What’s Next for SAS?

First, some background.

— SAS is a privately held company.  Founder and CEO Jim Goodnight owns a controlling interest.

Goodnight is 71 years old.

— Goodnight’s children are not engaged in management of the business.

Within the next few years, SAS faces a dual transition of management and ownership.   This should be a concern for customers and prospective customers; due to SAS’ proprietary architecture, building on the SAS platform necessarily means a long-term bet on the future of the company.  Suppose, for example, that IBM acquires SAS: will SAS continue to support interfaces to Oracle and Teradata?

Succession is a problem for any business;  it is especially so for a founder-managed business, where ownership must change as well as management.   Goodnight may be interested in SAS as a going concern, but his heirs are more likely to want its cash value, especially when the IRS calls to collect estate taxes.

Large founder-managed firms typically struggle with two key issues.  First, the standards of corporate governance in public companies differ markedly from those that apply to private companies.  The founder’s personal business may be closely intermingled with corporate business in a manner that is not acceptable in a public company.

For example, suppose (hypothetically) that Goodnight or one of his personal entities owns the land occupied by SAS headquarters in Cary, North Carolina; as a transaction between related parties, such a relationship is problematic for a public company.   Such interests must be unwound before an IPO or sale to a public company can proceed; failure to do so can lead to serious consequences, as the Rigas brothers discovered when Adelphia Communications went public.

The other key issue is that founders may clash with senior executives who demonstrate independent thought and leadership.  Over the past fifteen years, a number of strong executives with industry and public company experience have joined SAS  through acquisition or hire; most exited within two years.  The present SAS management team consists primarily of long term SAS employees whose leadership skills are well adapted to survival under Goodnight’s management style.  How well this management team will perform when out from under Goodnight is anyone’s guess.

SAS flirted with an IPO in 1999, at the height of the tech-driven stock market boom, and hired ex-Oracle executive Andre Boisvert as COO to lead the transition.  Preparations for the IPO proceeded slowly; Boisvert clashed with Goodnight and left.  SAS shelved the IPO soon thereafter.

Subsequent to this episode, Goodnight told USA Today that talk about an IPO was never serious, that he had pursued an IPO for the benefit of the employees, and abandoned the move because employees were against it.    In the story, USA Today noted that this claim appeared to be at odds with Goodnight’s previous public statements.  The reader is left to wonder whether the real reason has something to do with Goodnight’s personal finances, or if he simply did not want to let go of the company.  In any case, it’s not surprising that many SAS employees opposed an IPO, since Boisvert reportedly told employees at a company meeting that headcount reduction would follow public ownership.

Since then, there have been opportunities to sell the company in whole or in part.  IBM tried to acquire the company twice.  Acquisition by IBM makes a lot of sense; SAS built its business on the strength of its IBM technology partnership; SAS still earns a large share of its revenue from software running on IBM hardware.  Both companies have a conservative approach to technology, preferring to wait until innovations are proven before introducing them to blue chip customers.

But Goodnight rebuffed IBM’s overtures and bragged about doing so, claiming an exaggerated value for SAS of $20 billion, around ten times sales at the time.  It’s not unknown for two parties to disagree about the value of a company.   But according to a SAS insider, Goodnight demanded that IBM agree to his price “without due diligence”, which no acquiring company can ever agree to do.  That seems like the behavior of a man who simply does not want to sell to anyone, under any circumstances.

Is SAS really worth ten times revenue?  Certainly not.  SAS’ compound annual revenue growth rate over the past twenty years is around 10%, which suggests a revenue multiplier of a little under 4X at current valuations (see graph below).  Of course, that assumes SAS’ past revenue growth rate is a good indicator of its future growth, which is a stretch when you consider the saturation of its market, increased competition and limited customer response to “game-changing” new products.

Software Industry Rev Gro and Mult
Source: Yahoo Finance. Market Capitalization and Revenue for publicly owned software companies

One obstacle to sale of the company is Goodnight’s stated unwillingness to sell to buyers who might cut headcount.  SAS’ company culture is the subject of business school case studies and the like, but the unfortunate truth is that SAS’ revenue per employee badly lags the IT industry, as shown in the table below.  SAS appears to be significantly overstaffed relative to revenue compared to other companies in the industry, and markedly so compared to any likely acquirer.

Table of RPE
Source: Yahoo Finance; SAS Website

One could speculate about the causes of this relatively low revenue per employee — I won’t — but an acquiring company will expect this to improve.  Flogging the business for more sales seems like pushing on a string — according to company insiders, SAS employs more people in its Marketing organization than in its Research and Development organization.  An acquirer will likely examine SAS’ product line, which consists of a few strong performers — the “Legacy” SAS software, such as Base and STAT — and a long list of other products, many of which do not seem to be widely used.  Rationalization of the SAS product line — and corresponding headcount — will likely be Job One for an acquirer.

So what’s ahead for SAS?

One option: Goodnight can simply donate his ownership interest in SAS to a charitable trust, which would continue to manage the business much the way Hershey Trust manages Hershey Foods.   This option would be least disruptive to customers and employees, and the current management team would likely stay in place (if the Board is stacked with insiders, locals and friends).    It’s anyone’s guess how likely this is; such a move would be consistent with Goodnight’s public statements about philanthropy, but unlike Larry Ellison, Goodnight hasn’t signed Warren Buffett’s Giving Pledge.

But if Goodnight needs the cash, or wants his heirs to inherit something, a buyer must be found.  Another plausible option consistent with Goodnight’s belief in the virtues of private ownership would be a private equity led buyout.  The problem here is that while private equity investors might be willing to put up with either low sales growth or low employee productivity, they won’t tolerate both at the same time.    A private equity investor would likely treat the Legacy SAS software as a cash cow, kill off or spin off the remaining products, and shed assets.   The rock collection and the culinary farm will be among the first to go.

There are a limited number of potential corporate buyers.  IBM, H-P, Oracle, Dell and Intel all sell hardware that supports SAS software, and all have a vested interest in SAS, but it seems unlikely that any of these will step up and buy the company.   Twice rebuffed, IBM has moved on from SAS, reporting double-digit growth in business analytics revenue while SAS struggles to put up single digits.   H-P and Dell have other issues at the moment.  Oracle could easily put up $10 billion in cash to buy SAS, and Oracle’s analytic story would benefit if SAS were added to the mix, but I suspect that Oracle doesn’t think it needs a better analytics story.

SAP has the resources to acquire SAS; a weak dollar favors acquirers from outside of the United States.  Such a transaction would add to SAP’s credibility in analytics, which isn’t strong (the recently announced acquisition of KXEN notwithstanding).   Until recently, there was no formal partnership between the two companies, and SAS executives spent the better part of the last SAS Global Forum strutting around the stage sniping at SAP HANA.  It will be interesting to see how this alliance develops.


A reader on Twitter asks: what about employee ownership?  Well, yes, but if Goodnight wants to sell the company, the employees would need to come up with the market price of $10-11 billion.  That works out to about $750,000 for each employee.  There are investors who would consider lending the capital necessary for an employee-led buyout, but they would subject the business and its management to the same level of scrutiny as an independent buyer.

A Few Interesting Things About SAS Visual Analytics

This post is more than two years old, but remains popular.  For an updated discussion, read How to Buy SAS Visual Analytics on this blog.

Thanks to a white paper recently published by an H-P engineer, we now have a better idea about what it takes to implement SAS Visual Analytics, SAS’ in-memory BI and visualization platform.

(Note: SAS has taken down the white paper since this post was published).

(Updated again June 27:   SAS has reposted an edited version of the white paper, with interesting parts removed.  The paper currently posted at this link is not the original.)

It’s an interesting picture.

A few key points:

(1) Implementation is a science project.  

Quoting from the paper:

…too often the needed pre-planning does not occur and the result is weeks to months of frantic activity to address those issues which should and could have been addressed earlier and in a more orderly fashion.

Someone should explain to SAS and H-P that vendors are supposed to provide customers with honest guidance about how to implement a product.  If “needed pre-planning does not occur”, it’s likely because the customer wasn’t told it was necessary.  Of course, some customers ignore vendor guidance; but if the issues described in this white paper are systematic (as the author suggests), there are only two plausible explanations: (a) the vendors don’t know how to implement the product, or (b) they’re positioning the product as an “appliance” that is “ready to run.”

Elsewhere in the paper, the author notes that a response to a key question about how to monitor this application is “evolving”.  In other words, thirteen months and three releases into production and the vendors still don’t have an answer.

(2) Pre-Release testing?  What’s that?


Experience on initial installations is showing that networking is proving to be one of the biggest challenges and impediments to a timely implementation.

Well, duh.  This is the sort of thing ordinarily revealed in something called “system testing” and “benchmarking”, the product of which is something called “reference architecture”.   Smart people generally think it’s a good idea to do this sort of testing and benchmarking before you release a product rather than figuring it out in the course of “initial installations”.

Pity the early adopters for this product.

(Data and management networks) are typically overlooked and are the cause of most issues and delays encountered during implementation.

Well, why are they overlooked?   Ordinarily when implementing a product one starts with something called an “architecture review” where you — I’m talking to you, SAS and H-P — tell the customer how the product works and point out these network thingies and why it’s a good idea to provision them and not leave them just hanging out there.

(3) It’s not an appliance.

The author refers to the hardware VA sits on as an “appliance”; word on the street is that SAS reps position VA as an alternative to appliances (such as IBM PureData, Teradata Aster or EMC Greenplum DCA).   Well, caveat emptor on that.  The author goes into an extended discussion of data networking, with much interesting detail on such topics as the type of cables you will need to wire this thing together (copper or glass fiber).

It seems that you need lots of cable, and for good performance you need good cable.

(4) Implement with care.

The potential exists, with even as few as 4 servers, for a Data Storm to occur.

No, he does not mean the Amiga game.    For those not hip to the lingo, a Data Storm is a Really Bad Thing that you don’t want to happen in your IT environment, and vendors generally design products that don’t set off Data Storms.

A Data Storm is to Business Intelligence what train wrecks are to travel.

(5) Infrastructure requirements may be daunting.

After an extended discussion of IP addresses and the load this product places on your network, the author writes:

Since a switch with 100s to 1000s of ports is required to achieve the consolidation of network traffic, list price can start at about US$500,000 and be into the millions of dollars.

And after more extended discussion about networking and cabling:

…while all this sounds very scary and expensive….

Dude, you have no idea.

…there can be assistance from vendors during the hardware ordering process that makes this simpler and clearer to comprehend.

No doubt.  Hardware vendors will be happy to explain all of this to you.

(6)  High Availability?  Not exactly.

The author opens the High Availability section with a Readiness Checklist detailing whether or not you should even attempt to cold swap a SAS Head Node.  The answer: it depends.  Left unanswered: what to do if the Readiness Checklist says Do Not Attempt.  I presume that the answer is “call H-P”, but I’m just guessing.

This leads to the next section:

How to transplant a SAS Head Node and survive the experience (you hope) in a SAS VA configuration.

Nine steps follow, closing with “Assuming that everything seems to be in working order…”

(7) Finding people to keep this running will be a challenge.

The author is a twenty-year veteran of SAS and H-P (with four acronyms after his name), and his paper is littered with words like “scary”, “problematic”  and “crisis”.

Comments on “SAS’ Nimble Dance”

There are many subjects in analytics more interesting than the SAS PR operation, but my Google Tracker pinged a few times this week after SAS successfully planted this article in the New York Times.  This morning I feel like shooting fish in a barrel, so here are four brief comments.

Steve Lohr writes:

In 2009, I wrote a long piece that looked at SAS and the challenges it faced. The headline read, “At a Software Powerhouse, the Good Life Is Under Siege.”

The piece in question — which looked like an IBM plant at the time — drew great mirth at SAS, especially the part about how hard-working SAS managers check email while driving home.   That explains why some say it’s dangerous to cross Campus Drive at 5:05.

A new version, coming in June, will be able to run entirely in remote “cloud” data centers. “It’s a complete cloud distribution, totally cloud-ready,” James Goodnight, co-founder and chief executive of SAS, said in an interview…Those clouds can be private ones operated by companies or government agencies. But SAS has its own hosted data centers, and its software now also runs on Amazon’s Web Services cloud.

Someone should explain to Mr. Lohr that the point of “Infrastructure as a service” is that you do not need special software,  unless of course your software license agreement is unduly restrictive, or your software vendor has a cumbersome license key.   “Now” appears to be 2011, according to this thread from the SAS support site; running SAS on AWS is not exactly a new thing, although doing so seems to be a science project according to the thread.  SAS crowing that its software is “now ready for Cloud” reminds me of folks at Electronic Arts crowing that you can now run SimCity because they bought more servers.

On Wednesday, SAS executives came to New York for an event at the Pierre Hotel to show off its retooled technology to customers. The code has been rewritten to run on modern hardware — so-called massively parallel computers….SAS has developed new visual tools — so users can do data analysis with a point-and-click on a laptop, or swipe-and-tap on an iPad tablet, as SAS demonstrated this week. The goal is to broaden the base of SAS users well beyond its traditional core of SAS-trained data experts. “Democratizing data is exactly what this is about,” said James Davis, an SAS senior vice president and chief marketing officer.

“Democratizing data” may or may not be a smart strategy for SAS; time will tell.   But what about “SAS-trained data experts”, what does this announcement mean for them?  SAS seems to be telling “SAS-trained data experts”  that they are working in software not designed to run on modern hardware, a point that many loyal SAS customers will be surprised to learn.

As a private company, SAS does not report its financial results. But Mr. Goodnight said its revenue grew 5.5 percent last year, held down by weakness in Europe and a strong dollar against the euro, which reduced reported sales. Europe is about the size of the United States as a market for SAS.

Nice try, Dr. Goodnight.  In 2012, the dollar declined against the Euro, by about three percent, which increased the dollar-denominated value of European sales.  In any event, all software companies operate in the same currency environment and, as noted here, IBM and SAP reported double-digit growth in 2012.

SAS Analyst Conference: Take Two

Analyst comments about SAS’ 24th annual analyst conference continue to dribble out.   Ordinarily, events like this produce a storm of Google alerts, but this year the quiet speaks volumes.   Yesterday, Tony Cosentino of Ventana Research published his perspective on the conference, writing at length about SAS Visual Analytics; link here.

Here are a few quotes from Mr. Cosentino’s post, with my embedded comments.

“For SAS, the competitive advantage in Big Data rests in predictive analytics…

…a capability that is completely absent from the current version of SAS Visual Analytics, the software featured in Mr. Cosentino’s article.  The big “news” of the analyst conference is that SAS says they plan to add some toylike predictive analytics to Visual Analytics this year, which will give the application functional parity with, say, MicroStrategy vintage 1999.  I don’t completely understand why this is news at all, since SAS said they would do this at the analyst conference last year, but spent 2012 attempting to sell their other in-memory architecture without visible success.

“…according to our benchmark research into predictive analytics, 55 percent of businesses say the challenge of architectural integration is a top obstacle to rolling out predictive analytics in the organization.”

No doubt this is true, and SAS’ proprietary server-based architecture is one reason why this is a problem.  SAS/STAT, for example, is still one of the most widely used SAS products, and it exports predictive models to nothing other than SAS.  SAS Visual Analytics simply adds to the clutter by introducing an entirely new architecture into the mix that is hard to integrate with legacy SAS products in the same category.  For more details about the data integration challenges posed by SAS Visual Analytics, see my previous post.

“Integration of analytics is particularly daunting in a big-data-driven world, since analytics processing has traditionally taken place on a platform separate from where the data is stored…”

A trend that continues with SAS Visual Analytics, which is deployed on a platform separate from where the data is stored.

Jim Goodnight, the company’s founder and plainspoken CEO, says he saw the industry changing a few years ago. He speaks of a large bank doing a heavy analytical risk computation that took upwards of 18 hours, which meant that the results of the computation were not ready in time for the next trading day.

Banks have suffered serious performance issues with analytics for more than “a few years”.    And 18 hours is pretty good compared to some; there are organizations with processes that take days and weeks to run in SAS.

Goodnight also discussed the fact that building these parallelizing statistical models is no easy task. One of the biggest hurdles is getting the mathematicians and data scientists that are building these elaborate models to think in terms of the new parallelized architectural paradigm.

Really?  Parallelized algorithms for statistics and data mining are hardly new, and commercial versions first appeared on the market in 1994.  There are companies with a fraction of SAS’ headcount that are able to roll out parallelized algorithms without complaining about how hard it is to do.  A few examples:  Alpine Data Labs,  Fuzzy Logix,   Revolution Analytics (my current employer) and Skytree.

The biggest threat to SAS today is the open source movement, which offers big data analytic approaches such as Mahout and R.

If this is true, SAS Visual Analytics is not an effective response because it caters to a completely different user persona.  The biggest threats to SAS today are IBM, SAP and Oracle, who have the analytic tooling, deep pockets and credibility to challenge SAS in the enterprise analytics market.  SAS Visual Analytics seems more like an attempt to compete with SAP HANA.

At the same time, SAS possesses blueprints for major analytic processes across different industries as well as horizontal analytic deployments, and it is working to move these to a parallelized environment. This may prove to be a differentiator in the battle versus R, since it is unclear how quickly the open source R community, which is still primarily academic, will undertake the parallelization of R’s algorithms.

Actually, it’s already done.

SAS Analyst Conference

SAS held its annual Analyst Conference in Steamboat Springs, Colorado last week, an event that drew scant buzz from persons not on the SAS payroll.   For a good summary of major news from the event, check Cindi Howson’s post on the BI Scorecard blog (link here).

A few key points:

(1) SAS isn’t talking about SAS High Performance Analytics Server, its marquee in-memory software for predictive analytics.   This product went into production fourteen months ago and has no public reference customers to date.  Given the full-court marketing press SAS gave to this product last year, the implication is that (a) nobody’s buying it; (b) it doesn’t work; or both.

(2) SAS continues to provide a public speaking platform for a “customer” who sings the praises of High Performance Analytics Server but hasn’t actually purchased the product.  Note to analysts: if some guy tells you how much he likes the product, ask him if he bought it.

(3) On the other hand, SAS is aggressively promoting its other in-memory software (Visual Analytics), which seems to be selling smartly.  (SAS has a target to sell 1,000 licenses in North America this year).  Visual Analytics is a slick in-memory BI tool, that currently does not support predictive analytics.

(4) SAS plans to add some simple predictive analytics to Visual Analytics in 2013.

(5) SAS’ BI revenues grew only 3.2% in 2012, compared to the double digit growth reported by other vendors.   This quote from Gartner’s most recent BI Magic Quadrant offers insight into why:

References continue to report that SAS is very difficult to implement and use — it was the No. 3 vendor in both categories. Aggravating this, although it has a worldwide network of support centers and an extensive list of service partners, SAS’s customer experience and product support are in the lower quartile of vendors in the Magic Quadrant. A revision of user interfaces and an enhancement of product integration is under way to help improve the customer experience, but SAS must also improve its level of service — including level of expertise, response time and time to resolution.

(6)  As Howson notes, Visual Analytics “…may offer a more modern and appealing interface, but only when data has been loaded into memory on the SAS LASR Server.”  And there’s the rub, because it turns out that loading data into Visual Analytics is not exactly a day in the park.

(7) According to SAS product documentation,  there are exactly two ways to load data into VA: from a registered table in a relational database or from a SASHDAT file stored in HDFS.   According to SAS, the first option is “appropriate for smaller data sets because the data must be transferred over the network.  If the table is unloaded or the server stops, the data must be transferred over the network again.”   So if you’re working with Big Data, the only way to load data into VA is to first create a file in SAS’ proprietary SASHDAT format, store the file in HDFS, then load it into VA.  And by the way, you must use SAS Data Integration Server to create a SASHDAT file.  Surprise!  More SAS software to buy.

(8) Howson misses the obvious point, though, that SAS Visual Analytics cannot read data from Hadoop unless it has been previously extracted and reloaded in SAS’ closed format.  Which misses the point of using Hadoop in the first place.

(9) The other big announcement is that SAS now says it will support public cloud.  Yay.  I’m reminded of this article from November 2011, in which SAS CEO Jim Goodnight declared that cloud computing is “a lot of hype.”   Color me shocked.  It seems that when Jim Goodnight makes public statements about SAS’ product direction, we really can’t take him seriously.

Analytic Applications, Part Four: Enabling Customers

This post is the last in a four-part series covering analytic applications organized according to how enterprises consume analytics.

Part One (here) covered Strategic Analytics, or analytics that address C-suite questions and issues.

Part Two (here) covered Managerial Analytics, which serve to measure the performance of products and programs at a departmental level, and to optimize the allocation of resources across programs.

Part Three (here) covered Operational Analytics, analytics that improve the efficiency or effectiveness of business processes.

All of these applications have one thing in common: they exist to serve internal needs of the enterprise, which retains the value produced by analytics.  This is not a bad thing; credit card customers benefit indirectly when an issuer  uses analytics to avoid giving credit to customers who subsequently default, but the firm itself is the direct and primary beneficiary of the credit risk analysis.

Customer-enabling analytics turn this logic on its head: the analytics are designed to provide a benefit to customers, while the enterprise benefits indirectly through product differentiation, goodwill or some combination of the two.

There are four distinct categories of Customer-Enabling Analytics:

  • Analytic Services
  • Prediction Services
  • Analytic Applications
  • Product-Embedded Analytics

On the surface, Analytic Services provided by consulting firms, marketing service providers and so forth are simply a sourcing alternative for the previously defined Strategic, Managerial or Operational Analytics, but not fundamentally different.   In practice, however, analytics delivered by service providers tend to be very different than analytics developed “in-house”.  With few barriers to entry, the market for Analytic Services is highly competitive; as a result, successful providers tend to be highly innovative and specialized, offering services that cannot be easily reproduced.  Moreover, the relationship between service provider and enterprise consumer (and the visible costs associated with a project) tend to ensure that project goals are well-defined, a step that is often omitted from internally-delivered analytics (to the detriment of all engaged).

For Analytic Services, the “product” sold and delivered is an analysis project, which is typically priced based on the effort required to complete the project and the time-value of resources consumed.  For Prediction Services, the product sold and delivered to the customer is a prediction, not a project, and is typically priced on a per-use basis.  Credit scores are the best-known example of Prediction Services, but there are many other examples of prediction services for sales, marketing, human resources, insurance underwriting.  As with Analytic Services, the end uses to which Prediction Services appear to be the same as in-house delivered Strategic, Managerial and Operational Analytics, but in practice externally developed Prediction Services work in a very different way.  Since the development and deployment costs for a predictive model are amortized over a large volume of transactions,  Prediction Services enable a broad market of smaller enterprises to benefit from predictive analytics that would not be able to do so otherwise.  Prediction Service providers are also able to achieve economies of scale, and often have access to data sources that would not necessarily be available to the enterprise.

Analytic Applications are a natural extension of Analytic Services and Prediction Services.  Analytic Applications are business applications that consume data-driven predictions and support all or part of a business process.  Examples include:

  • Mortgage application decision systems (which consume predictions about the applicant’s propensity to repay the loan)
  • Insurance underwriting systems (consume predictions about expected losses from an insurance policy)
  • Fraud case management systems (consume predictions about the likelihood that a particular claim or group of claims is fraudulent)

These applications are often sold and delivered by providers under a “razor-and-blade” strategy, where the application itself is delivered under a fixed price and combined with a long-term contract to provide Analytic Services or Prediction Services.

Each of the first three categories of Customer-Enabling Analytics is similar to and competes with “in-house” delivered Strategic, Managerial and Operational Analytics.  The fourth category, Product-Embedded Analytics, is potentially the most disruptive and offers enterprises the greatest potential return.  Product-Embedded Analytics differentiate the firm’s products in meaningful ways by solving a consumer problem.

If this sounds esoteric, it is because the best examples are often not thought of in the same way we think about other kinds of analytics:

  • Consumers have a problem finding information.  Google’s search engine solves this problem.
  • Consumers have a problem finding a movie they want to watch.  Netflix’ recommendation engine solves this problem.

These examples — and many others, including Facebook’s newsfeed engine, Match.com’s matching algorithm — use machine learning technology in ways that directly benefit customers.   But the firms that offer these services benefit indirectly, by building site traffic, selling more product or satisfying customers in a manner that cannot be readily reproduced by competitors.

Analytic Applications (Part Three): Operational Analytics

This is the third post in a series on analytic applications organized by how analytic work product is used within the enterprise.

  • The first post, linked here, covers Strategic Analytics (defined as analytics for the C-Suite)
  • The second post, linked here, covers Managerial Analytics (defined as analytics to measure and optimize the performance of value-delivering units such as programs, products, stores or factories).

This post covers Operational Analytics, defined as analytics that improve the efficiency or effectiveness of a business process.  The distinction between Managerial and Operational analytics can be subtle, and generally boils down to the level of aggregation and frequency of the analysis.  For example, the CMO is interested in understanding the performance and ROI of all Marketing programs, but is unlikely to be interested in the operational details of any one program.  The manager of that program, however, may be intensely interested in its operational details, but have no interest in the performance of other programs.

Differences in level of aggregation and frequency lead to qualitative differences in the types of analytics that are pertinent.  A CMO’s interest in Marketing programs is typically at a level of “keep or kill”;  continue funding the program if its effective, kill it if it is not.  This kind of problem is well-suited to dashboard-style BI combined with solid revenue attribution, activity based costing and ROI metrics.  The Program Manager, on the other hand, is intensely interested in a range of metrics that shed insight not simply on how well the program is performing, but why it is performing as it is and how to improve it.  Moreover, the Program Manager in this example will be deeply involved in operational decisions such as selecting the target audience, determining which offers to assign, handling response exceptions and managing delivery to schedule and budget.  This is the realm of Operational Analytics.

While any BI package can handle different levels of aggregation and cadence, the problem is made more challenging due to the very diverse nature of operational detail across business processes.   A social media marketing program relies on data sources and operational systems that are entirely different from web media or email marketing programs; preapproved and non-pre-approved credit card acquisition programs do not use the same systems to assign credit lines; some or all of these processes may be outsourced.  Few enterprises have successfully rationalized all of their operational data into a single enterprise data store (nor is it likely they will ever do so).  As a result, it is very rare that a common BI system comprehensively supports both Managerial and Operational analytic needs.  More typically, one system supports Managerial Analytics (for one or more disciplines), while diverse systems and ad hoc analysis support Operational Analytics.

At this level, questions tend to be domain-specific and analysts highly specialized in that domain.  Hence, an analyst who is an expert in search engine optimization will typically not be seen as qualified to perform credit risk analysis.  This has little to do with the analytic methods used, which tend to be similar across business disciplines, and more to do with the language and lingo used in the discipline as well as domain-specific technology and regulatory issues.  A biostatistician must understand common health care data formats and HIPAA regulations; a consumer credit risk analysis must understand FICO scores, FISERV formats and FCRA.  In both cases, the analyst must have or develop a deep understanding of the organization’s business processes, because this is essential to recognizing opportunities for improvement and prioritizing analytic projects.

While there is a plethora of different ways that analytics improve business processes, most applications fall in to one of three categories:

(1) Applied decision systems supporting business processes such as customer-requested line increases or credit card transaction authorizations.  These applications improve the business process by applying consistent data-driven rules designed to balance risks and rewards.  Analytics embedded in such systems help the organization optimize the tradeoff between “loose” and “tight” criteria, and ensure that decision criteria reflect actual experience.  An analytics-driven decisioning system performs in a faster and more consistent way than systems based on human decisions, and can take more information into account than a human decision-maker.

(2) Targeting and routing systems (such as a text-processing system that reads incoming email and routes it to a customer service specialist).  While applied decision systems in the first category tend to recommend categorical yes/no, approve/decline decisions in a stream of transactions, a targeting system selects from a larger pool of candidates, and may make qualitative decisions among a large number of alternate routings.   The business benefit from this kind of system is improved productivity and reduced processing time as, for example, the organization no longer requires a team to read every email and route it to the appropriate specialist.  Applied analytics make these systems possible.

(3) Operational forecasting (such as a system that uses projected store traffic to determine staffing levels).   These systems enable to organization to operate more efficiently through better alignment of operations to customer demand.  Again, applied analytics make such systems possible; while it is theoretically possible to build such a system without an analytic forecasting component, it is inconceivable that any management would risk the serious customer service issues that would be created without one.  Unlike the first two applications, forecasting systems often work with aggregate data rather than atomic data.

For analytic reporting, the ability to flexibly ingest data from operational data sources (internal and external) is critical, as is the ability to publish reports into a broad-based reporting and BI presentation system.

Deployability is the key requirement for predictive analytics; the analyst must be able to publish a predictive model as a PMML (Predictive Model Markup Language) document or as executable code in a choice of programming languages.

In the next post, I will cover the most powerful and disruptive form of analytics, what I call Customer-Enabling Analytics: analytics that differentiate your products and services and deliver value to the customer.

Book Review: Big Data Big Analytics

Big Data Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses, by Michael Minelli, Michele Chambers and Ambiga Dhiraj.

Books on Big Data tend to fall into two categories: they are either “strategic” and written at a very high level, or they are cookbooks that tell you how to set up a Hadoop cluster.  Moreover, many of these books focus narrowly on data management — an interesting subject in its own right for those who specialize in the discipline, but yawn-inducing for managers in Sales, Marketing, Risk Management, Merchandising or Operations who have businesses to run.

Hey, we can manage petabytes of data.  Thank you very much.  Now go away.

Big Data Big Analytics appeals to business-oriented readers who want a deeper understanding of Big Data, but aren’t necessarily interested in writing MapReduce code.   Moreover, this is a book about analytics — not just how we manage data, but what we do with it and how we make it drive value.

The authors of this book — Michael Minelli, Michele Chambers and Ambiga Dhiraj — combine in-depth experience in enterprise software and data warehousing with real-world experience delivering analytics for clients.  Building on interviews with a cross-section of subject matter experts — there are 58 people listed in the acknowledgements — they map out the long-cycle trends behind the explosion of data in our economy, and the expanding tools to manage and learn from that data.  They also point to some of the key bottlenecks and constraints enterprises face as they attempt to deal with the tsunami of data, and provide sensible thinking about how to address these constraints.

Big Data Big Analytics includes rich and detailed examples of working applications.  This is refreshing; books in this category tend to push case studies to the back of the book, or focus on one or two niche applications.  This book documents the disruptive nature of Big Data analytics across numerous vertical and horizontal applications, including Consumer Products, Digital Media, Marketing, Advertising, Fraud and Risk Management, Financial Markets and Health Care.

The book includes excellent chapters that describes the technology of Big Data, chapters on Information Management, Business Analytics, Human Factors — people, process, organization and culture.   The final chapter is a good summary of Privacy and Ethics.

The Conclusion aptly summarizes this book: it’s not how much data you have, it’s what you do with it that matters.  Big Data Big Analytics will help you get started.

Analytic Applications (Part Two): Managerial Analytics

This is the second in a four-part taxonomy of analytics based on how the analytic work product is used.  In the first post of this series, I covered Strategic Analytics, or analytics that support the C-suite.  In this post, I will cover Managerial Analytics: analytics that support middle management, including functional and regional line managers.

At this level, questions and issues are functionally focused:

  • What is the best way to manage our cash?
  • Is product XYZ performing according to expectations?
  • How effective are our marketing programs?
  • Where can we find the best opportunities for new retail outlets?

There are differences in nomenclature across functions, as well as distinct opportunities for specialized analytics (retail store location analysis, marketing mix analysis, new product forecasting), but managerial questions and issues tend to fall into three categories:

  • Measuring the results of existing entities (products, programs, stores, factories)
  • Optimizing the performance of existing entities
  • Planning and developing new entities

Measuring existing entities with reports, dashboards, drill-everywhere (etc.) is the sweet spot for enterprise business intelligence systems.  Such systems are highly effective when the data is timely and credible, reports are easy to use and the system reflects a meaningful assessment framework.  This means that metrics (activity, revenue, costs, profits) reflect the goals of the business function and are standardized to enable comparison across entities.

Given the state of BI technology, analysis teams within functions (Marketing, Underwriting, Store Operations etc.) spend a surprisingly large amount of time preparing routine reports for managers.  (For example, an insurance client asked my firm to perform an assessment of actual work performed by a group of more than one hundred SAS users.  The client was astonished to learn that 80% of the SAS usage could be done in Cognos, which the client also owned).

In some cases, this is simply due to a lack of investment by the organization in the necessary tools and enablers, a problem that is easily fixed.  More often than not, though, the root cause is the absence of consensus within the function of what is to be measured and how performance should be compared across entities.   In organizations that lack measurement discipline, assessment is a free-for-all where individual program and product managers seek out customized reports that show their program or product to the best advantage; in this environment, every program or product is a winner and analytics lose credibility with management.  There is no technical “fix” for this problem; it takes leadership for management to set out clear goals for the organization and build consensus for an assessment framework.

Functional analysts often complain that they spend so much time preparing routine reports that they have little or no time to perform analytics that optimize the performance of existing entities.  Optimization technology is not new, but tends to be used more pervasively in Operational Analytics (which I will discuss in the next post in this series).   Functionally focused optimization tools for management decisions have been available for well over a decade, but adoption is limited for several reasons:

  • First, an organization stuck in the “ad hoc” trap described in the previous paragraph will never build the kind of history needed to optimize anything.
  • Second, managers at this level tend to be overly optimistic about the value of their own judgment in business decisions, and resist efforts to replace intuitive judgment with systematic and metrics-based optimization.
  • Finally, in areas such as Marketing Mix decisions, constrained optimization necessarily means choosing one entity over another for resources; this is inherently a leadership decision, so unless functional leadership understands and buys into the optimization approach it will not be used.

Analytics for planning and developing new entities (such as programs, products or stores) usually require information from outside of the organization, and may also require skills not present in existing staff.  For both reasons, analytics for this purpose are often outsourced to providers with access to pertinent skills and data.  For analysts inside the organization, technical requirements look a lot like those for Strategic Analytics: the ability to rapidly ingest data from any source combined with a flexible and agile programming environment and functional support for a wide range of generic analytic problems.

In the next post in this series, I’ll cover Operational Analytics, defined as analytics whose purpose is to improve the efficiency or effectiveness of a business process.

Analytic Applications (Part One)

Conversations about analytics tend to get muddled because the word describes everything from a simple SQL query to climate forecasting.  There are several different ways to classify analytic methods, but in this post I propose a taxonomy of analytics based on how the results are used.

Before we can define enterprise best practices for analytics, we need to understand how they add value to the organization.  One should not lump all analytics together because, as I will show, the generic analytic applications have fundamentally different requirements for people, processes and tooling.

There are four generic analytic applications:

  • Strategic Analytics
  • Managerial Analytics
  • Operational Analytics
  • Customer-Enabling Analytics

In today’s post, I’ll address Strategic Analytics; the rest I’ll cover in subsequent posts.

Strategic Analytics directly address the needs of the C-suite.  This includes answering non-repeatable questions, performing root-cause analysis and supporting make-or-break decisions (among other things).   Some examples:

  • “How will Hurricane Sandy impact our branch banks?”
  • “Why does our top-selling SUV turn over so often?”
  • “How will a merger with XYZ Co. impact our business?”

Strategic issues are inherently not repeatable and fall outside of existing policy; otherwise the issue would be delegated.   Issues are often tinged with a sense of urgency, and a need for maximum credibility; when a strategic decision must be taken, time is of the essence, and the numbers must add up.   Answers to strategic questions frequently require data that is not readily accessible and may be outside of the organization.

Conventional business intelligence systems do not address the needs of Strategic Analytics, due to the ad hoc and sui generis nature of the questions and supporting data requirements.   This does not mean that such systems add no value to the organization; in practice, the enterprise BI system may be the first place an analyst will go to seek an answer.  But no matter how good the enterprise BI system is, it will never be sufficiently complete to provide all of the answers needed by the C-suite.

The analyst is key to the success of Strategic Analytics.  This type of work tends to attract the best and most capable analysts, who are able to work rapidly and accurately under pressure.  Backgrounds tend to be eclectic: an insurance company I’ve worked with, for example, has a strategic analysis team that includes an anthropologist, an economist, an epidemiologist and graduate of the local community college who worked her way up in the Claims Department.

Successful strategic analysts develop domain, business and organizational expertise that lends credibility to their work.  Above all, the strategic analyst takes a skeptical approach to the data, and demonstrates the necessary drive and initiative to get answers.  This often means doing hard stuff, such as working with programming tools and granular data to get to the bottom of a problem.

More often than not, the most important contribution of the IT organization to Strategic Analytics is to stay out of the way.  Conventional IT production standards are a bug, not a feature, in this kind of work, where the sandbox environment is the production environment.  Smart IT organizations recognize this, and allow the strategic analysts some latitude in how they organize and manage data.   Dumb IT organizations try to force the strategic analysis team into a “Production” framework.  This simply inhibits agility, and encourages top executives to outsource strategic issues to outside consultants.

Analytic tooling tends to reflect the diverse backgrounds of the analytics, and can be all over the map.  Strategic analysts use SAS, R, Stata, Statsoft, or whatever to do the work, and drop the results into Powerpoint.  One of the best strategy analysts I’ve ever worked with used nothing other than SQL and Excel.  Since strategic analysis teams tend to be small, there is little value in demanding use of a single tool set; moreover, most strategic analysts want to use the best tool for the job, and prefer to use niche tools that are optimized for a single problem.

The most important common requirement is the capability to rapidly ingest and organize data from any source and in any format.  For many organizations, this has historically meant using SAS.  (A surprisingly large number of analytic teams use SAS to ingest and organize the data, but perform the actual analysis using other tools).    Growing data volumes, however, pose a performance challenge for the conventional SAS architecture, so analytic teams increasingly look to data warehouse appliances like IBM Netezza, to Hadoop, or a combination of the two.

In the next post, I’ll cover Managerial Analytics, which includes analytics designed to monitor and optimize the performance of programs and products.