Unlocking Intelligence In The Next Decade

There’s a shift happening right under the radar and I think it might be time to acknowledge the urge to jump on this intelligence train. The analytics scene is very much alive (and kicking) and above all, it’s changing, yet… a lot of people are still exploring their first encounter with any form of data or analytics. People who have mainly been using Google Analytics as an analytical toolset are not evolving to a next phase, while there’s so much opportunity to create added value with data and analytics, that we could easily broaden its scope to a more general term called intelligence. Short for, in the near future, probably more focused on, machine intelligence.

In the meantime, I’ve been following some courses in this thing called predictive analytics, basics in machine learning and started to experiment myself with custom tag management integrations to test one of the marketing analytics shifts, a more moment-driven approach instead of end-to-end goal-driven approach. It triggered me to discover R and play with it as well. On my radar is Python.

This is a personal and very broad view on analytics, where it’s probably heading in the next few years and why we all should think bigger about this topic, far beyond marketing & business analytics. Some of that in there as well, yet… a more holistic view on data, the use of intelligence and the world, right now. Ideas on how to look at this as an asset. An asset that uncovers intel about other assets. On intelligence as a whole. How we potentially can unlock it to drive prosperity.

One could describe Intelligence as the ability to connect the dots of any sort or form of intel and turn it into an output or an outcome. That’s somewhat applicable to any industry or topic even. Creative with Intelligence, as I like to call it, gains momentum as several trends are emerging.

Some of these trends are already happening as we speak. Some other items might sound a bit more futuristic, yet there are some signs and patterns noticeable that we’re heading in a specific direction.

Intelligence: Value vs Added value

Before you begin reading the rest of this article, I want to make a clear point-of-view on this value-thing. Value is the amount of effort you put into something. For example: I worked for more than 30h on this article, so its value is at a 30x hourly rate. The value of its substance is relative and pure perception into the eyes of its beholder.

Added value is the “hmmm” and “aha” you create on top of that article. The outcome, whether it be an insight, a thought, a trigger or even a specific action, … this is the potential added value. Depending on the magnitude of this insight that drives the action, you’ll add enormous amounts of value or just a tiny bit. If the insight is eye-opening to someone, the added value can clearly be defined and turned into a next step, trigger, … If the insight is mediocre or even useless, the added value is just a perception. Your own perception, that is!

Added value comes in many formats and in this case, for analytics, it comes in the shape of layers. Layers to help support decisions, trigger actions or define and redefine any kind of strategic lines. The second type of layers are the layers on top of existing physical or digital products. They can hold all kinds and types of data & techniques combined with technology, or even shaping technology, in some cases. The data isn’t as interesting as such. Handling data is crucial. Extracting the insights to create the added value is the hardest part as this is reading the data in the correct way. This needs a sense of intuition, imagination, practical creativity and even vision in some cases.

Added value could be thought of as being Creative with Intelligence.

Get Serious with Your Intel(ligence) & Start Today!

The way I see it, the current Analytics scene is defining the middle, right there at the border between company thinking and customer thinking. Business goals at the left (black) (company-focused), analytics in the middle (gray) (company-focused & customer-focused), product & marketing (white) (preferably customer-focused/centered, especially for marketing, the product is a no-brainer). The challenge is in the gray, always has been, always will be.

Depending on how you handle the analytics part, let’s call it intel from now on, you’ll be a more outside-in focused company or an inside-out focused company. Those who balance on the edge of company and customer in the middle area, are the ones that build the best products, have the best marketing and know how to change their course without impacting the customer (that much). They know how to turn intelligence into something useful, something practical.

It’s OK to know just a little about analytics in general. Yet, you need to keep an open mind and a wide perspective as this field keeps growing due to more bits than atoms. More on that later in the paragraph on “Data Capital”.

It struck me even more than before of what I already knew: this field is GIGANTIC. It’s changing and it’s growing fast, emerging towards a very mature space, bridging the gap between business (strategy, communication, etc.) and performance (ex.: making good products, less useless ad spend, etc.) by actually blending creativity with intel and even art. In the context above, Performance is (getting) commoditized as we shift to a more service-driven society.

Marketing is Changing, Fast!

The data capital era has begun. Google just gave a final wake-up call to the world by shifting from a mobile to an AI-first company. I’m not like any other consultant, surfing on this wave of AI, but… building a company based on recursive intelligence is quite a smart thing to do. Data capital has been their biggest asset up till now and they really understood that machine learning, deep learning & neural networks, especially in their case, will incrementally feed enormous exponential growth due to the scale only a few in the world currently have. Inclusive growth in many unexpected industries and branches, that is! It’s happening in software and machines! It’s actually defining software more than machines. It’s defining software _in_ the machines, creating better products and machines in the end. Build the machine that builds the machines. Google and some others are building the parts for the machines that build the machines.

Honestly, I really think marketing will be shaken up, as an industry, as a whole, in the long run. Like in retail, any new product, and thus its launch of its life-cycle is the trigger or reason to do marketing. The story, the other trigger for marketing, exploits its existence out of the product. As machine learning is eating software, and more and more products are getting digital, the product is taking marketing to a whole other level. You’d better become a real good storyteller in the future, as the product will eat the current way we do marketing these days. The illusion of marketing & communication-solving business problems will simply blow up if the product isn’t good enough, physical or software. Same thing for marketing. Mediocre shit won’t be able to create that illusion. That’s why digital transformation & communication go hand in hand today ;)

Machine Learning is eating software and thus the world

What’s Happening Right Now? How diverse is This?

There is so much happening in the Analytics space. It’s a, excuse me for my bluntness, FUCKING BIG blue ocean out there. Gigantic lakes (width) vs deep puddles (depth) would be more appropriate to use in this context. Like Mr. Lemaire says, there’s gold in the margin (Dutch article).

Not sure if you can call these interferences, digital anymore. One could say that Analytics is getting refurbished. Business Intelligence is transforming from a pure descriptive stage to a more predictive/prescriptive stage with the help of machine learning, making analysis more obsolete (at first sight, automated) and turning those into direct and concrete recommendations. On the other hand, algorithms and the needed data modeling for that are getting build into consumer products, software, and industrial machinery, skipping the recommendations, an analyst or technical consultant would provide, altogether by building end-to-end pipelines and iterating through the whole process, over and over again. Besides consumer products, software, and industrial machinery, this process is getting used and far more abused in art & interactive installations and visualizations.

So… One could say that data is being used to build, feed and incrementally improve pipelines with and for algorithms, to make sure they blend almost into anything software or hardware related.

Over the past few months, I’ve stumbled upon examples (apart of the traditional examples like AI in mobile apps) of how wide and extremely creative data & algorithms get applied to all kinds of different challenges, in all kinds of industries. Some with a particular purpose, others with no purpose at all!

And I’m sure there will be a ton more of these examples popping up as we speak. Yes, it’s true. The current state of technology allows us to look at particular sources of input in a different way. Things we couldn’t read or understand, like audio, video, and images, now suddenly are turned into a form of intelligence with the ability to create progress in any output or outcome.

How to Move Forward, Today?

If you’re just getting started with anything related to data or analytics, this is the time to start and skip the legacy others have and move forward as from the start! First, … stop thinking about it as pure Analytics. Think about it in any form or context of holistic topics: Intelligence.

You can skip the standard Google Analytics basics as a whole if it comes to implementation if you’re comfortable with that. You might even skip the CRM and ERP vendor selection if you’re comfortable with that. You can skip reporting & BI and go for a recommendation tool instead if you’re comfortable with that.

The point is that the context of your environment requires you to obtain a more agile approach. A structure that is potentially (extremely) scalable, yet tailored to your market, requirements, goals and even your team. Build processes, but methods first (!), by turning intel into output! The trend from the typical clean & pre-defined structured ETL data to the RAW data handling approach is a perfect example of that. More on that in the paragraph “From aggregated to raw data capital”.

If you’re starting now, don’t do what others did. Explore the current scenery first, before you act. Compare it to the equivalent of Africa skipping the PC’s, and thus landlines, and going all-in on mobile as from the start. Compare it to the equivalent of China almost skipping cash altogether in a few years, avoiding credit cards and use Whatsapp to pay for anything and even link QR Codes directly to bank accounts, with the obligatory fraud of course. Think at least one step ahead, skip the Olympic minimum.

A job, a career or a form of mastery in any intelligence related space can offer a good up to great economic security. And… to me, it’s an area which allows you to think (extremely) big about the use of intelligence and take on the challenges the world is facing after the internet revolution: the wellbeing of mankind (energy, health, transportation, economics, communication, …) characterized by glocalization (climate change, scarcity, overpopulation, waste management, …). We need a glocal approach to global well-being.

Ok. Quite the intro. And I’m not sorry about that.
But … let’s get into the specifics of this topic: Ways to think about Intelligence.
Intelligence has a very holistic ring to it. It’s up there with terms like “emotion” and “art”.
It can mean different things to you than the person next to you.

These are several ways to think about Intelligence, out of what elements it exists and how it can be of value or added value depending on its context. This is a personal view on Intelligence and ways to think about its elements, parameters, and quirks. Intelligence, to me, is being resourceful with intel to improve a holistic outcome or achieve a specific output.

#1 – Data capital

There’s GDP (short: stuff we sell/trade), the most talked of measurement it if comes to economic value. Yet, there are five types of capital in the world. Natural capital (resources, sinks, and processes), human capital (short: talent and collective skills/knowledge), social capital (short: the network), financial capital (short: money in or from the bank) and a very interesting one as well; manufactured capital! (short: machinery, real estate, … assets in the process).

Nowadays, there’s a sixth type of capital to exploit: data.

GDP doesn’t cover the load anymore. Neither are the five types of capital. Data capital is added value to our economy. To keep it simple, look at any digital platform to get the idea behind data capital. A good example, to me, is Airbnb, to just name one. It’s a two-sided marketplace based on heavy data assets, light on physical assets. No hotels, no rooms, nothing owned (not anymore), just data of those physical assets in a format that creates added value, as a sort of layer, on top of the current economic value created by those physical world/assets. Making existing products smarter with tech and thus data can add value (bits on top of atoms*). Building layers on top of existing markets can add value or even create new kind of services (bits out of bits*) and products (atoms out of bits*).

Technology fuels progression, yet the real value comes from working with the data, meaning… the capital is the action in the data. The layer of data that makes a service or product more actionable. I personally don’t believe technology changes us. It exposes us. We discover technology first, yet it doesn’t change our behavior: we just always pick the easiest route to a solution whenever we can. A great example is the concept of voice and the current state of voice assistants, especially Google Home. As long as there is no app which makes good use of the possibilities and thus works with the data (in its simplest form: the ins, commands, and outs, replies, of voice) to make it useful on a daily basis, … voice assistants don’t have any added value to our lives (atoms out of bits & bits out of bits*).

Like any other resource, data capital comes with a technical, and to some extent, with a social debt. Marketing has a well-known, kinda, social debt. Using quick-fix one-trick pony tools will accumulate your technical debt. The possibility that you’ll build up more technical or social debt than direct monetary debt is realistic as we are moving towards less atom world*. 

In fact, when a business has fewer physical products, it should heavily invest in data as an asset! Just one type of data can even be enough to build a whole business around the people using this kind of data.

If the concept of bits and atoms resonates with you and you understand that we’re currently creating more potential data capital than GDP, allow me to go in depth on what I think are some ways to look at data capital, a main element of Intelligence.

#1.1 – Current state of data capital

It strikes me over and over again, how few people in a company really know their numbers. Whenever I can, I’ll ask for data. In the past 12 years as a marketer, I’ve only met two clients that knew their numbers. How many sales they did per day, per week, per month. How many active customers they had at that moment. How many new ones they got per month, etc.

The basics of a knowledge base: Descriptive Analytics.
Know what’s in your database. Know who’s in your list. How it’s a current state is at and how it evolved in the last 90 days or even year. This is so crucial to have a clear view of the current state of your data capital. What data is available? What’s missing you know? What can you learn from the current state your business or project is in?

Every time, I’m baffled by the dummy level so many people and companies are still at.
Struggling to get insights is one thing, but turning those insights into knowledge & wisdom is really a big issue that will not be solved anytime soon. It takes time to comprehend and read data. Tools can fix a part of the issue, although a fool with a tool is still a fool.

#1.2 – The leverage state of data capital

Like any other asset, data capital needs to grow to fuel growth. This state is what I like to call the awareness state. Not that many people are able to get to this state. It’s the state where they realize that by leveraging the current data capital, it can grow to fuel growth. This is the state where you understand that no matter what the outcome of a project is, good or bad, it will give you leverage anyway. (see also the chapter on R&D and beneficial derivatives)

Let’s take real estate as an example to explain this matter. You’ve bought an apartment by lending money from the bank. In order to pay the mortgage and make life a bit easier, you rent a room of your apartment for €450/month to a friend. Your debt gets paid and you’re living in your own apartment at zero costs. You keep it that way. That doesn’t fuel your growth, does it?

There are two ways to fuel growth. Predict growth, including working on growth is one. Or just work on growth as you go, is two. You’ll need to work on growth no matter what. You don’t necessarily need to predict it. Either way, it acquires an investment in time and other resources (human, social & financial capital).

Marketing & Analytics should go hand in hand no matter what. It’s ridiculous if they’re split up. How can marketing really measure their performance (and by that I mean ROI) without the help of analytics? In a marketing context, the business analytics insights are often forgotten. On the other hand, the business needs to get their insights from the market reaction on the product & marketing (both, as they best go hand in hand). The interest of investing in cost-cutting & ROI driven is leverage for building marketing knowledge and business wisdom. One who is creative, ties it all together, naturally. One who aims to build processes handles it as service and will always focus on the wrong part of the chain due to ego as cold hard numbers don’t lie.

#1.3 – Renting data to build data capital

This is a fairly easy thing to understand. Yet, few people invest in this based on a holistic approach. Turn third-party data into first-party data whenever you can, directly or indirectly (on the back of achieving a certain goal or KPI). The leverage of renting the audience capabilities of Facebook (covering half of Europe unique online reach on the internet by the way) accompanied with some other old-fashioned marketing channels like TV or Radio can give you a lot of market insights and/or potential audience building capabilities, keeping GDPR Compliant, without a doubt. Sigh.

#1.4 From aggregated to raw data capital

One of the biggest issues of the adoption to extract the value out of data capital are the vendors. Vendors have always been scaled towards a certain market and thus mass of customers by picking the easiest path, the path of what the client wants. An outside-in approach, which is theoretically good, yet… the client doesn’t always knows what he wants and blame human behavior for this, he always chooses the path with the least amount of resistance.

In the data world, this means that vendors build aggregated and processed metrics and steered upon vanity metrics to feed and solve the problem of shitty KPI’s. Those days are over within a few years and it’s time to jump on this bandwagon of raw data handling.

With the uprising interest and progression made in machine learning techniques, we’ve come to a conclusion that our aggregated data is good for standard reporting but that doesn’t solve shit.

The biggest shift in (digital) analytics is now mainly the shift from an aggregated user- and session-based to a unique user-based view based on hit-level data. Who’s to blame? Google Analytics! Well adopted and created for the masses. Don’t get me wrong. I love Google Analytics, it was groundbreaking when it started to track web data and until this day Google Tag Manager is by far the best product Google has come up within the Analytics scene, … Yet, the time has come to build a layer on top of that with raw data to do serious analytics, the one that has real impact! Also thanks to the access to a good tag management system they build so you can build the data scheme’s you need by yourselves.

Going from sampled, aggregated and prepared session-level data to unfiltered, raw hit-level data is a good thing and creates the leverage to do more and build on your data capital in a cloud-based platform, for instance, Google Bigquery:

  • Building your own device graph instead of Google’s or Facebooks’ graph
  • Tracking specific GDRP compliant hit-level data
  • Connecting different and other offline and/or online data sources albeit from an app or chatbot even, based on unique user-level data!
  • Feature tracking for machine learning on the granular level you want instead
  • The ability to apply statistics & machine learning for personalized experiences (PII) instead of a visit or session based experience

This IS the time where behavioral tracking connects with long-term business outcome aka end-to-end tracking (the online/partly offline customer journey) from click/visit behavior till the last purchase before churning is possible!

As our initial journey for whatever we are planning to do almost always starts on a digital device or platform, … we can unlock this raw data approach to any industry or problem that we’re facing: Grades of students, the way they learn new things, the way they process information, how we grow our crops, how the weather evolves over time, etc. all these parameters, if quantifiable, they can be used as a form of intel to come up with new ideas and solutions to problems.

RAW Data is very important! It’s an important building block in the evolution of (digital) analytics.

#1.5 Unlockable Data capital

One of the biggest issues with tracking is the quality of the data. If 80% of the tracking data needed is qualitative than you can do a good analysis. If not, you’ll need to fix this first. So your data capital is potentially worth millions, yet it needs to be unlocked in order to process into an added value! No machine learning without clean data. It really doesn’t matter if you plan on using a tool like IBM Watson, or Google Tensorflow, Dataiku or specific narrow AI tools, … You need to sort & clean your data first!

The caveat is here the technical debt of tracking all the things versus the things you only need to accomplish a great return on the investment (ROI). Should you invest in a DMP or a CMP? Maybe a custom build a data warehouse with real-time components?  Or will a simple excel file be just enough to cover your needs? Depends on what you want to achieve. No matter what, you’ll definitely need a data engineer.

#2 – Secure Privacy Pipelines: Blockchain, GDPR Compliant APIs & (Mobile) Apps

Feeling safe & secure is a part of the Maslov pyramid. Hence it’s a key aspect of anything related to our comfort. Our privacy is valuable, yet no ordinary man or woman can describe the specifics for their online double. So… it’s crucial to think about this in a broader way.

If data capital is on your agenda, GDPR & any related privacy and thus also security items will probably be on your agenda as well. No doubt about that. However privacy is often confused with security, privacy comes in different formats depending on the use and usage of that particular data, meaning… using personal data aka PII (personally identifiable information) is interesting, yet storing that data comes with a lot of caveats.

Using PII for accounts, personal services, personal products or any other thinkable application is actually always applicable in a way how JIT (Just in time) works. The PII, at best, should be the last new and up-to-date version if possible. Kind of like, real-time. The right PII at that exact moment. Just that moment, for once. Like the “Ask once, use twice” principle of e-Estonia like mentioned in this article by Frederik. We can learn a thing or two about digital society. It’s a good start but Tim Berners-Lee Decentralized approach with the Solid platform is probably where it’s heading next, to get to that real-time up-to-date PII info.

it's me mobile app - secure personal ID

Everyone thinks blockchain is all about cryptocurrency and contracts, yet… it’s potentially the best solution to keep personal data up-to-date and secure. No need to store this data anymore as a business. Imagine a country where _all_ the PII is provided by the government. They could charge us all a fee but that would be plain wrong. Instead, the could cut costs of all kind of services at a very high pace! That’s basically what e-Estonia is in a nutshell.

Besides that, it could be a great thing for making commercial platforms more trustful as well. GDPR wouldn’t be necessary anymore, right? In the past, harvesting and thus storing the data was a key aspect of getting to know more about customers. If Facebook could tab into a secure government data service, one could more easily control the amount of PII you want to give to Facebook. If the government wants to protect us, well… let them do it the proper way then. Blockchain _will_ be used for PII, I’m 200% sure about that and not only for crypto purposes. It’ll fix a lot of privacy clutter.

Make it even a government-as-a-service and it would lift the digital economy as a whole and increase security and reduce fraud in many ways (Dutch article about the issue).
Hear me out.

Let’s face it. The illusion of one-to-one marketing is actually plain old science fiction. That would mean that marketing should be based on segmented groups, that get personalized messages, based on a series of predefined (limited) intents with a touch of personal identification in order to create the illusion of segments of one. Once again, the cost of this is immense (see also the paragraph on the Cost of Complexity). The amount of data and infrastructure needed is only possible for the happy few. And even then. This government service could help to create these kinds of advertisement possibilities. GDPR could actually be controlled if done so. But… this could take advertising to a whole other level where this AR hyper-reality world would totally make sense, yet, let’s face it, humanity is extremely slow in adoption. How cool it may even look, it’ll take another few decades. A century even.

On the other end, and maybe the most important item of all … just take away the pain of storing PII for any small, medium or large business so the hurdle is gone to use data as an asset.

I would recommend our government and other governments to invest in this on a country-wide scale, where we, as a business or person, could tap into and skip the whole issue of stocking GDPR compliant PII data and all its legal issues. A blockchain driven PII API one would say where you can tap into … hashed & secured from beginning to end – never changed, never viewed, never questioned to be lost: Pseudonymization, not anonymization. If one organization needs to know about changes in your family, address location or any other personal matter, it’s the government. Why not build the damn thing and offer the service, paid! Build trust at scale. Promote & brand with a government security level badge & build the infrastructure you REALLY need (Dutch article). Let Europe deal with the cross-country regulations and exchanges, even with other continents.

We could benefit economically (automation = prosperity) as a country and as Europe. We could make this a more inclusive (more research capabilities: health for instance) & safer Europe (or even world) and fuel a base for the next generation of service industries : the one that ties all the data together to solve global issues (ex: building a digitized energy supply network across cities, rural areas, countries etc.)

The next generation of service design needs this. To work on anything that is potentially and personalized at best: health, (carbon) taxes, education, energy, and water supply, agriculture, food-waste & diets, …

If we could only trust our government with this responsibility, they would also need to be transparent about their roadmap and hire the best people, not the cheapest via a public procurement!

On the other hand, the most realistic future is this one (and mark my words): the one who will own ‘the cloud’, is the one that will host or build ‘the blockchain’. So the probability that this will be hosted on a private firms cloud is very real. We need to think about that. In terms of data privacy, as this part is sensitive to any form of capital, even fraud. Do we really want this to be in the hands of a private firm? If not, is the government capable do tackle this challenge? If not, could every citizen have the IQ and EQ and above all interest to handle their own PII or even get a decent grasp about GDPR & tracking (shit is complicated, right?)? That’s the trickiest question, isn’t it?

And what will it cost? To create, host and maintain data pods by the government. Another option is to come up with a tax for walled data and business models. Companies like Fitbit, Apple, Google and others that use data related indirectly to health and their parameters, should be taken account for their inaction on data! They only use data for their own profit, but they have access to crucial data and have the ability to leverage health with insights on a global scale! Fitbit for instance. We pay them to capture and hold on to our data and all we get is some shitty tracker and some lame dashboards. No predictions: “hey fat ass, go for a walk, you’re butt has been mistaken for a rhino’s derrière.” They have the ability to benchmark data. Apply machine learning to discover patterns tied to activity, weight, BMI and fat percentage (thus health in a way). Why don’t we force them to provide more!? Useful shit. The whole fucking product/service journey that really benefits us. On a holistic level, the longer they let me live, the probability that I’m buying new Fitbit products is relatively high, I guess. No?

Tax the inertia of Data Capital to improve society!

#2.1 Cloud is the future of Blockchain

Whoever wins the clouds, will have a huge part in blockchain. Done.
An API, for now, will do. Or are they keeping up with appearances?

#2.2 Decentralized Data Storage

Centralizing data is a thing of the past, at least… not ALL your data needs to be centralized. There are several initiatives getting the shape to store data decentralized with the possibility to connect and build temporarily data lakes or batches of training data (let’s face it, machine learning will be a huge deal in the future) depending on the timely purpose. The hardest part is to realize that the visual & communication data is currently woven into several well-known social platforms and by default thus decentralized.

Besides what we say and do, we buy stuff as well. That data is in the hands of the seller. The other types of data are PII, which is totally all over the place right now, across the web, a never to be erased trail the average person can’t erase. And the future is looking very challenging if all the things on the internet will be connected as well. Once again, in the hands of the platform, the seller or the person himself. Hard to say.

Complex shit. For security reasons, we definitely need decentralized storage and blockchain technology.
For sure. It’s only very hard to get there. The chain is secure, the locking and unlocking are just painfully slow…

#3 – Inclusive growth

Progress is life and unlocking progress can only be done by doing the stuff that helps growth. That can be economical, personal, business, etc. Data capital is the layer we’ve been missing so far to unlock the next wave of growth. With the help of (cross-)technology, mankind is finally getting aware we should focus on inclusive growth: what I personally and simply refer to as “including the up-side and solving/dealing with the down-side”, which is going to be the responsibility of the biggest companies that invest in not one aspect of AI, but all the aspects and combination of those aspects of AI.

If so, it will be hard to measure its impact and its accountability.

It will take a few more years as investments are being made in the next two years into the format of data collection, labeling and potentially storing (or not: data minimization). Those who will, will leverage the GDP of the country. Laggards won’t. Come on Belgium! Watch China, not the US.

Potentially, regulation could be set in place to help inclusive growth on a glocal level. But these big companies already understand that serving a greater cause is helping them to grow even faster. From AI to automation in software is the biggest focus in the next decade, for sure for lots of companies. The creative use of AI or the algorithm you’ll be served is one of the biggest assets to work on as a marketer in the next few years (image below – Full report here from McKinseyInteractive visualization).

AI impact on Economy

AI impact per industry

#3.1 Cost of Complexity or Opportunity by Complexity?

Building, handling & storing data capital comes with a monetary cost of which the largest part of it will be technical debt. Depending on the challenge you want to solve, you’ll need to figure out if the complexity is worth the cost and actually is a huge beneficial opportunity, or not. Both are actually to be calculated from an inclusive perspective. It actually doesn’t matter in which timeframe you put this. In the end, it depends on your willingness to invest and stick with the issue long enough until it gets solved.

The complexity of the challenge is the biggest factor in terms of investment.
Lets split that into two holistic views, shall we?

There are basically two sorts of complexity:

  1. The complexity of one
  2. The complexity of many

The shared factor for both is volume.
Allow me to explain.

To solve a very specific problem (for instance on an individual level as an extreme), the complexity is in getting to the core of the problem, meaning, you have to dig into the data and the more you’ll capture, the more you’ll analyze potential solutions. So you need more data at first to find out what data you’ll actually need to make the solution efficient. Get effective first, by sourcing and analyzing different data sources to get to the solution. After that, you can get into “efficiency modus”. To solve a more general problem (for instance a global issue), you’ll have to source a large amount of data, which probably will translate into a long time to get there. A lot of data points, at best, stretched over a longer period of time, meaning you”ll need to stock more data as well and your time to analyze will probably increase due to the handling and pre-processing of those big amounts of data.

This might sound holistic, yet it’s a very basic way to look at these kind of complexities you’ll struggle with. Deep versus width. The quadrant is yours to fill, depending on your problem you need to solve, but … this is my personal view on this: if you want to solve the complexity of many you’ll need a certain degree of knowledge of one, which makes it a volume issue, in the end, to come up with the data capital needed. On the other hand, if you dive into the complexity of one first, you can easily scale the degree of knowledge needed to get to the complexity of many. Depth always first, width always last.

Caveat: don’t forget that volume comes with side-effects like GDRP compliancy, storage capabilities, security, pre-processing, misinformation, data manipulation, … and thus resources … and thus costs. Depending on what you want to accomplish, the return can be huge and inclusive in terms of growth: mainly job creation and thus wealth creation. Up to you if you put the People Planet Profit in place.

So… should we dive into complexity and add more to the challenge, or not? Or should we reduce complexity to reduce costs and recover & focus back at the core of things? The amount of complexity in any topic can be seen as the variation in characteristics (more or less important) and the number of connections (dependencies). The added value from adding complexity to a product or service can be beneficial, yet its a challenge to keep communication & the process as simple as possible. A balance between the two is necessary to improve or increase the overall impact. Digital products and digital platforms have a very interesting place in this ecosystem, as well as for society, as well as for companies. They feed each other and thus they fuel a concept called digital transformation, as the added-value is almost at marginal cost, yet its key to keep things as transparent is simply possible for society to extract the value from these transformations.

As complexity is complex (duh) and it comes with a cost, hence the rise of digital twins. They actually have been around for quite some while in larger B2B industries and are now finding their way into more B2C related industries. The simple follow the money principle, a good pattern for next job allocation/creation to watch by the way.

In order to minimize value-decreasing complexity, you can use a complexity fingerprint. Go through the whole process and count the number of drivers per category. Note the drivers that generate 80% of the EBIT and loose/solve the other drivers. A good example can be found below:

#3.2 R&D and Beneficial Derivatives

R&D is definitely a driver that can add great amounts of value or could costs you a shit load of money. Often forgotten is the part where R&D doesn’t deliver that solution strive to find, yet gives you beneficial derivatives. Even if they are tied into a long attribution, meaning they could have incremental value in the long run and seen in an inclusive perspective.

Depending on the potential impact of the challenge investigated, the monetary investment can be justified. For instance, the great challenge of climate change as Bill Gates explains in this video. If you focus on the right driver, you can justify almost any beneficial derivatives based on the return of the cost. Want to tackle the C02 issue? You can either move a whole country to eat less meat or try to find a more efficient way to generate energy. At thousands of other options, that could be weighted based on the cost versus beneficial return. Hence why you need to think bigger, even when you try solving the smallest problem!

If there are too many variables in your drivers, you should look into feature extraction and dimensionality reduction.

#4 – Creativity & Resourcefulness

Creativity. What’s in a name. I think we all misunderstand the context of creativity. It could be being good at something artistic like painting, filming and editing cool videos, making cool brochures or being good at Photoshop + InDesign and make an amazing poster. On the other hand, resourcefulness is also a form or even an extent of creativity. Both are very interesting and offer the ability to do things differently. Even offer a unique proposition.

#4.1 Zone of proximal development

Developing yourself is a lifetime process. We all developed cognitive skills by interacting with our environment, where we are and what’s in our environment. Not who’s in our environment. That’s where this social-cultural theory comes in.

The zone of proximal development is an area of learning that occurs when a person is assisted by a teacher or peer with a higher skill set. The person learning the skill set cannot complete it without the assistance of the teacher or peer. The teacher then helps the student attain the skill the student is trying to master until the teacher is no longer needed for that task.

Lev Vygotsky stated that we can’t just look at what students are capable of doing on their own; we have to look at what they are capable of doing in a social setting. In many cases, students are able to complete a task within a group before they are able to complete it on their own. He notes that the teacher’s job is to move the child’s mind forward step-by-step (after all, teachers can’t teach complex chemical equations to first-graders). At the same time, teachers can’t teach all children equally; they must determine which students are ready for which lessons. When those children are able to do something without guidance, is within the zone. Someone who’s in the zone, is focused on the stuff he/she does unguided and this is the zone where you learn and enhance your skills. If someone is in the zone, … you can state he/she is working on something that matters to him or her. Being in the zone is the gateway to satisfaction. 

#4.2 Coaching, not teaching

Next, to the impact of social-cultural influence, the concepts of validated learning and leading by example is a good methodology to actively influence learning. Validation matters, as this skips the whole idea of teaching (telling how). Do & get feedback instantly has a HUGE impact on the growth of people. It starts with skills but in the end, the way of thinking and linking the patterns of learning are essential to a life long learning journey. It pays to understand the framework and system of the 5000 paged book instead of going through every page and memorizing the whole fucking thing.

Compare it to speed reading. This technique is somewhat similar to discover the framework or pattern of how to captivate big chunks of information and drill down to the essentials. This is the only thing that needs to be taught. The way you dissect information into chewable chunks of patterns and essential factors of information. The rest is up to the coach to help the student to navigate through the maze with his abilities to use.

I highly believe the next generation of experts (like plumbers) will be in the service economy and will be highly related to software, meaning… turning insights and in general bits into bits or atoms. Plumbers will keep on existing, guided by software. The software will be made to better plumbing and different pipework.

Other more global topics will be on the agenda, in order to tackle it on a local level. Stuff like micro and macroeconomics, intelligence interpretation, philosophy, … It will require more thinking and curating, which is a different skill set. Something schools don’t train you for. They train you for doing a job in the industries available. They don’t coach you into dissecting and dealing with information, soft skill related challenges like for instance: dealing with a crisis.

Learn to learn is the next focus of schools!
Learn to learn will have a big impact on job creation as well.
Thinking Matters. Pattern detection matters.
Future patterns will emerge, so learning to build your own learning framework matters.

Parents, youth movements, companies and especially schools should be more about coaching and connecting with other people, not teaching (only)! It’s in everyone’s interest to invest in the leverage of the closest person to you. On an emotional and intellectual level. Imagine the impact within six degrees of separation!

#4.3 Cognitive enhancement

Smart drugs and cognitive enhancers are a thing. It could be fair to say that this is the most ethical, at this moment, way to enhance your abilities. Being aware of your capabilities, feelings and how you react to specific situations, this cognitive enhancement stream could be an interesting next step to get to even a deeper state of self-awareness and steer upon enhancing your capabilities. It is a different thing though. There are chemical enhancements on the market. Or you could train yourself to focus on enhancing certain parts of your cognitive skills if you’re aware. If you’re able to use both, THAT could become very interesting!

#4.4 Finding a state of flow

Being creative is a verb. Being creative is also a state of doing, but getting into a flow where you actually are doing creative work is very hard to get into. The challenge and deadline (work) get you in a state of anxiety unless you can perform under stress. Yet… the best creative work comes out of a state of boredom and a next logical state of doing: experimenting. The other end is getting bored and turn into a state of laziness.

How you function, how you deal with your surroundings, how you deal with the information, how you deal with expectations, how you deal with deadlines, how you deal with experimentation (which could be a process for you), … this awareness and thus self-awareness kind of intelligence really matters to get in a flow of doing, stop focusing on shit stuff but try to get and keep yourself laser-focussed and logically loose track of time and thus be satisfied & happy in the long run.

Keeping a state of flow is the hardest thing to do. If you’re able to learn by yourself, you’re in the zone, but the challenge must be big enough to keep you in the zone for a decent period of time. That’s why solving complex problems is one of the most satisfying things to do in life. No wonder start-ups are so hot. No wonder everyone likes to spend more time doing a specific hobby than go to work. No wonder we like to hang out with friends and have fun. It’s satisfying. It’s meaningful.

TL;DR – Intelligence is a network

The concept of Intelligence is a hard thing to describe. It is a very subjective view, depending on several factors. It could be different for any one of us, yet it has one thing in common. Nothing is linear, nothing is homogeneous. All its nodes are connected and heterogeneous. These nodes are a connected bunch of people, technology and information in any format, but… here’s the catch … connected nodes placed into context. A web of context. A network of contextual communication, tools, and human connections.

We already have the concept of Intelligence active in some countries in the form of a bureau, yet… it’s not only for fighting fraud or crime. That would be stupid, right? All that potential to see beyond the economic or top topical topics. The future is a connected one, for sure. The only factor that’s holding us back to connect all the information, technical systems, … is mankind itself.

We’re scared of what we’re capable of.
We’re scared shitless.

I must confess. It’s very hard to explain how I think about Intelligence in a holistic perspective but this is the baseline, for me: Unlocking the ability to be Creative with Intelligence is unlocking unknown potential … and I’m personally very afraid we won’t ever unlock ours!

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