Historically, the essence of enterprise applications has been data management — they capture business information, then show it to you. User interfaces are typically straightforward in the UI technology of the era — forms, reports, menus, and the like. The hard part of building enterprise applications is getting the data structures right. That was all true in the 1970s; it’s all still true today.

Indeed, for many years, the essence of an application software acquisition was the database design. Maintenance streams were often unimportant; code would get thrown out and rewritten. But the application’s specific database structure would be adapted into an extension to the acquirer’s own.

Examples that come to mind from the pre-relational era include:

• Bill of materials planning. This was before even my time, but it seems to have been a big part of what kicked off the whole DBMS industry, even though manufacturing applications then spent a decade not being DBMS-based.
• Order entry/accounts receivable. This was a tough problem from the mid 1970s though the mid 1980s or so. In particular, accounts receivable stumped John Landry at three consecutive companies — McCormack & Dodge, Distribution Management Systems, and Cullinet — before he claimed to finally have figured it out.
• Multi-currency support, about which I had an exchange that may be paraphrased as:
  • Pat Tinley of Ross Systems: “I’ve finally figured out how to do multi-currency right.”
  • Me: “Didn’t you tell me that at MSA?”
  • Pat: “I was wrong then.”
• Process manufacturing, and the co-products/byproducts it entailed. This led to the one significant patent suit outcome in enterprise software history, in which Marcam really did chase Ross Systems’ product off the market.

A shining relational-era example is SAP’s inclusion of workflow as a central aspect of 1990s application design.

The resulting apps, however, are cumbersome — very cumbersome. They’re cumbersome to use. They’re cumbersome to install. They’re cumbersome to change. People who use enterprise applications feel trapped in a bureaucratic hell. That is why I agree with the sentiment that operational enterprise applications are the verge of significant change.

• Historical notes on analytics — the pre-computer era
• Historical notes on analytic terminology (in which many terms used in this post are defined)
• Historical notes on analytics — departmental adoption (this post)
  

What set off my “history of analytics” posting kick is, simply put:

• Most interesting analytic software has been adopted first and foremost at the departmental level.
• People seem to be forgetting that fact.

In particular, I would argue that the following analytic technologies started and prospered largely through departmental adoption:

• Fourth-generation languages (the analytically-focused ones, which in fact started out being consumed on a remote/time-sharing basis)
• Electronic spreadsheets
• 1990s-era business intelligence
• Dashboards
• Fancy-visualization business intelligence
• Planning/budgeting
• Predictive analytics
• Text analytics
• Rules engines

If we leave out data management/system technologies* — e.g. data warehouse appliances or Hadoop — that’s pretty much everything that succeeded (and a couple that perhaps didn’t). I don’t know what to put on an “Enterprise-wide from the get-go” list except for a couple of duds like executive information systems and balanced scorecards.

*”System software” technologies such as DBMS often do eventually fall under the purview of central IT. But even for them there’s typically a multi-year period during which departments take the initiative in bringing them in.

Of course, this should surprise nobody; information technology is almost always adopted departmentally first, with the exceptions arising mainly in cases where departmental adoption makes no sense. Reasons include:

• The problem being solved is department-specific.
• The expertise and specific techniques to solve the problem are (or seem) subject/department-specific.
• The budget to solve the problem is department-specific.
• The best reasons to centralize technology often involve integration among departments, and new technology is rarely expected to start out being all that integrated.

Departments most likely to be early adopters (relative to others) of analytic technology seem to be:

• Finance/planning, especially in the old days when analytic technology was newer (nowadays finance might be more involved in trying to push reporting/analysis discipline out to a whole company).
• Sales/marketing, because they often have more data than other departments (actual purchase transactions, other customer contacts, and also a lot of external data).
• Investment research, because financial analysis is almost literally their core product. (Ditto trading, for very similar reasons.)

Three examples, for me, serve to bring all this home.

Business PC use famously started with individuals and departments just acquiring PCs, outside of the IT department’s control or even knowledge, way back in the day of the Apple II. Most commonly, the reason to get the PC was to run an electronic spreadsheet, generally VisiCalc.

10-15 years ago, when business intelligence vendors banged the drum for enterprise-wide BI/dashboard adoption, I’d ask them “So, do you have an enterprise-wide dashboard yourselves?” Invariably, they didn’t — but they did have departmental dashboards for sales and/or marketing. It became clear that this was a general pattern in BI adoption.

Multiple generations of technologies that one might think of as having to do with artificial intelligence – e.g. expert systems, predictive analytics* and text analytics — have wound up with applications being concentrated in the same few areas:

• Marketing
• Quality/maintenance
• Scientific/engineering research
• National security/law enforcement/anti-fraud
• Underwriting/investing/risk assessment
• Publishing (for the text-oriented technologies)

Those categories comprise 90%+ of the applications I can think of for the golly-gee-whiz technologies of their day. (You could add simulation to the list as well.) And outside of the publishing and criminal-catching sectors, those apps are pretty departmental in nature.

*I think predictive analytics has evolved into a blend of statistics and (other) machine learning, and machine learning can be viewed as a kind of AI.

So why do I think you should care about all this? Two reasons:

• History is cool.
• It has relevance to current issues in analytic technology adoption, which I plan to write more about soon.

I hope you agree.

• Historical notes on analytics — the pre-computer era
• Historical notes on analytic terminology (this post)
  
• Historical notes on analytics — departmental adoption

Discussions of the history of analytic technology are complicated by the broad variety of product category names that have been used over the decades. So let me collect here in one place some notes on how (and when) various terms have been used, specifically:

• Management information systems
• Decision support (systems)
• Report writer
• Fourth-generation language
• Executive information system
• Business intelligence
• OLAP (OnLine Analytic Processing)

Obviously, I can’t cover everything in this post. Omissions include but are not limited to:

• Anything in the data warehouse/data mart area. (For one thing, I don’t want to deal with the whole Inmon/Kimball dispute.)
• Anything in the predictive analytics area (but see the first point in a 2009 SPSS post).
• Terms I’ve recently sponsored, such as investigative analytics or machine-generated data.
• Big data (analytics) — I just discussed that mess a week ago.

The first prevalent term I recall for “information technology” was management information systems (MIS). I mention that mainly to note that it actually sounded a bit analytics-oriented, and hence to point out that in the old days — 1960s and so on — it didn’t seem necessary to name a separate category that amounted to “analytics”.

Meanwhile, the first prevalent term I recall that covered much of what we’d now call “analytics” was decision support, or decision support systems (DSS). I think DSS was always ill-defined, with multiple subcategories, just as analytics is today. The heyday of this term was in the 1970s/1980s.

Report writers were around in various forms for decades; consider for example the early 1970s history of Cullinane/Cullinet. By the time I became an analyst in the early 1980s, these were mainframe tools that let you specify paper reports, and the market leader were probably Pansophic’s EASYTRIEVE and Informatics’ Mark IV. According to marketing, they could be used by non-programmers; in reality, they were a much easier way for programmers to do what end users asked. They were used both for one-shot queries and, as their main design point, repetitive reporting.

The report writer category then survived into the era of business intelligence (BI). (Indeed, Cognos’ big integrated BI tool early this century was called ReportNet.) More on that in the BI discussion below.

The term fourth-generation language (4GL) was widely used from the 1970s through the first part of the 1990s. Usually, a 4GL was:

• A vendor-specific programming language …
• … sold in connection with an interpreter …
• … that was particularly good at database manipulation.

In particular, the original 4GLs:

• Were primarily sold and used for analytics.
• Were often sold/used on a remote/timeshared basis.
• Often had some kind of (pre-relational) DBMS bundled in.

Classic examples of such 4GLs included FOCUS (the core product of Information Builders), RAMIS, and NOMAD; SAS arguably started out as a product of that kind too. Starting in the 1980s, however, 4GLs were used more generally, and indeed survived as an OLTP (OnLine Transaction Processing) technology long after they were supplanted by BI tools for most analytic purposes.

The last pre-BI term I want to mention is executive information system (EIS). EIS was in essence the 1980s term for “dashboard”, although the technology was much more primitive than it is today.

The term business intelligence was coined in the 1950s and then reinvented in the 1980s; however, it has described a major category only from the 1990s onward, specifically starting when GUIs (Graphical User Interfaces) became prevalent.. “Business intelligence” is sometimes used to comprise all of analytics; more commonly, however, it refers to tools focused on data selection and presentation.

These days, most of what we’d call BI comes in a single integrated package, focused on a dashboard; most of the exceptions are somewhat old-fashioned report writers. In the 1990s and early 2000s, however, business intelligence had several distinct subcategories …

… one of which was called OLAP (OnLine Analytic Processing). Actually,  the term “OLAP” has been confusingly been used to mean several different things, including:

• Pretty much all of analytics.
• A particular non-relational DBMS architecture that I prefer to refer to as MOLAP (Multidimensional OLAP).
• An integrated suite of DBMS, 4GL, and perhaps other tools around a MOLAP architecture. (Examples: The IRI Express and Arbor/Hyperion Essbase products Oracle bought.)
• A client-side BI tool with a little MOLAP DBMS built in. (Example: Cognos’ erstwhile flagship BI product PowerPlay.)

I hate the term “OLAP” with a passion, in part due to that confusion, and in part due to the specific way the confusion came about: Ted Codd introduced the term, allegedly objectively, but actually as a marketing shill for Arbor Software, which had an obvious business incentive to pretend that its specific technologies solved a broader class of problems than they actually did.

OK. With that too cleared away, I feel ready to write about the actual history of analytic technology.

• Historical notes on analytics — the pre-computer era (this post)
  
• Historical notes on analytic terminology
• Historical notes on analytics — departmental adoption

Sometimes, what people describe as being “New, new, new!!!” in analytics has actually been happening since before they were born, or even before their parents were. Occasionally, I point this out. I think it’s time to collect some of those observations into a short series of posts.

Before getting to the history of actual analytic software, I can’t resist racing through some really old stuff. In a 2004 white paper, I wrote:

Transactional business processes have been around literally since the beginning of recorded history. Some of the oldest known writings are clay tablets that record merchants’ tallies in Sumerian cuneiform, complete with seals to enforce transaction integrity. Analytic business processes date back nearly as long, especially in military applications; the first chapter of Sun Tzu’s The Art of War is called “Calculations,” or in some translations “Laying Plans.”*

As enterprise complexity increased, so did the sophistication of analytic business processes. Almost two centuries ago, Nathan Rothschild made an investment fortune from early news about the Battle of Waterloo, and several decades later Florence Nightingale** introduced statistics to the study of public health. With the invention of machines to tabulate information in the late 19th Century, analysis began to blossom.

*Back when I wrote that, I also considered including some of the accounting Caesar cited in his Commentaries on the Gallic Wars, but eventually decided that was a “production application” rather than anything we’d recognize as “analytics”.

**Florence Nightingale is, simply put, one of the more impressive women in history. Unfortunately, a couple of other statistical greats were associated with the deplorable subject of eugenics. I am thinking specifically of Francis Galton, who invented several really basic statistical concepts, and seems to generally have been a brilliant guy, and Karl Pearson. (Hat-tip to John Verostek for, um, tipping me off to Galton.)

Statistics also seems to have led the way in business applications of analytics. Specifically, statistical quality control dates back to the 1920s or so, pioneered by Walter Shewart and given greater visibility by his protégé W. Edwards Deming. On the monitoring side, various organizations collected industry-wide numbers by the 1930s or so. For example, movie box office receipts were reported at least as far back as 1939, perhaps by Variety; the New York Times bestseller list seems to have started slightly later, in 1942; and a rather superficial site on media history first gives hard numbers for the decade of the 1930s.

Still, to continue quoting the same white paper:

What utterly transformed both transactional and analytic business processes was the advent of electronic computing in the 20th Century. In particular, the volume of available data exploded. Even more important to analytic processes was the superhuman increase in the speed of computation. Various types of software emerged — business intelligence (BI) tools, spreadsheets, statistical packages, and the like – permitting kinds of analysis that had been infeasible without computers.

And with that out of the way, let’s return to discussing computer-era analytics.

• Oracle started out doing what amounted to custom development for government (military/intelligence) clients.
• Even when Oracle said it had productized its software, the stuff didn’t work very well without services to get it running.
• Oracle and Ingres both got a huge fraction of their early revenue* from deals to port their software to various brands of hardware.** That’s a lot like professional services.
• Oracle’s huge Tools Group grew out of professional services, if I have the story straight. Indeed, its first product was written by later long-time group chief Sohaib Abbasi when he was a consultant.

*Revenue recognition rules were rather different back then. Multi-million payments or guarantees for ports could be recognized as lump-sum revenue up front.

**Ingres once ran on more hardware platforms than it had employees, when both numbers were somewhere in the 40s. Most of the boxes on which the porting was tested were in one small office.

The benefits for a young software company of being in the professional services business include:

• It can be high-margin, especially if it shares the cost of sales with your software offering.
• It allows you to say “yes” to whatever the customer asks for. (More precisely, it takes you closer to that goal that you’d be without a service offering.)
• It allows you to fund capable staff. Or to put it even more bluntly …
• … professional services brings in revenue that pays your bills.
• It gets you involved with customers, learning stuff about their needs, and specifically addressing them.

The disadvantages of professional services generally boil down to various forms of defocus; you can screw up your development schedule, your development priorities, your sales priorities, your partnering efforts, your market positioning, your burn rate or just about anything else.

Many software companies pursue substantial professional services when they’re young. Many don’t. Both strategic choices can make sense.

This was all slightly before my time — I only entered the industry and met Software AG in 1981. So does anybody else out there recall more of the story than I do?

• The brochure quote basically pitches business intelligence/ performance management, with unconstrained drilldown.
• Pitching BI/analytics benefits for what start out as being transactional applications has been going on for pretty much the whole history of the applications industry. This just happens to be a great proof point.
• The unconstrained drilldown part could almost be taken for granted today, in the relational era. In 1972, however, it was a rather bold (and for all I know exaggerated) claim.

The exact verbiage is: 

1.  The system should focus management attention on the critical variables in distribution profitability, providing measures of the productivity of all key resources of the firm.

2.  Where deviations from expected performance levels are detected, they should be highlighted on an exception basis, so that managers can allocate their most critical resource — time — to the problems or opportunities that variances in performance represent.

3. The manager should be able to pursue the analysis of an exception to whatever level of detailed information is needed to take positive action; and the system should, therefore, permit the manager to secure information at any desired level of detail without prior knowledge of his information needs.

Here’s the part I know from my own memories as

• The son of a Management Horizons employee (namely my Dad).
• A software industry stock analyst (in particular, one who followed Informatics General).

My father moved to the Columbus area in 1973 to join Management Horizons, a consulting firm serving retailers. Management Horizons had its own spin-out already, a time-sharing company called Management Horizons Data Services (MHDS), with which it still shared a building on what is now Old Henderson Road in Upper Arlington. And, this being a world full of coincidences, MHDS is very on-topic for the primary focus of this blog (software industry history).

MHDS’ main business was a full suite of what we might now call ERP for distributors and/or retailers. That never amounted to much. But its secondary business was an electronic interchange for direct placement of orders, called Ordernet. Ordernet turned into Sterling Commerce, a > $1/2 billion company that has been acquired for >$1 billion more than once.

The chain of events, roughly, is: 

• Management Horizons sold MHDS to what would become Citibank.
• Citibank flipped MHDS to early software industry conglomerate Informatics General.
• Sterling Software grew itself 10X in size by a hostile takeover of Informatics. (Sam Wyly was involved in that.)
• Sterling split into Sterling Commerce and what I might call The Rest of Sterling.

At that point I forget the details, but a couple of multi-billion dollar acquisitions/divestitures have ensued.

Some day I may dig out the numbers for Informatics’ revenue breakdown in the early 1980s. Ordernet wasn’t big, but looked like a rising star. MHDS  classic wasn’t big either, and didn’t look like it was going anywhere. Both appearances were later born out.

Online research uncovered some other sources, namely:

• A two-part biography of Alton Doody’s Management Horizons co-founder William Davidson, whose main relevance to this post is that it pointed me to …
• … a 1996 history of Management Horizons written by Davidson.
• An oral history from Informatics co-founder Werner Frank.

On pages 18-19, the Werner Frank link notes that Informatics acquired MHDS in what sounds like 1974, from then-owner Citibank. MHDS was doing $7.8 million in revenue (which Frank failed to break out among its two business segments), was acquired for $3.4 million, and apparently brought a $3.3 million long-term contract along with it, as well as a cheap loan.

Unfortunately, Davidson’s history* is inaccurate in some details I can check from memory. E.g. it says the Doody Company was created in 1974, which is too early, and it omits Informatics General from the history of MH’s information processing division. It also includes Aetna in the list of outfits that owned MHDS, which is news to me and also, apparently, to Werner Frank.

*As I write this, another copy is available for sale on Amazon. Just Google on “History of Management Horizons.” Yes, we paid around $20 for my copy too, even though there’s a $0.99 price tag on it clearly crossed out.

Anyhow, Davidson wrote:

• One of Management Horizons’ founding projects was helping retrain the NCR sales force away from dumb cash registers and toward what we’d now call point-of-sale devices, a key step in the IT revolution of actually using POS information.
• MHDS started out with two leased IBM System/370-155 computers.
• MHDS was divested to Citicorp’s predecessor bank in May of 1973. “At the time of divestiture some 92 wholesale distribution sites were under contract to MHDS with about 30 or so on-line to it.”

We now have details for their joint Celebration of Life, a better term than “Memorial Service,” or at least one less fraught with religious overtones. It will be Sunday, November 14, 4 pm, at Friendship Village of Dublin (address and directions below).

To quote a previous post:

Please make in-lieu-of-flowers donations to the Clinton Foundation, which is doing terrific work in Haiti relief, microfinance, tropical disease, HIV/AIDS, and much, much more.

Unfortunately the Clinton Foundation has no obvious “In Memory Of ____ ” option, so please feel free to make mention of a gift in the comments below, should you choose.

At this time I do not plan to blog at any length about my parents’ retirement years or final declines. More precisely, I do not plan to cover those subjects at length unless I am prepared to weave them into “lessons learned” kinds of posts. But to cover those in very abbreviated form:

• My parents, while still fairly strong and active, moved into the Friendship Village of Dublin community linked in the directions below.
• Peter Monash’s dementia was mild until the end. He had difficulties remembering certain words or names, and generally was slower mentally than he was in his prime, but in essence his personality and lively mind were unaltered.
• Much the same was true of Anita Monash until January of this year, give or take a month. And even until (almost) the end, she recognized everybody, could respond to fairly sophisticated concepts, and so on.
• They both had issues with the classic old-age banes of falls, pain, weakness, hearing loss, pneumonia, and skin infections. Neither seemed to have a problem with strokes. Peter Monash also lost the sight in one eye, which interfered with reading. Much of Peter Monash’s weakness was kidney-related. Anita Monash had longstanding fibromyalgia.

Location details for Peter and Anita Monash’s Celebration of Life are:

• Friendship Village of Dublin, phone number and overview map at that link.
• 6000 Riverside Drive, Dublin, OH. The entrance is on a cross street at the south end of the facility. Google Maps isn’t looking terribly reliable.
• You want the main building entrance, on the East/Riverside Drive/Scioto River side of the facility.
• The actual event will be in the “Convocation Room,” near the main lobby.

And finally, I’m the executor of the wills of both Peter and Anita Monash — dated 2004 — and we have a glitch. There’s a bequest of some nice craft items to Robert Zwink, perhaps now or previously a resident of the Columbus, OH area, and I have no idea who Robert Zwink is. Mr. Zwink — if you discover this post, please contact me via the Contact link above. If you’re the wrong Robert Zwink, but have an idea of a namesake who might be the correct one, please help me out by putting us in touch.

The series so far

• Introduction to the lives and marriage of Peter and Anita Monash. The first post in the series, it also has details such as time of death.
• Religion and the Holocaust in the lives of Peter Monash, Anita Monash, and my grandparents.
• Peter Monash’s life before he immigrated to the United States.
• Peter Monash’s life his first quarter-century in the United States.
• The peak of Peter Monash’s working life, and the overall business movement in which he played a key part.
• Anita Monash’s life until she got married.
• Anita Monash’s life from marriage through retirement.
• An overview post, framing the whole series.

I wrote previously that Anita Monash had gotten married in the glow of a period of relatively good health. It didn’t last. Within two years she’d moved to Lubbock, TX and then Chicago, IL; had a son (me); and grown deadly ill in the aftermath of the pregnancy. The latter resulted in a family move to return the Los Angeles area, where her physician father lived.  A brief move to Northern California for my father’s job didn’t work out well either; she wound up in the Stanford Medical Center, and I was farmed out at age 4 to live with one of my Dad’s assistant store managers and his family. Soon everybody was back in the Los Angeles area — I even went to Beverly Hills High School and UCLA just as she had — until my father’s career took the family to the Columbus, OH area.

As is common for people who have such severe ulcerative colitis, my mother eventually had cancer of colon, a few months after I left her nest. One of my father’s clients — the Feldberg family behind Zayre — had donated large amounts of money to Beth Israel Hospital in Boston, which happened to have the top gastroenterological surgical team in the country, so that’s where she had her colon removed Christmas Eve, 1976.

Actually, this was not her first run-in with something resembling cancer. As a little girl, she’d been taken to Berlin to have a tumor removed that was pressing on her brain. The whole thing was terrifying — she equated the abandonment of being sent off down the hospital hallway to me being shipped out of my home for weeks a generation later. About the only redeeming feature to the whole experience was a fiercely protective poodle, egotistically named Anton by the family friend with whom she was staying, the actor Anton Walbrook.

Even when she felt better — as she largely did after her ileostomy — health was central. Her most energetic charitable activities had nothing to do with fundraising (which she always hated, equating it to “begging”), but rather volunteering through the Ostomy Society to help one new ostomy patient after another adjust to their new lifestyle. (The Ostomy Society seems to be an overall outstanding give-it-forward kind of organization.) In what was actually a much bigger part of her life, she also had a decade-long stint of active elder care. And if that wasn’t enough, she found a few other stray rehabilitation patients to sponsor and help along the way.

Things changed in the early 1980s, when her last parent died (my grandmother) right around the time my father started working essentially on his own, from home. My mother served him in a manner much more common in their generation than subsequent ones; she was the classic super-secretary, who in particular wrote a whole lot of his correspondence and even reports pretty much from scratch. (Actually, that wasn’t a new role for her. Next to my sofa today is a side table she originally claimed from Alton Doody’s office as her fee for helping my father with some Management Horizons work. And of course it’s essentially what she did in Hollywood as well.)

This grew into a fairly significant role in the Peter E. Monash & Associates consulting practice — interviewing clients, giving advice, appearing on TV, and generally living up to the title she concocted for herself of “Retail Psychologist”. Still, my father loved the work more, had decades more experience and reputation, had better health and stamina, was more outgoing, and didn’t run afoul of sexism. And so he was more serious about the work than she was, and more reluctant to retire when the time to do so came.

And that’s where I’ll leave it for now, as I don’t plan to write about my parents’ retirement years or final declines immediately at this time.