Working Up

by Dwayne Phillips

This bothers me—this bothers me often and deeply. We abbreviate or shorten descriptions of individual persons. We “lump them together” so as not to name individuals or describe them. Such removes accountability. This bothers me.

Here is a news story about the Congress of the United States. There are hundreds of such stories everyday. Someone is informing Congress about spending this or spending that or whatever.

How about instead of writing “Congress,” we write “persons elected to represent voters in their home districts and states.” Hmmm. That provides a different perspective, huh?

Simple response, “Well, that is too many words and everyone knows what Congress is and we sort of shortened that to one word and it has the same meaning and you know. Right?”

Well, this bothers me. I prefer writing the long version at the top of the news story and maybe using the shorter or abbreviated version in the rest of the news story. That way we are all reminded of whom we speak, how they appear, their homes, and their primary duties.

“Congress” is vague. I prefer precise, concrete, specific, and clear over vague.

And then we come to accountability. How about naming the specific elected representatives who were addressed by the group in this news story? How about we inform the reader about the origin of these individuals and something of their personal interest in the topic of discussion. Now we can hold these individuals responsible for their actions in this news story.

Well, that is all too long, too many words, too much space on the page, too expensive, too tiring, and all sorts of good excuses. Despite the goodness of the reasons, they are still excuses.

How about some clarity and accountability instead? Please.

by Dwayne Phillips

The Great Resignation? The Great what-do-we-call-it? How about the embarrassing realization that we have been grossly inefficient in much of what we have been doing the last 20 years?

We are in the midst of the Great Resignation. Record numbers of us are quitting our jobs and moving on to something better. At least we hope it is “better” in one way or another. Otherwise, why are we going to so much trouble?

Simple, “I don’t want to spend my day commuting. I can do my job from home.” Some persons are doing two “full-time jobs” from home.

Uh, wait. Let’s move away from fantasy and back to reality. Many of us have office jobs. We go to the office; we work, and we return home. Repeat daily.

And now, we have all these fancy pants folks telling us that they can do all the work from home in three or four hours instead of in eight “at the office.” And the embarrassing thing is, they are not stretching the truth. They can do it all from home in half the time and not commute.

Now comes the great embarrassment.

1. No one had to build all those expensive office buildings
2. No one had to hire all those managers to ensure people were motivated and working
3. No one had to live in an expensive urban center
4. And, the embarrassment that hits all us “workers,” no one had to spend all that time commuting

We are all embarrassed at how inefficient we all were. Why did we all waste so much time and money? Why did we all fail to realize that the tools for greater efficiency and greater productivity were right in front of us? Why did we all do these foolish things for so long?

Quick, find a scapegoat! Find someone to blame for all this. Gosh, this is embarrassing.

by Dwayne Phillips

Forty years ago I saw what Augmented Reality could do. I’m still waiting for it.

Back in 1980, I spent much of my workdays repairing electronic equipment (yes, I am that old). Pull a piece of equipment out of the rack, put it on the workbench, remove the cover, and trace through the circuit boards trying to find what failed and replace it.

One company (I cannot recall which) made this much easier on some of their equipment. They put a clear plastic board over the circuit board and held it in place with little supports. The plastic board had holes in it above test points and adjustment points (variable resisters). The plastic board also had words and arrows and other helpful things printed on it. “Test here,” “Adjust here,” and so on.

Looking down at the circuit board with this clear plastic board was augmented reality (AR) in 1980. Aha! Look at the object of your work and see helpful things hovering in the air above it. This was wonderful!

And here we are 40+ years later. Where are those glasses I can wear that show me these things. Look at the object of my work and see helpful tips floating above the work. This is just what everyone who does any sort of maintenance on just about anything needs.

Examples:

• Auto repair
• surgeons
• dentists
• air conditioner repair
• plumbers
• and the list goes on to include editors of essays

And then we can extend this to those who teach. A coach can look through AR glasses at a player who is attempting a skill. The glasses point to flaws in technique Aha! That is it. And therapists who are trying to help patients recover their skills.

What do we have? Advertisements on playing fields on TV. That’s it?

Come on folks. We can certainly do better some 40 years later. Huh?

by Dwayne Phillips

Data seems to be opposite of everything else when it comes to saving it, using it, and producing value.

When we use things, they lose value. Drive a car a thousand miles and its loses value, i.e., no one will pay as much for a car with 1,000 miles as they will for a car with 10 miles. Hit nails with a hammer for ten years, the hammer is worn and not as valuable. There are exceptions like houses that gain value after time, but there are other economic forces in play.

Then we consider data. Store data and don’t use it. That costs money as we have to buy computers and disk drives and turn them on and pay the utility bills and pay people to administer them. The data loses value when not used.

Use data. Employ it to decide on what to buy and sell and when and where and that data produces value.

Hmmm, using data multiplies its value. Not using data reduces its value. Doesn’t make sense in light of many other things, but it makes dollars and cents when used.

Not in the business of business and making money? Consider a non-profit organization that connects people. Whenever someone says “data,” substitute “people.” Employing people increases their value. Connecting people increases their value. Having people sit and do nothing decreases their value.

I often read the cliche’ “data is the new oil.” Perhaps “data is like people” is more apt as well as “data use means value.”

I’ll have to think about this a little more.

by Dwayne Phillips

We don’t want to be slow. Stop all slow processes; be quick. There are, however, slowing processes—things that cause pause for thinking. And thinking is almost always a good thing.

“This is slow. This is too slow. Let’s stop doing this,” said a frustrated person who has a good idea (or in most cases this is a good idea).

We don’t want to be slow at work. Others will “get there first” and take market share or this or that or something that we want. There is no need for slow procedures where we have to have 12 different people sign a piece of paper and with vacations and sickness and all that it takes a month to find each of those 12 people and … you know.

There are, however, slowing procedures and processes. “We won’t do this until everyone looks at it, thinks, and says to go ahead.” We can do this in an hour or half an hour; fast enough? And everyone thinks about it.

That is too slow for some people. Some people are in “too much of a hurry.” Let’s think first. Thinking is good. Right?

One of the problems is that these are all subjective terms and sentences. My opinion, your opinion, their opinion, etc.

Still, let’s pause and think. Even for five minutes, let’s think. That is a slowing process. It isn’t slow, but slowing. I think that is good.

by Dwayne Phillips

Many of today’s data scientists are similar to man of the web designers of the 1990s. I think this is a good thing.

A recent conversation with a colleague helped me to realized something about data science in today’s world. I thought data scientists were computer scientists, engineers, and mathematicians who knew programming and a field of specialty in addition to their STEM background. (I think I read that description somewhere.)

“Oh no,” said my colleague. “Most of them I work with have a liberal arts degree, a feel for numbers and logic, and enough smarts and initiative to have learned how to string together ten lines of Python to call the right packages and do something.”

What these data scientists lacked was a feel for science, repeatable experiments, rational thought, and such. They were basically parroting things they saw online.

Hmmm, that sounds familiar. In the 1990s in the days of Web 1.0 we had “web designers.” A successful web designer I knew had a bachelor’s degree in English. He had an appreciation for art and what looked good on the screen. He could read and write. He had enough smarts and initiative to learn HTML and a little about cascading style sheets and the like.

Then the dot com boom crashed in the late 1990s and he went to grad school to work on a Masters of Fine Arts.

Will we have a data science boom crash ka-bang or something and all our current stuff crumble to the abyss? I don’t know. I hope not.

I liked the idea in the 1990s of liberal arts majors working in the tech field. They brought a lot with them to the rooms full of techies. They made us and the industry better.

I like the idea of liberal arts majors being data scientists. They bring a lot with them to the ZoomerTeams meetings of full of techies. They make us and the industry better.

They also demonstrate the idea of “democratization.” I hate the term, but like the idea. We have built tools that people can use. These liberal artists are smart. They can learn these tools and use them (often better than us techies who built the tools). Sure, they take missteps along the way and have experiments that aren’t repeatable and don’t know what configuration management is (come to think of it, most STEMmers don’t know what configuration management is either, but that is the topic for another day).

Still, these tools bring more people into the room, and we are all better because of that.

by Dwayne Phillips

Managers want to hear the summary. The details are delegated. It is unfortunate that the summary rarely agrees with the details.

“Summarize it for me. Give me three choices. I delegate.”—managers everywhere all the time.

Simple statement from the managers. They have manager tasks to do. They delegate work to others. The others are supposed to summarize things and then do the work.

That would be nice. I guess it works in some fairy tales. I have rarely seen it work in the real world.

Here is a recent news report on algorithms—those mysterious things that seem to run the world even though we don’t understand what they are.

Algorithms are what the computer software does. The computer software is written by persons who understand the algorithms. Hence, you can regulate algorithms by regulating the computer programmers or at least supervising them.

The managers are supposed to supervise the computer programmers.

“What does this do? Give me the summary. I don’t have time for the details,” said the manager.

That is nonsense. The details are in the details. Look at the source code of the software to understand the algorithm. No time? Well, that is your job. Make the time.

Summaries in PowerPoint and memos do not contain details. The details are in the details. I have often seen summaries that aren’t quite true. The creators of the summaries are not trying to hide the details or lie about them. They were hired to write details into software. They weren’t hired to summarize. They simply do a poor job of summarizing.

They summarize (poorly). The managers approve (ignorantly). The results are not what was desired. Everyone gasps and says, “Gosh. There is no way to regulate algorithms.”

Come on folks. Let’s all do better.

by Dwayne Phillips

Special projects fail because they aren’t that special. We pretend or wish them to be so we can forego proven techniques and hard-learned lessons.

We know why fill-in-the-blank projects fail. Let’s fill in the blank:

• Artificial Intelligence
• Machine Learning
• big data
• data lake
• non-profit
• whatever

Of course these projects are different from the normal project. Every project is different. And given that, what is a “normal” project? I guess we could average this and that and find a norm and standard deviation and such.

These special projects fail because they aren’t that special. They are more like the normal project than they are different from it. There are known ways to project success and there are known ways to project failure.

Telling myself that, “This is a special project, not like others so I don’t have to stick with the known fundamentals and avoid the known failure modes” is wishful thinking. Sometimes wishes come true. More often, however, they don’t.

This isn’t that special. Sorry.

by Dwayne Phillips

Sometimes we “know where everything is” despite outward appearances. Sometimes we have “a place for everything and everything in its place.” Sometimes both ideas work. Sometimes neither work.

Data is everywhere. Data is the new oil or bacon or pizza or something good or bad. The trouble with data is if I cannot find the one thing I need at the time and place I need it, all those data are useless.

Therefore, everything in its place. Look in the right place, as long as I can remember which place is the right place. Sometimes I can, and sometimes…the other.

Aha, I’ll just pile everything in to one place. I have a magic finder that finds whatever it is I want. Aha, that is Google search and WordPress search and Apple finder and Windows finder and … I guess there are many more. And they all work well! They allow me to simply toss new things on the pile. They “index” everything (what a bad noun-to-verb thing) and find it.

Sometimes this works. Sometimes that works. Sometimes nothing works. What am I to do?

Maybe I’ll just remember what it is I thought to be important. Then again, maybe I’ll forget it and move along happily anyways.

by Dwayne Phillips

Almost anyone can gather the material. Organizing it or creating a story from it, however, appears to be a rare yet valuable skill.

We have lots of information. Go to Wikipedia. Download PDFs of the pages. Concatenate the pages. There it is.

Go to a search engine. Find a dozen hits. Copy and paste. There it is.

Simple. Right? Everyone will read it. Right?

Wrong.

Someone needs to arrange or organize the material into a sequence that leads the reader somewhere. Some call that a “story.” Some call that a “flow.” Whatever we call it, we know it when we see it because it moves us.

Some persons do this well. Most others don’t. Natural-born talent? Maybe for 1% of us. The rest of us have to try hard(er). Let’s get to it.