Lately, I’ve been trying to get a bead on culture and the effect culture has on Project/Technology ROI and Implementation Cost. The research I have uncovered suggests something very striking. What I mean is that most, if not all, of the Culture Assessment tools I examined have a specific view point. That viewpoint is simple: That if you interview people with a certain list of questions, you can come to find the culture of your organization. The results are graphs, charts, and definitions. At the time I was examining these products, I had no problem with them. As time went on, I came to realize that there might be something missing.
On the surface, these methodologies seem like magic. I could see why they sold. But as I thought about it and reflected on my experience in corporate consulting, I came to a conclusion…
Most of the methodologies out there right now really don’t take the whole of their organization into consideration. They just think they do.
Interestingly enough, they claim to give the whole picture of the culture of the organization supported by their results, but I don’t believe they have been very successful up to this point . In my opinion, this is what the current Organizational Assessments promise to show you:
Here’s the kicker: This view commonly says there is ONE culture that describes 100% of the organization. That the whole of the organization has the same culture. I’m not sure that this is an accurate depiction of what is really going on in reality.
My position on why this occurs? Most culture assessments methodologies come to this conclusion is because of four main factors:
- Leadership is most often interviewed in a culture assessment which creates a bias.
- There is no cross-pollination of information before the survey results are tallied.
- Employees (and people in general) are trained and conditioned to assume culture is a top-down mandate.
- The current dominant POV is that culture is bound by the borders of the organization.
Well, I have news…Good news for some and maybe not for others.
Leadership, while it has influence, does not a culture make.
You see, there is another reason for organizational strife, bad ROI, silos, and poor project management in general. It is not just competition for scare organizational resources. It is also the competition between various, border-less, and unconstrained cultures vying for survival and influence. Cultures do not stay within departments (or even organizations)! Assessing only leadership’s perception of culture does not give a clear picture of how software, process, or procedure should be implemented. Each culture has its own way of integrating new information in the form of technological change and a more successful implementation team will understand and put this into practice.
What we really have in an organization are nested cultural nodes, or simply “Pocket Cultures“, which are all interacting in a very complex way. Through this interaction, the true organizational culture emerges. Some Pocket Cultures, like personalities, can be dominant in the organization. This does not mean the dominant culture is the correct one. It is just the one that uses strategic means to keep its position as dominant.
So, what does all this mean?
- Current organizational assessment tools are most likely ill-equipped to deal with a reality which takes this complex cultural interplay into consideration.
- Executives can expect a higher adoption rate and ROI if they understand the concept of Pocket Cultures.
- Implementation Project Managers should lead with an assessment of Pocket Cultures to find the best entry-point into the organization, giving them a much higher success rate.
Well, that’s my rant. I can see I have a lot of work to do on this idea. I’ll bring some of the big brains I know together to mull this over. Firstly, I will will be working on developing an assessment which takes pocket cultures into consideration. Everything after that is a hazy future-fog, but I bet you that it is fun out there!
For more on Organizational Types, see: Organizational Types or Wikipedia.
We are the music makers,
And we are the dreamers of dreams,
Wandering by lone sea-breakers,
And sitting by desolate streams;—
World-losers and world-forsakers,
On whom the pale moon gleams:
Yet we are the movers and shakers
Of the world for ever, it seems.
So, I was going to write about unemployment and how the job market has changed, but I got scooped by an amazing article by Drake Bennett called The end of the office…and the future of work. It is a great look into the phenomenon of Structural Unemployment. The analysis is very timely, but can go much deeper. Drake, if you plan on writing a book here’s your calling. There’s lots of good stories written on this subject out there by giants such as Jeremy Rifkin, John Seely Brown, Kevin Kelly, and Marshall Brain.
While reeling from the scoop, depressed and doing some preliminary market research, I happened upon a gem of a blog post by none other than our favorite search company, Google. Before proceeding on in my post, I do recommend that you do read the blog post by Steve Baker, Software Engineer @ Google. I think he does an excellent job describing the problems Google is currently having and why they need such a powerful search quality team.
Here’s what I got from the Blog post: Google, though they really want to have them, cannot have fully automated quality algorithms. They need human intervention…And A LOT OF IT. The question is, why? Why does a company with all of the resources and power and money that Google has still need to hire humans to watch over search quality? Why have they not, in all of their intelligent genius, not created a program that can do this?
Because Google might be using methods which sterilize away meaning out of the gate.
Strangely enough, it may be that Google’s core engineer’s mind is holding them back…
We can write a computer program to beat the very best human chess players, but we can’t write a program to identify objects in a photo or understand a sentence with anywhere near the precision of even a child.
This is an engineer speaking, for sure. But I ask you: What child do we really program? Are children precise? My son falls over every time he turns around too quickly…
The goal of a search engine is to return the best results for your search, and understanding language is crucial to returning the best results. A key part of this is our system for understanding synonyms.
We use many techniques to extract synonyms, that we’ve blogged about before. Our systems analyze petabytes of web documents and historical search data to build an intricate understanding of what words can mean in different contexts.
Google does this using massive dictionary-like databases. They can only achieve this because of the sheer size and processing power of their server farms of computing devices. Not to take away from Google’s great achievements, but Syntience’s experimental systems have been running “synthetic synonyms” since our earliest versions. We have no dictionaries and no distributed supercomputers.
As a nomenclatural [sic] note, even obvious term variants like “pictures” (plural) and “picture” (singular) would be treated as different search terms by a dumb computer, so we also include these types of relationships within our umbrella of synonyms.
Here’s the way this works, super-simplified: There are separate “storage containers” for “picture”, “pictures”, “pic”, “pix”, “twitpix”, etc, all in their own neat little boxes. This separation removes the very thing Google is seeking…Meaning in their data. That’s why their approach doesn’t seem to make much sense to me for this particular application.
The activities of an engineer would be to write code that, in a sense, tells the computer to create a new little box and put the new word in a list of associated words. Shouldn’t the computer be able to have some sort of continuous, flowing process which allows it to break out of the little boxes and allow for some sort of free association? Well, the answer is “Not using Google’s methods.”.
You see, Google models the data to make it easily controllable…actually for that and for many, MANY other reasons. But by doing so, they have put themselves in an intellectually mired position. Monica Anderson does a great analysis of this in a talk on the Syntience Site called “Models vs. Patterns”.
So, simply and if you please, rhetorically:
How can computer scientists ever expect a computer to do anything novel with data when there is someone (or some rule/code) telling them precisely what to do all the time?
Kind of constraining…I guess that’s why they always start coding at the “command line”.
I do have an original post in the mix which talks a bit about some of the unseen things at work in the unemployment numbers being posted, but for now here’s the words of Monica Anderson talking about inventing a new kind of programming. From Artificial Intuition:
In 1998, I had been working on industrial AI — mostly expert systems and Natural Language processing — for over a decade. And like many others, for over a decade I had been waiting for Doug Lenat’s much hyped CYC project to be released. As it happened, I was given access to CYC for several months, and was disappointed when it did not live up to my expectations. I lost faith in Symbolic Strong AI, and almost left the AI field entirely. But in 2001 I started thinking about AI from the Subsymbolic perspective. My thinking quickly solidified into a novel and plausible theory for computer based cognition based on Artificial Intuition, and I quickly decided to pursue this for the rest of my life.
In most programming situations, success means that the program performs according to a given specification. In experimental programming, you want to see what happens when you run the program.
I had, for years, been aware of a few key minority ideas that had been largely ignored by the AI mainstream and started looking for synergies among them. In order not to get sidetracked by the majority views I temporarily stopped reading books and reports about AI. I settled into a cycle of days to weeks of thought and speculation alternating with multi-day sessions of experimental programming.
I tested about 8 major variants and hundreds of minor optimizations of the algorithm and invented several ways to measure whether I was making progress. Typically, a major change would look like a step back until the system was fine-tuned, at which point the scores might reach higher than before. The repeated breaking of the score records provided a good motivation to continue.
My AI work was excluded as prior invention when I joined Google.
In late 2004 I accepted a position at Google, where I worked for two years in order to fill my coffers to enable further research. I learned a lot about how AI, if it were available, could improve Web search. Work on my own algorithms was suspended for the duration but I started reading books again and wrote a few whitepapers for internal distribution at Google. I discovered that several others had had similar ideas, individually, but nobody else seemed to have had all these ideas at once; nobody seemed to have noticed how well they fit together.
I am currently funding this project myself and have been doing that since 2001. At most, Syntience employed three paid researchers including myself plus several volunteers, but we had to cut down on salaries as our resources dwindled. Increased funding would allow me to again hire these and other researchers and would accelerate progress.
From our new Use Case Document (v1.0) on our speculated use of Artificial Intuition (AN) technology applied to finally and truly solving Semantic Search:
True “Semantic Search” is the holy grail of Web Search. When indexing web pages, the pages will be fed through an Artificial Intuition based device that produces a set of “semantic tokens”. These tokens might look like large integers; they are opaque to humans. But they specify, as a group, to any compatible AN device what the web page is ABOUT. It is a trivial matter to add those tokens to the search index side by side with the words in the document, which is what is currently stored in the index.
At query time, the same algorithm is run on the userʼs query. Longer queries will now become more precise queries since they allow more context to be activated. A set of semantic tokens can now be extracted from the userʼs query and matched in the index lookup process just the way words are looked up today. Even short queries can generate many relevant semantic tokens in a cascading process we could call “regeneration” – when a sufficiently specific query sentence is entered, all tokens identifying the context will be regenerated from the query. [Note: This is an expected but not yet experienced effect.]
The result will be a high precision search that returns documents that perfectly match the userʼs query. There will be no false positives caused by ambiguous word meanings, and some documents returned may not even contain the words in the userʼs query but they will still be spot-on ABOUT what the user wanted the results to be about. All efforts that have been called “Semantic Search” to date are still syntax based. Some, like PowerSetʼs technology, use grammars. But grammars are not semantics, they are describing syntax. This use of the term “Semantic Search” is a marketing parable.
Final version should be available for wide distribution soon. Email me if you would like a copy at mgusek at syntience dot com.
My 90-year-old Nana (Paternal Grandmother) is an inventor and her inventions work.
For example, one of my Nana’s inventions is a color-coded flagging system for dog doo-doo left in her front yard by neighbors who don’t clean up their pet’s mess. The system is simple. If it is a fresh dog dropping the marker (A tomato stake and colored plastic bag.) is yellow which warns people not to step there lest they need to clean their shoe of said droppings.
As the dropping starts to “mature” (Or get dried out and easier to pick up.), my Nana replaces the yellow flag with an orange one to inform her which ones are ready to pick up that week. These flags, in concert with a systematic lawn-checking walking pattern done on a weekly basis, keeps shoes clean and dog doo-doo marked for elimination.
Did I mention each flag has “Doo-Doo” written on the plastic in blue Sharpie?
The invention of this system is made real by the operations of the mind of my Nana. This process of invention is inherently “Model Free”, meaning that my Nana did not need to know differential equations or string theory to make her idea manifest. What’s “Model Free” mean anyway?
“Model Free” means you do not need a PhD or to know the hard sciences like Physics to solve a problem. You just observe the problem and a solution comes to you.
Many in the fields of Economics, Neurology, and Computer Science have been trying to come up with ways to solve complex problems using descriptive models. However, nothing seems to work as well as good ole fashion gray matter. If you wonder why this is, don’t think it is because these complex problems cannot be solved. We just need to change our perspective to understand the operations of the “Model Free” so we might expand our tool sets to encompass the methods of Creation, Natural Construction, Emergence, and Complexity. Innovation also falls in this category…To a point.
So is innovation “Model Free”?
Since innovation encompasses ideas and inventions applied successfully in practice, I have to say not so much. Innovation can be “Model Free” if it is implemented in a Model Free environment, but innovation quickly becomes subject to the introduction of models when the innovation is tied to a corporate agenda or to the scientific method. Using the example of my Nana, she successfully implemented a working invention which made her life much easier. “Model Free” innovation can quickly become “Model Rich” innovation as soon as someone says:
PROVE IT! Tell me how this makes life easier! (Or how it saves/makes me money…)
In the case of business or science, this means MEASUREMENT. So, I’m expanding the definition of “Model Free” to include the absence of measurement. As soon as an innovation starts including aspects of measurement, it ceases to be completely “Model Free”. Can you see the guys in the white lab coats and the consulting khakis approaching a 90 year old woman and attempting to get her to prove the “value proposition” associated with her design? Ridiculous, but measurement in its ivory tower has overwhelmed natural processes of creation and has brought us to the extreme brink.
When did we start believing measurement and models are the the source of invention and innovation, not the other way around?
It is a great confusion, imho.
In the first post setting up for the discussion of Pull, I mentioned that in this second post I was going to go over the personality type that represented the modern push-oriented individual. I think I’d like to stop for a second and before I do that clear something up. After reading the first post again, I realized that I was focusing rather negatively on the current situation in Command and Control (C&C) at the large scale, only exploring the weaknesses.
I want to make sure that everyone understands that C&C is NOT inherently a bad structure or framework for an organization.
It is when the extremes of C&C are taken as the only way to structure an organization that we really start to see the brittle and inflexible nature of this organizational structure. Here, for the benefit of later discussion, is a table which fairly shows the strengths as well as the weaknesses of the C&C hierarchy:
|Highly Efficient||Rigidity Against the Unexpected|
|Conserves Resources||Requires Complete Theories|
|More Predictable Outcomes||Environment Must be Ideal|
|Easily Measurable and Transparent||Shuns Innovation and Invention|
|Logically and/or Reasonably Defined||Can Ignore Hidden Risks|
|Repeatable||Tendency to “Break Big”|
|Plug and Play Models / “One Size Fits All”||Black and White|
|Highly Productive||Doesn’t Handle Complexity Well|
|Doesn’t Require Practice|
I want to ensure everyone gets this simple fact as well:
C&C is a human-made tool just like a hammer or a wrench is a tool.
In this case it is a tool which organizes people and resources into a management framework. Using a certain tool for a certain job can yield more effective results. It only makes sense that we start looking at the emerging organization as needing a more elegant tool to organize people and resources.
I like to tell my friends at Syntience: You can use a hammer to drive a screw into a wooden board, but it is much more elegant to use a screwdriver.