13 February 2009 by Sanjay Saigal.

“It was a glittering time. They confidently swept into office, ready, moving, generating their style, their confidence – they were going to get America moving again. There was a sense that these were brilliant men, men of force, not cruel, not harsh, but men who acted rather than waited. There was no time to wait, history did not permit that luxury; if we waited that would all be past us… Things were going to get be done and it was going to be great fun; challenges awaited and these men did not doubt their capacity to answer these challenges… History summoned them, it summoned us: there was little time to lose.”

Halberstam’s breathy description of the DC gestalt ca. JFK’s inauguration is curiously (and scarily) applicable to today’s America. Ascribing the opposition’s intransigence to mere habit, the Administration is charging ahead to evaluate, diagnose and fix the problems bedeviling our world. This attitude bodes well for researchers of all stripes. A recent email from a major university administrator contained the following phrase - “the campus expects a brief open window of riches”. Things are going to be done and it is going to be great fun!

In this climate of transparent, analytically-driven, decision-making from top to bottom, how does the profession measure up? On the one hand, Operations Research academics are doing what they can to capture the riches soon to rain down from Mt. Washington. However, it’s really interesting on the commercial side of the fence. Especially among vendors, much activity is afoot:

• I mentioned Gurobi’s surprisingly mature LP/MIP solver here. The start-up has taken a tack of ubiquity – making the product available in as many optimization environments as possible. In addition to Excel-compatibility through Frontline’s Solver framework (see Frontline CEO Dan Fylstra’s comment), the optimizer is available in conjunction with three leading algebraic modeling languages, AIMMS, GAMS and MPL. I understand that a wrapper for AMPL is also in process.
• Interestingly, Gurobi is undercutting the price floor maintained by the previously comfortable duopoly of the two benchmark solvers: CPLEX and XPRESS. Instead of $15,000 or more per seat, Gurobi is asking approximately half the amount. While the impact of per-seat license fees in the adoption of optimization technologies is routinely exaggerated, cutting the price in half will definitely perturb the status quo. (And cause not a little consternation in Ilog’s – IBM’s, if you will - executive suite, which has come to depend on CPLEX’s profitability. I suspect that Fair Isaac will be less impacted, since its optimization-based direct revenues represent a much smaller part of its profitability.) Gurobi has not yet shared its deployment pricing model. That will determine its uptake in the significant optimization ISV market.
• In August 2008, Microsoft soft-launched a .NET-oriented optimization framework called Microsoft Solver Foundation (MSF). In addition to a rich .NET API, MSF can also be used as an Excel add-in. (Surprisingly, it does not particularly share the elegance of integration with Excel evident in Frontline’s products.) MSF 1.1, introduced today, even includes Gurobi’s MIP solver (was it code-named Zelig?!) as the default. This very smart choice should make it a powerful solver alternative, especially in small shops, which should be able to use it free for internal use. Longer-term, MSF’s integrative framework designed to incorporate third-party optimizers, backed by the sales heft of Microsoft, could make it a product to be reckoned with.
• SAS Institute, a major player in the Analytics space, has sharpened its focus in the optimization space in recent years. As a privately-held company, its actions are often opaque. SAS’ OR group has recently been in an aggressive hiring mode, though I remain more impressed by the effort than any (known) star hires. Further, its marketing seems to be targeted at its existing customer base. Admittedly, the base is large and lucrative. But it’s difficult to visualize such a narrowly-focused effort expanding its mindshare in the broader market.
• IBM’s acquisition of Ilog and Fair Isaac’s acquisition of Dash Optimization have been written about here and elsewhere. FI is using Dash’s XPRESS engine to extend its largely financial services-based business. Unless the company has a more expansive strategy up its sleeve, that seems like fairly limited leverage. IBM, on the other hand, is selling a fairly pervasive strategy for Ilog’s Optimization and Rules products. Even after filtering out the more egregious spin, the combination of IBM’s consulting reach and its sales prowess cannot but have a multiplicative impact on Ilog’s core strength: technology.

All in all, 2009-2010 promises to be the mother of all “interesting times” for Analytics.

Posted in Operations Research, Vision | 3 Comments »

16 January 2009 by Sanjay Saigal.

In an article on how business schools are responding to globalization, Robert Dolan, the profitability management guru, recounts his introduction to the chasm between theory and practice:

[Dolan…] tells a funny story about when he, a freshly minted PhD in Operations Research, was assigned to teach a course in marketing at the Chicago School of Business.

He didn’t know much about marketing , so he decided he would visit the offices of Proctor & Gamble to find out what it was all about: “I put on my suit and was making my way out of the campus when the dean spotted me and asked me where I was going, all dressed up.

When I told him I was going to Proctor & Gamble to learn about marketing, he asked me, ‘Do they have any Nobel Laureates in Proctor & Gamble?’ When I said ‘I don’t think so,’ he said, ‘Well, we’ve got dozens of Nobel Laureates. So you just stay right here’”.

I thought of Dolan’s (apocryphal?) dean while chatting about possibility of professional certification in OR with colleagues. Though certification is often viewed as a mechanism to help consumers distinguish quality practitioners from the riff-raff, in my view it could meet entirely different goals:

1. Bridge the yawning gap between technique-centered OR programs and the impact-oriented dictates of practice: As I recently observed while bemoaning the lack of good Management Science texts, the academic bent that favors rigor over utility inevitably creeps into pedagogy. Irrespective of the mainstreaming of OR, this sub-optimality is unlikely to change.

2. Position OR to “own” the strengthening brand of Advanced Analytics: An analyst certification that allows (among other things) applicants from other backgrounds to enter the profession could make OR the de facto credentialing authority in a crowded and contested field.

3. Certification could revitalize the OR ecosystem: A new practice-oriented market for course materials, coaching apparatus and examination aids would be needed. The primary beneficiary of such a development would most likely be forward-looking academics.

Posted in Operations Research, Vision | 1 Comment »

26 December 2008 by Sanjay Saigal.

The BBC recently reported on the long-simmering power struggle in Christiandom’s holiest site: the Church of the Holy Sepulchre in Jerusalem. This site – by tradition the place of Jesus Christ’s burial and resurrection – is contested by six denominations that occupy tiny bits of it: Roman Catholic, Greek Orthodox, Armenian Orthodox, Syrian Orthodox, Egyptian Copt and Ethiopian Orthodox. Each faction constantly maneuvers to improve its territory, their shenanigans (real and perceived) often boiling over in eruptions of violence.

The church itself is in a state of near-collapse. But since its ownership is in dispute, all attempts at repair are stymied. The situation is dismally Pareto optimal: any effort at improving matters makes at least one other party unhappy. The following heartrending yet funny anecdote captures the hopelessness of the situation:

The intractable nature of the territorial arguments over the site are epitomised by the short wooden ladder that rests on a ledge above the church’s main entrance.

It has been there since the 19th Century because rival groups cannot agree who has the right to take it down.

Under the Status Quo agreement, rights to the windows reached by the ladder belong to the Armenians, but the ledge below is controlled by the Greeks.

So the messiness persists, despite its ongoing cost to the Christian community. (Not to mention the potential catastrophic downside – complete collapse of the complex’s badly deteriorating roof.)

I was reminded of the Church of the Holy Sepulchre while navigating the recent brouhaha in the Analytics blogonook created by a post in the Intelligent Enterprise blog. Doug Henschen quizzes IBM’s Ambuj Goyal on Big Blue’s “analytics strategy” following its recent acquisition of Ilog, a leader in decision technology components (and my past employer). Henschen’s didactic frame is the notional question: “Will IBM Add Analytics to its Toolbelt?”

Henschen summarizes the interview’s takeaway in his lede as “[IBM contends that] predictive and statistical modeling — key offerings for the likes of SAS and SPSS — are overrated. IBM has what Goyal describes as better, cheaper alternatives in a mix of techniques developed for industry- and domain-specific challenges.” This startling conclusion has been met with (I’ll use a polite word) skepticism by Analytics-oriented blogs. James Taylor at the EDM blog smells the ghost of sales campaigns past:

Sadly this reminded me of the old days of IBM - when FUD (fear, uncertainty and doubt) was IBM’s reponse (sic!) to anything they did not do well. Predictive analytics are not overrated, at least not by anyone who understands them. It is true that predictive analytics, like all good technologies, are sometimes overused by over-enthusiastic supporters and that they can’t do everything. IBM’s lack of this technology is a mistake as without it their solution set is incomplete and no amount of FUD will change that.

Anne Milley at SAS’s company blog SAScom is predictably indignant in light of Goyal’s presumed attack on SAS’s knitting. Milley labels Goyal’s comments “dizzying spin” and suggests that IBM execs “deride the value [of Analytics] because they haven’t been able to monetize the analytics in their research labs even as others achieve significant returns”.

The BBC report came to mind because, as in Jerusalem, the disagreement occasioned by Goyal’s interview is fundamentally doctrinal. It centers on the existential (and ungrammatical) question – what is Analytics?

I have previously mentioned Tom Davenport’s HBR article called “Competing on Analytics”. (Davenport has also published a subsequent book with this title.) Davenport never quite defines Analytics, but his view is obviously expansive; his keystone success story is Marriott Corporation, whose Total Hotel Optimization system relies, at its core on Linear Programming (i.e., Optimization). Davenport sows confusion by using interchangeably using Analytics and labels like “statistical masters”. Analytics is not Statistics. Statistics is a tool in the arsenal of the Analytics professional. But it doesn’t describe the category.  

The SAS Institute has adopted the branding framework of Analytics to compete in the Decision Management space. Historically the purveyor of a statistical toolkit (which later morphed into a Data Warehouse platform,) SAS had weak or non-existent offerings in Optimization and Inferencing/Rules, and came late to the Business Intelligence and Predictive Analytics (BI/PA) wave. So its stance of statistical methods as somehow defining Analytics is natural.

Conversely, prior to the Cognos acquisition, IBM had negligible footprint in the BI/PA space. In terms of Optimization, while boasting incredibly qualified R&D and Consulting groups, it has been unable to develop a profitable software offering. (In the ‘80s IBM attempted to market an optimization toolkit called OSL, made many sub-optimal decisions along the way, and finally killed the product in the late ‘90s. More recently, IBM has tried to leverage an open source toolkit called COIN-OR as a brand-builder, in my opinion essentially futilely.) The acquisition of Ilog provides IBM immediately with best-of-breed offerings in Optimization and Inferencing (or Business Rules). However, it still lacks anything like SAS’s statistical platform. Goyal’s pitch suggests that they know it. And they are trying to advance the notion that what they have is what the market needs.

As is not uncommon, differences born of necessity are being painted in Marketing’s primary colors to manipulate each vendor’s “rightness”. While this makes perfect sense to advance each party’s immediate business imperatives, it does not help the cause of Analytics in the Enterprise. James Taylor correctly observes that:

The important thing is to focus on the decision and then figure out how to solve it… Business rules, optimization, data mining, predictive analytics and adaptive control [are all] necessary ingredients for Enterprise Decision Management and business success.

Indeed.  Let me illustrate with an Analytics success story. In a highly successful Space and Assortment Planning project at a greeting card manufacturer, most of the techniques in Taylor’s list were invoked. SAS was used to derive the space elasticity curves that drove revenue forecasts, CPLEX was used to optimized a complex sequence of layout models, and JRules was used to manage scenarios, preferences and exceptions. As the then Ilog project lead, I cannot recall a discussion where we, or any technology partisan on the large project team, attempted to push a technology agenda. The team uniformly focused on creating the fastest-performing, most accurate and most efficiently usable application.

Along similarly cooperative lines, instead of advocating the relative importance of one’s own bag of tricks and denigrating the competition’s, actors in the Analytics ecosystem need to adopt Davenport’s catholic approach. Let’s focus on expanding the impact of the field, on making it as pervasive a function in business as Accounting or Human Resources. To mash up the rallying cry of Bill Clinton’s 1992 campaign with the verbal stylings of the logorrheic Sarah Palin, “It’s all of the above, stupid”!

Posted in Business Intelligence, Operations Research, Vision, Intechné | 1 Comment »

19 April 2008 by Sanjay Saigal.

At the recently concluded INFORMS 2008 Practice Meeting, multiple colleagues asked about our vision for Intechné. Quite simply, our vision for the company is to reliably deliver smart decision-making capability to our clients.

It goes without saying that smart business decision-making involves advanced analytical techniques from the fields of Operations Research, Statistics, and Artificial Intelligence. These include Predictive Analytics and Data Mining (to detect correlative, possibly causal, relationships in historical data), Monte Carlo Simulation and Decision Analysis (to simulate the impact of such relationships and tease out key sensitivities in anticipative decision-making) and various flavors of Optimization (both mathematically-driven algorithms and less-structured, heuristic approaches). But the application of a wide spectrum of techiques does not necessarily guarantee smart decisions. Intechné differentiates itself by explicitly focusing on an often overlooked issue in applying advanced analytics in the enterprise: Risk.

Viewed in the context of applying advanced analytics to business improvement, risk is like the weather: everybody talks about it, but nobody does anything about it. Or, nothing systematic at any rate. At the purely technical level, approaches such as mathematical optimization produce “brittle” decisions; very small changes in input can produce dramatically different recommendations. Perhaps that’s not of concern in the few situations where human operators are not in the associated decision chain. But in general, analytics are used to support decision, not execute them. Unexplainable, non-intuitive, or volatile decisions often force operators to work around their decision-support systems, or even completely ignore them. For instance, we found a sophisticated SAP/APO installation essentially ignored by its users (demand planners at a Food & Beverage company) because it couldn’t auto-profile different product types. While the overall MAPE was ok, sales forecasts for individual products diverged from reality in unexpected ways.

When it comes to delivering decision-support technology based on advanced analytics, a host of implementation risks arise beyond standard IT development risks. For instance, the response times of constraint programming models can decay exponentially with input size. (This is quite different from, as an example, rule-based decision engines.) Encountered unexpectedly, such non-responsiveness leads to expensive and disruptive modeling/algorithmic rework at an advanced project stage.

All in all, the business of delivering smart business decision-making is characterized by these, and many other risks. In informal feedback from colleagues and clients, we find that project mortality in this area is unacceptably high: about one in three advanced analytics projects fails to perform to expectation.

An active risk management orientation lies at the core of our vision for Intechné. In forthcoming communications we describe how this orientation is incorporated into our practice culture, and how it has been shown to improve client results.

Posted in Vision, Risk | 1 Comment »