Working Up

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.

by Dwayne Phillips

Regardless of position or circumstance, some people do things while others don’t.

This appears to be one of those parts of human nature that have puzzled me for years. Given a situation, some persons will talk, think, talk, ignore, and employ just about every verb there is except for the forms of “do.”

At the same time, there are persons who will use many verbs many of which are quite useful, then these persons will “do.” The persons will write the memo, update the procedure, take out the trash, cut the grass, fetch paper, go to the store, or do just about anything that moves the world in the forward direction.

Many years ago I knew a gentleman who worked in a state government. He knew many people in many offices of the state government. On occasion, persons would come to him and describe a stalemate they had reached while attempting to access services of the state. This acquaintance would give them a phone number and a name. The name was of a person who worked in the office in question. It was not the job of that person to do what was being requested, it was simply that this person worked in that office and this person worked in that office. This person was in that office and was one of those who would do. My acquaintance was always correct. The person seeking help would contact a person who would do. Work was done; the situation was resolved.

If you want something done, find someone who will do. One day I may understand how this works. For know, I simply know it exists.

by Dwayne Phillips

Lest we forget, we have always had more data than we could process. For some reason, we are now recording it with magnetism.

I just read yet another article telling me that “data is everywhere.” Then there are the usual numbers of peta-peta-something-or-other bytes of data every second or so. Cries follow of, “how are we going to use all this data?”

Eventually, the magazine article tells me to hire the writer who just happens to sell data processing this and that.

Data has always been everywhere. Leonardo Di Vinci had more data than he could process. He just didn’t have a machine that stored data using magnetism in one way or another. Now we do. Now these machines costs little compared to a rich person’s income (like me and like mine).

The poor of the world don’t seem to have data everywhere.

Funny how that works. Rich people create technologies that show what the rich people are lacking so the rich people can spend their riches on what only they lack.

Oh well.

by Dwayne Phillips

We usually understand one another better when we speak in simple, real-life terms—also know as plain English (for us English speakers).

Please speak in plain English.

Instead of: What are the Key Performance Indicators in your organization this reporting period?

Try this: What keeps you awake at night?

Instead of: Red, Green, or Yellow, how is your project?

Try this: When you are driving home from work, are you thinking about the project or about your home?

The list could go on. Find any “management term” anywhere and translate it to plain English, please.

This is not easy. This does not always work. It does, however, work more often that the other stuff.