I’ve been involved in a large number of pioneering software efforts, and I’ve lived through many tech fashion seasons. It’s very clear that the vast majority of experienced software professionals and managers would rather follow current technology fashion than actually making things better. Because of the pathetic state of software knowledge and analytics, not only do they get away with throwing away truly massive gains for their organization, no one in power has a clue what they’ve done! This happens over and over and over and over again. A few people may know there’s a vastly better approach available – one that’s proven and risk-free – but no one hears them. The siren song of the hot new thing wins most every time.
I have been involved in many such farces in one way or another. Here’s the story of one of them from many years ago. See this and this for general background of the technologies and patterns. It's important to remember: I'm not saying that Sallie Mae was a disaster when everyone else was fine. Sallie Mae was a normal, mainstream project at the time, and therefore an excellent example of the overall insane pattern.
The Workflow project at Sallie Mae
In the early 1990’s, document imaging and workflow technology were hot; they were something people talked about the way they talk about AI/ML and innovation today.
Sallie Mae, then as now, was the government-backed student loan organization. In the early 1990’s they decided to upgrade their computer-based processes to something that was modern in order to make things more efficient. At the time they had over 10 million paper documents per year arriving at their processing centers. They had six of them at the time, each employing thousands of people. Even a 10% productivity improvement would have been big at that scale! Sallie Mae managers had read about the new document imaging and workflow technology that was rapidly growing at the time, and set up a task force of professionals to study it, create an RFP and get it implemented.
The task force knew they weren’t experts at this technology, so they sought out people to help them. Among other things, they attended the AIIM industry association’s annual convention, at which I happened to be a featured speaker due, among things, to my book they had just published. Here is the forward to the book, written by AIIM:
I had also led the technology at one of the industry’s tech vendors, had personally written their workflow software, been involved with major buyers including Amex, etc. So they approached me and asked me to help them out. I agreed. They also contracted with a small consulting firm to join in.
I dove in to analyze the situation. Sallie Mae had loads of IT people who were concerned about the new technology. The existing technology was basically IBM mainframes with many thousands of 3270 terminals on people’s desks. Implementing the new technology would involve buying expensive workstations with large image displays for each worker. The IT people worried about the LAN that would be needed to connect the workers to the image storage system, so I created a spreadsheet to enable them to calculate the transmission speeds that would be needed. While the other consultants were busily drawing up diagrams of paper movement so that workflow could be implemented, I dove into the details of what actually happened at a couple representative workstations.
I observed some people at one of the processing centers doing the work of processing the form. I timed the work and carefully observed the screens. I read the manual that was used to train the workers for the job. I arrived at a pretty radical (for the time) hypothesis: the vast majority of the work was logic-enhanced data entry. If the humans did no thinking but just entered the data, they could work 10X faster. The logic spelled out in the training manual could all be programmed on the data as entered, with a tiny number of exceptions kicked out for human handling.
If this could really be achieved, it had consequences for Sallie Mae. Instead of a massive per-employee technology investment with the work remaining roughly unchanged, a small fraction of the employees could do the same work in a completely new way. I figured I better make sure I was right before I started rattling cages and maybe embarrassing myself.
Logic-enhanced heads-down data entry at Sallie Mae
I knew that everyone assumed that implementing workflow was the goal, with productivity improvements assumed to be 30% after massive investment and disruption. This was broadly accepted, and no one involved was interested in challenging it or testing it. If there was any chance of them taking a new approach, I knew I better have the numbers.
The main things I discovered were:
- Most people entered 1,000 to 1,500 KPH (keystrokes per hour).
- Roughly 20% of those keystrokes were overhead, i.e., navigating to the next field on the mainframe screen, often navigating to a whole new screen. The mainframe screens followed how the data was organized inside the computer, not at all how it appeared on the paper forms.
- The workers weren't bad typists -- they were obviously doing some thinking between fields, essentially applying the special rules they were taught in training to guide them what to enter where.
I refreshed my knowledge of practical image-enabled heads-down data entry (HDDE), See this for an explanation and details. I laid out some plans.
- Image-enabled HDDE could reasonably achieve 12,000 KPH, as it was doing at multiple high-volume operations. Moreover, the navigation overhead could be brought down to a couple percent.
- The training manual was amazingly close to a natural language description of a program. If this field on the form has that, then enter X in Y mainframe field, and so on.
- The current method interwove the entry of the form with the rules. In the new system, all the data would be entered exactly as it was on the paper form, and then a new set of software would apply the rules and enter the data into the mainframe system. When the rules changed, instead of everyone taking a training class, the software would just need to be changed.
- Some forms in the existing method were sent to a customer service person for exception handling. Much of that would still happen, but the numbers were low.
- I knew that Image character recognition could handle some fraction of the data entry work. But it was a new technology, and I figured a 10X productivity improvement and avoiding a massive technology investment for workflow was more than enough to make the case.
I wrote up my results. Since “re-engineering” was a popular concept at the time, and since “zero-based budgeting” was talked about a fair amount, I wrote a paper and called my approach “zero-based re-engineering” to explain the approach and link it to fashionable things.
My involvement with Sallie Mae ended, quickly and quietly. No one objected to my proposal. No one questioned my analysis or numbers. There were some vague comments thanking me for my creative, detailed work. But I was invited to no more meetings, got no more assignments, and that was that.
What happened was that the workflow-at-Sallie-Mae train had long since left the station. My work was nothing but a distraction, with the potential of becoming an embarrassment if things went badly. I had demonstrated that I wasn’t a “team player,” and in most organizations, there’s little worse than that.
Conclusion
Like most history, the workflow project at Sallie Mae in the 1990’s doesn’t matter at all. It’s history! Its value is that it’s a typical example of a recurring pattern in software implementation and evolution. Here is a more recent example. Sallie Mae was not unusual at the time -- they were absolutely typical! Yes, they had an "insider" recommend a simple, radically better way of doing things and rejected it -- but that was the overwhelming mainstream pattern. By recognizing the pattern for what it is, the few who care can learn from it, recognize the pattern when it’s taking place, and maybe even do things better.
Most organizations will almost always follow the tech fashion rather than achieve dramatic gains using proven but unfashionable technologies. Many entrepreneurs are part of the problem, since they need to sell into buyers who value fashion over real, objective, provable value. The best entrepreneurs find a way to fit in with buyers’ hunger for fashion, while also delivering real value. Sometimes they cloak the value in super-attractive, flimsy outerwear. That's OK. They’re entrepreneurs – they find a way!
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