Category Archives: Technology

Going Digital. Digitization. Digital Transformation.

Do you know what any of these terms mean? Are you sure? and I’ve been “digital” for three and a half decades — and I’m not sure I’m whatever “digital” is when it’s spoken by someone who hasn’t been digital since before it was cool. I had a TRS-80 which, supposedly, understood the BASIC programming language (it did, but even if you think you know BASIC, unless you had the joy of Level I Basic, you don’t … especially if you haven’t experienced the joy of only three error messages … which, I will admit, was better than the one error message I got on the VAX) and that was followed by an 8088 … remember that? Probably not … it was long before it was all about the pentiums … but, like the TRS-80, it was digital. (And if you don’t understand any of this, all you need to know is I’m the One That’s Cool.)

The thing is if you’re using a computer, you’ve already went digital. It works on bits, not analog signals. So what does it mean to go digital? Since there isn’t a business these days that doesn’t use computers, there isn’t a business that’s not digital. The only question is how much is done on the computer. And, more importantly, how much is done entirely on the computer. This is, simply put, a meaningless bullshit phrase.

Now let’s talk about digitization. Technically, this is just the process of converting an analog signal to a digital one (by selecting a sampling frequency, such as every second, tenth of a second, hundredth of a second, etc.). Or, more generally, converting something in the analog world to the digital world. In the back office, this usually means converting paper to documents. But this can just be the process of scanning paper documents to image files, which can not be searched, automatically indexed on key meta-data, etc. That requires (advanced) OCR, and machine learning to take corrections and improve the OCR so that future digitization of faxed documents (sent as images) or PDF documents can be automatically converted into searchable, indexed, text formats with accuracy. So while this isn’t as much of a bullshit term as going digital is, it’s a pretty ambiguous one. A software provider doesn’t have to do very much to honestly say they support digitization given the multitude of (rather weak) definitions that exist.

So this takes us to digital transformation. This is supposed to imply that you dramatically improve all of your business process through the implementation of a new platform that transforms the way you do business to a process that is faster, better, cheaper … and delivers more value. But if you think about this, pretty much any platform you implement is going to transform the way you do business … but is going to be for the better?

So before you fall into the “digital” craze, think about what it really means!

Just like infinite scroll websites aren’t new (that’s what we sorted with before we could frame and tab and paginate, for those that don’t remember), neither is digitization!

Why are We Still Hyping Big Data When We Haven’t Mastered Small Data?

The end of the year is coming, everyone is looking for next year’s tech, and everyone wants cognitive / AI that works on Big Data. But there are two real problems with this:

1. With current hard drive and memory capacities in a single machine, we typically don’t have enough data to fill it (at least with efficient encodings) in our typical back-office functions or unbearable computation times on a quad-core (with efficient algorithm implementations).

2. When it comes to learning, the biggest sets we have for training are typically quite small!

We’ll start with the second point first. Consider spend analytics, where the primary task is to map transactions to a categorization hierarchy. If you want to use a “deep learning” AI, then you need a big data set to train that AI. But how many transactions will the average organization have that have been mapped and verified individually by a human? Maybe 10K or 20K. Even a spend analysis provider will typically have only verified a few hundred thousand or maybe a couple of million compared to the tens or hundreds of millions of transactions its big customers will throw at it a year.

The situation is even worse for contract analytics. A large multi-national might have 20K contracts, but how many have been properly indexed with meta data at the clause and term level? If you have 2K you’ve struck gold. A contracts analytics provider might struggle to cobble together a data set of 20K contracts. This is even smaller data.

And then moving on to the first point. Even though most first (and even second) spend analytics applications are slow, (Opera) BIQ has been able to process and re-categorize a million transactions on a dual-core or better laptop with 8GB of memory in under a minute for almost ten years, and their most recent version on a modern quad-core laptop with 16GB of memory can handle a million transactions in a little over a second! In fact, it can handle ten million transactions in less time than it takes you to enjoy two sips of your coffee. And when you consider that most analytics are only on a set of related categories for a relatively short time period (at most 3 years), the number of transactions is typically only a few hundred thousands for a large company and in the tens of thousands for a mid-size company. That’s not only small data, but data that can, these days, even be processed in your browser (as a new analytics offering will prove next year).

So before you go goo-goo-ga-ga over big data, understand how big your data really is and get the application that works best for your data, which will more likely be at a cost point that works best for Finance as well.

How Much Technical Debt Do Your Vendors Owe You?

And when does the debt become too high to be repaid?

But first, what is technical debt? It’s the debt owed to you by vendors that continually collect moderate to high maintenance fees or annual subscription fees but yet do nothing more than the odd bug fix. These vendors owe you a solution that is continually enhanced year over year. Especially if you are a SaaS client paying big money to keep the solution alive.

And in many of the bigger vendors, it’s growing massively, as chronicled by the deal architect in his post on the other technical debt.

And, as he notes, it’s really easy to spot. When a customer:

  • makes significant customizations,
  • has a stable of “ring-fence” applications, and, most importantly
  • continues to use (large) spreadsheets that were supposed to be replaced by analytical tools

and the customer is still paying a significant SaaS license fee or maintenance fee years after product/platform acquisition, the vendor owes them a huge technical debt.

And as the deal architect pointed out, this debt will continue to grow if they dance to the investors’ tune and only spend 10% to 12% of revenues on R&D annually. Start-ups pay multiples of that, and that’s why they build great new technology. If a company isn’t spending about 1/3 of its revenues on R&D, it’s likely it will never deliver the value you need and its technical debt will only grow.

But don’t wait until its debt is too great to ever be repaid, that does you know good. Once it’s clear a vendor is not going to continually deliver the ROI you need, move on. Once a cost is sunk, throwing more coin on the pile only sinks it deeper.

Source-to-Pay UIX 2017 (Collected Links)

What Makes a Great U(I)X?

What Makes a Great e-Sourcing U(I)X?

What Makes a Great (Strategic Sourcing Decision) Optimization U(I)X?

What Makes a Great Spend Analysis U(I)X?

UX Epilogue

3-D Printing Will Bring Changes to Direct Sourcing

But not overnight, at least not for the changes being touted as the future of direct sourcing.

Print a part on demand? Not likely. Not soon.

Print a sample part on demand for evaluation — you could have that tomorrow.

What’s the difference?

First of all, today’s 3-D printers can only work with very specific plastics. Generally speaking, these plastics will not be suitable for the vast majority of parts the organization needs.

Secondly, most 3-D printers cannot mass produce parts fast enough to be useful to an organization that needs the parts in quantity.

Thirdly, the economics of 3-D printing today are not nearly where they need to be for mass production compared to current production techniques.

It will be a while before each of these criteria are met, and until they are, 3-D printing won’t be the future of direct sourcing.

But they do have their uses. Let’s say you are collaborating with a supplier halfway around the world in the design and development of a new part. If it requires regular review of a physical part, and getting that part on a regular basis requires global expedited shipments that cost hundreds of dollars a shipment and take up to a week to arrive, then the organization will be spending thousands of dollars on shipments and losing weeks, if not months, of production time while it waits for a part to arrive.

But with 3-D printing, an almost exact replica of the part, down to at least 2mm, even if it’s a metal part, can be printed locally from the CAD/CAM design files. And this can be done for a few dollars in a few hours. This is a significant contribution to the NPD process. And a considerable change to direct sourcing as life-cycles, and costs, can be considerably compressed and quality improved before the first part is delivered.

This simple change alone is significant, and we don’t need to wait for the future to get results. As long as we go in with an understanding of what those results will be.