Aug 7, 2023 | Analytics, Leadership
We all need to become ‘knowledge workers’ say the pundits, who generally fail to define just what that term means, and how we achieve it.
Most would simply apply some added practical training and education, and bingo, knowledge, but I suspect it is more complicated than that.
Knowledge is way more than just education and training. It is also the wisdom of experience, domain familiarity, networks of people who can be called upon, and a capacity to make connections in non-obvious ways. It is intangible, as individuals, we have no physical stocks of knowledge, although we do now have relatively unlimited access to its sources.
The value of knowledge is also very hard to define, if not impossible, and it is not of much value when it stays in one place. Its value is highly contextual. It is of little obvious use having an expert in genetics when you are struggling with a problem of commercial governance. However, when you dig deep enough, you often find there are lessons to be learnt from other domains that can be applied, and in the process of digging, you learn.
The real value of knowledge is when it flows from one to another, and on to many, then, magically, it grows, evolves, and is put to uses not previously considered, creating even more value.
Therefore, the definition of a knowledge worker should be more like ‘Builds, shares, and leverages data for use beyond their domain’.
Improvements and alternatives encouraged.
Jul 12, 2023 | Analytics, Marketing, Operations
Anyone who reads my stuff on any sort of regular basis will know I have been deeply engaged with the potential impact of AI on all of us, since I stumbled across ChatGPT in early December last year. Of particular interest is the apparent potential for efficiency gains, particularly amongst the SME manufacturers I serve.
On one hand, I have been excited by the potential of AI to generate efficiency and expand the operational scale of SME’s. On the other, scared shitless at the potential for bad actors to sneak into our collective pockets and steal everything.
I need to write to think.
It forces me to sort out the stuff swimming around between my ears, as when I can articulate it sufficiently to write about it in some coherent manner, it leads to some level of understanding.
So, here is my list of the good stuff, followed by the bad, as it relates to the core of my business: strategy and marketing, starting with SME’s and the written word.
The good things AI can do for you.
- Summarising large blocks of copy, even when it seems very messy.
- Brainstorming; ideas, subject lines, complementary ideas, headlines.
- Editing and grammar. (I have been using the editing and ‘speak’ functionality of word for years, it is essential to me, and is AI that we now just treat as part of the furniture)
- Assembling descriptions and fact sheets
- Looking for logical holes in an argument
- Repurposing copy from one platform to another
- Research
- Outline and first draft.
- Translation and transcription
The stuff AI is no good at doing.
- Humour
- Reflecting current news and events
- Factual reliability. (Sometimes, it just makes stuff up)
- Finding a good metaphor
- Being creative. The great irony with creativity is that AI opens a whole new set of what is possible with visual tools, which can then combine with verbal cloning tools to completely alter apparent reality.
- Looking ahead
- Breaking complexity down to ‘first principles’
- Pouring another glass when faced by a blank page and a deadline.
Then there is all the other stuff AI will do, and evolve to do in the very near future, that is not writing. Graphic design, integrating currently separate digital systems (API’s on powerful steroids) identifying trends and holes in huge masses of data. The impact on medical technology is already profound. When the human genome was first successfully mapped in 2000, the cost of that first success was in the tens of billions of dollars. Now you can send away a sample and have it returned with your genome map for a few dollars overnight.
The key it seems, is to be very good at explaining to the tool what it is you want, in the detail a 5-year-old will understand. As the header cartoon illustrates, being human while driving this stuff will rapidly become the differentiator.
Header credit: GapingVoid.com.
May 17, 2023 | Analytics, Management
The ‘Long tail’ is a graphic recognition of the Pareto principle, the 80/20 rule. It holds true in every situation I have ever seen. Rarely exactly 80/20, but always somewhere in the region.
We tend to accept it as a reflection of revenue and profit: ‘20% of our customers generate 80% of our revenue’.
Often, we manage our businesses, particularly the sales effort, as if this is the only place the principle works.
It applies equally to transaction costs, long term potential, management attention, geography, product class, customer type, and many other useful to know indicators.
Take for example, those customers that in 5 years’ time will be amongst your most profitable. Chances are, they are currently hiding somewhere in your long tail, denied the focus and assistance they might value that will assist them to grow in importance, simply because they are not seen. I call them ‘Strategically Important Customers.’ Unimportant now by most measures, but critically important in the long term.
Ignore these customers at your peril.
So, how do you find them?
- They meet the parameters of your ‘ideal customer.’
- They have a problem to which you have or are developing the ideal solution.
- Your share of their ‘wallet‘ is low when they meet other ‘ideal customer’ parameters.
Conversely, set your sales team to dig them out of your competitor’s long tail, deliver value to them, and convert.
An equally important task is to identify those customers who cost more to service than their current or potential profitability. The best thing you can do is send them to your competitor, so they can be saddled with the usually hidden transaction costs and low margins.
The profit and Loss statement is, or can be, a remarkably efficient way of capturing the information required to focus resources in the most optimised manner, dictated by your strategy. A P&L by customer, product, geography, market, and any other driver can be generated using readily available and relatively simple tools. The challenge is in overcoming the institutional definitions of how the data for the statements is collected, collated, and presented.
For example, what is an overhead, and how is it allocated?
In a factory, is the cost of supervisory staff allocated to individual product lines based on the actual costs, some rough ‘standard’ cost, or not allocated at all? Are those costs seen as overhead? Is the total overhead spread across total production by some magical formula devised by the accountants, or treated as a cost centre and managed proactively? What about those directly on the production line? Are their costs allocated in proportion to production volumes, customer offtake, or some mythical ‘absorption’ rate?
Take the time to ‘slice and dice’ your Profit and Loss statement. After having tackled the greater challenge of having the costs as they are actually incurred reflected in the customer P&L statement, you will be in a great position to take decisions that will have a significant impact on your overall profitability.
Feb 1, 2023 | Analytics, Governance, Strategy
Mark Zuckerburg has a lot to answer for, disrupting as he has the lives of my children. However, he is also very smart and rich, so being annoying must have something going for it.
When pitching the $5 billion Facebook float in 2012, Zuckerburg wrote to prospective shareholders via the prospectus, a letter that outlined his vision of what Facebook had become, and would continue to be.
This is to my mind the crucial paragraph, buried in the body of the letter.
“The Hacker Way is an approach to building that involves continuous improvement and iteration. Hackers believe that something can always be better, and that nothing is ever complete. They just have to go fix it — often in the face of people who say it’s impossible or are content with the status quo”
It now seems he has taken that perspective of his obsession to the world of virtual reality. He has invested billions of shareholder funds in his personal vision, triggering a loss of billions from the market value of Facebook, now Meta. He does not seem to care, but many other shareholders do. They must be getting very annoyed about now, the value of their shares dropping 70% from its peak 15 months ago.
At some point, businesses must develop stable, repeatable processes that just gets the mundane stuff done.
Facebook did that with remarkable efficiency for a long time, creating a river of cash. However, ‘hacking’ has taken hold.
Hacking to improve mundane processes should be part of the culture, so long as the experimentation is part of a managed process. The alternative to that discipline is chaos.
Mixing the cultures that accommodate the disciplined repeatable processes that get the bills paid, and the sometimes chaotic, creative environment of “hacking” is a function of the leadership of the enterprise.
Management needs to be “Loose” to accommodate the creativity and experimentation necessary for process improvement, while being “tight” to enable the learning that comes from experimentation to be incorporated into standard procedures when they prove to be an improvement.
Loose/tight management, is the environment in which “Hacking” Kaizen, or whatever you choose to call it thrives.
‘The Zuk’ has imposed his single minded obsession with hacking on the culturally poisonous monolith he created, because he can. If his VR vision becomes a reality, Meta share price will not only recover, but break all records. I do not expect that at any time soon, particularly if as rumoured, Apple comes out with their version. Meta now faces a governance challenge that could be a real game-changer.
Addendum February 4, 2023.
This article from the Statista website details the progression of losses Meta has booked on Zuks metaverse bet. $US13.7 Billion in 2022, on an increasing trend. While the share price has dropped dramatically, if you look at the PE ratios before and after the drop, it seems to me that the price is settling back to where an old fashioned investor, one who expected a return from dividends rather than capital growth on the basis of a never ending share price increase, might expect it to be. The same comment can be applied to many other digital pletform stock price drops over the last year or so. Fundamentals kicking in??
Addendum 2 February 5, 2023.
They are coming thick and fast!. I read this ‘Wired’ article by the brilliant Cory Doctorow this morning. It explicitly defines the life cycle of social platforms, something we all ‘sort of’ knew but dismissed in favour of the value for early adopters, progressively locking in users, at the same time they squeezed the algorithms to generate ad revenue. Doctorow calls it ‘Enshittification’, a lovely word. Towards the end of the article is a quote from a very young Zuckerberg ”I don’t know why tney trust me, Dumb fucks’. Here is the news Zuk, we don’t!!
Nov 23, 2022 | Analytics, Strategy
Strategy is a bit like economics, go to 5 so called strategists, and you will get 6 opinions.
This is terminally annoying to our accounting and engineering friends who thrive on certainty. However, it is perfectly OK, as we are dealing with the future, and that rarely turns out to be what we think it should be.
The challenge is a Bayesian one.
Over time becoming incrementally less wrong.
Good strategy enables the pace of that Bayesian improvement to be accelerated, sometimes by a geometric proportion.
Strategy generation is a process, it is about creating the future. It has not happened yet, so cannot be ‘proved’ in any definitive manner, until you have the outcomes to count. By that time, it is too late to do anything but adjust and learn for the next time. This does not imply wholesale change, which only emerges from poor strategy in the first place. By contrast, good strategy enables subtle adjustments to be made over time while the direction holds firm.
This makes strategy generation a series of choices powered by an assessment of the relative odds of varying outcomes emerging.
Over the years I have whinged about the mediocre quality of many marketing people I have come across, intellectual dwarfs that fall into ‘marketing’ because they failed to make the grade at something useful.
It is ironic then that almost without exception, the best marketers I have worked with, and for, have found themselves in marketing after becoming tired of the restrictions placed on more externally disciplined professions: accountants (which is where I originated) lawyers, scientists, and medicine.
The combination of the automatic discipline of the scientific method with the creative thinking based on quality data required in marketing and strategy is a potent combination indeed.
Header cartoon: courtesy, again, of Dilbert and his mate Scott Adams.
Oct 19, 2022 | Analytics, Operations
No matter your businesses size, digital capability has become a driver of commercial sustainability over the last decade.
It has become a clear case of digitise or die.
This does not mean you have to go from an analogue starting point to fully digitised in one step, that is unrealistic. However, failure to start the digitisation journey will eventually be the undoing of your business.
There are a number of logical steps you can take that will build capability quickly, without massive investment, although some investment, particularly of management commitment, is necessary. However, like any investment, you should expect a return.
If you are starting the journey, the following is one set of the steps you might expect to mount, not necessarily in this order, but this is a common pattern I have seen.
Step 1. Assemble a clear picture of the currently available data. Mostly this will be ad hoc, and manually collected. Machinery purchased over the last few years will have the facility to capture data that is often unused, or under-utilised. This might simply require some connection between the data logger in the gear to your server, or better still, to a cloud application.
Step 2. Build a common system for the assembly of data that will enable it to be analysed in a consistent manner. Many factors have differing sets of ‘data languages’ based on legacy practices, and short-term convenience. Creating a common data language is important, and the best tool for doing this are to map all the processes in the factory, and break them into what is in lean parlance, ‘value streams’. The languages can then be tailored to make sense to all who meet them.
Step 3. Invest in further data capture. In the early stages, this is often a case of retro fitting devices onto existing machinery and downloading it all into a common data base. Depending on your operations this can be as simple as excel. There are many available low level options that are of a modular design, so that as capability grows, the modules can be implemented progressively.
Step 4. Invest in the capability to analyse the data and turn it into actionable insights. It is at this point that people become invaluable to the system. Any digital system can only respond to inputs in the way they have been instructed. They are no good at assessing the inputs for which there has been no or little precedent, you need people for those vital tasks.
Step 5. progressively implement data generation and analysis to inform operations. Use the feedback to constantly improve the quality of the data and the analysis that is used to manage and improve operations.
Step 6. Rinse and repeat. Digitisation is not a task with a completion date, it is a journey without an end.
As I headed towards the ‘publish’ button, a notification of a new program by the Victorian government popped into my inbox. The ‘Digital jobs for Manufacturing‘ program will fund training of employees of eligible Victorian manufacturers in a 12 week part time course run by Victorian universities. Have a look.
Header credit: Tom Gauld who takes an ironic, but widely felt frustration felt by SME’s at digitisation at www.tomgauld.com
