring decisions may be postponed indefinitely, when the economy goes sour.

Likewise, supplier relationship management analytics tools let companies understand their options for dropping marginal vendors in favour of those that can offer preferential pricing. Still other analytics tools promise similar optimization benefits across the full range of business functions.

Clearly, analytics is a key asset in the ongoing business optimization struggle, both in good times and in bad. There are many business analytics initiatives that can help organizations consolidate, spend modestly and tweak existing processes within fiscal constraints. In fact, one of the key tests of any analytics-driven corporate business model is its ability to deliver superior results in eras of austerity.

For IT professionals, the greatest test may be in how well they safeguard their organizations’ core analytics assets from budget cuts during down economic times. The core issue is: How can you optimize your end-to-end analytic environment — such as, control costs of your business intelligence applications, predictive analytics applications, enterprise data warehouses and other key infrastructure — without impairing service levels or limiting your company’s ability to leverage analytics into new business opportunities?

The following are the most fundamental pointers for protecting your enterprise’s analytic core in a down economy:

  1. Outsource as much of your business intelligence, data warehousing, performance management, and other analytic applications as you can to software-as-a-service providers who can provide it to you on a pay-as-you-go basis. This eliminates the need for you to manage it all yourself from dedicated, in-house data centres with dedicated, full-time staff of your own.
  2. Single-source as many of the functional components of your analytic infrastructures as makes sense from vendors of comprehensive, all-in-one suites of business intelligence, data warehousing, data marts, extract transform load, data cleansing and other critical components. That way, you can obtain a bundled, integrated solution at a lower cost than if you procured these components separately from several vendors.
  3. Consolidate as much of your enterprise data warehouse (EDW) environment as you can — with associated reduction in server hardware, software licenses, and full-time operations and support staff — into fewer, more scalable, more energy-efficient, more cost-effective data centres.
  4. Migrate as much of your less-in-demand operational data to more cost-effective “cold” storage (offline, near-line) as you can, while only keeping the most in-demand data in more expensive “hot” (online) storage in your EDW.
  5. Offload more of your high-volume online analytical processing (OLAP) workloads to the new generation of data warehouse appliances, which can accelerate query processing at a fraction of the cost of traditional data warehouses while also freeing up EDW processing/storage capacity for other workloads.
  6. Virtualize as much of your business intelligence, OLAP and other workloads to grid, massively parallel processing, and other scalable, distributed processing architectures, so that you can run more of this processing on inexpensive commodity servers, share workloads across available CPU and storage resources. And move these loads from mainframes and other “big metal” platforms that are more optimized to online transaction processing.

None of these belt-tightening recommendations should be radically new or unfamiliar to IT professionals. These guidelines should not be regarded as mere IT contingency plans for coping with budget austerity. They are best practices that analytics-driven organizations should implement, in both boom and bust periods, to strengthen their business core.

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