Analytics and Insights, Insights, machine intelligence, Uncategorized

The relationship between humans, machines and markets

Technology has increased access to information, which in turn has made the world more similar on a macro and sub-macro level. However, despite increased similarity, research shows business models are rarely horizontal, emphasizing the importance of micro-level strategic consideration. Companies routinely enter new markets relying on knowledge of how their industry works and the competencies that led to success in their home markets, while not being cognizant of granular details that can make the difference between success and failure in a new market. Only through machine driven intelligence can companies address the level of detail needed in a scalable and fast manner to remain competitive.


Research from Harvard Business Review reveal that despite an increase in information and globalization, business models are generally not horizontally scalable from one country to the next.

Research from Harvard Business Review show that despite more access information and networks through globalization, business models , 86% of the time, do not work from one country to the next.


Relying on simple explanations for complex phenomena is a risk - 3% of markets have a negative correlation to one another. Therefore organizations need to contextualize marketplace characteristics and avoid addressing only one variable at a time.

Relying on simple explanations for complex phenomena is a risk – 3% of markets have a negative correlation to one another. Therefore, organizations need to contextualize marketplace characteristics and avoid simplistic linear or binary based KPIs.

Pos Correlation HBR

The world is complex, but computational power is getting more robust and cheaper, while machines are getting smarter. Despite that only 11% of country-to-country profitability had a positive relationship, asset rich large organizations with large networks and infrastructure are uniquely positioned to exploit this if they learn to understand their assets in higher resolution. In most cases this means letting go of long held beliefs and how things used to be done, in addition to being agnostic how to make money.

Amount of shares traded on the NYSE. This data, can be used as an analog to show how quickly information and connectivity is growing exponentially.

The amount of shares traded on the NYSE is shown as an analog to illustrate how quickly information is growing exponentially every year. Despite advancement in communication technology, companies are no better at market access.

Furthermore, machine intelligence and information has led to the rapidly diminishing value of expertise, in addition to eroding the value of information. The level of expertise needed to out-run or beat machine intelligence has exponentially increased every year. Over the next one to two years the most successful companies will come to accept the burden of proof has switched from technologies and A.I. to human expertise. Furthermore, machines will come to reframe what business and strategy means. Business expertise in the future will be the ability to synthesize and explore data sets and create options using augmented intelligence – not being an expert on a subject per se. The game changers will be those that have the fastest “information to action” at scale.

A residual of that characteristic makes a “good” or “ok” decision’s value exponentially highest in the beginning – and often times much more valuable than a perfect decision. To address this trend, organizations will need to focus on developing process and internal communication that foster faster “information-to-action” opportunity cost transaction times, similar to how traders look at financial markets. Those margins of competitive edge will continue to shrink, but will become exponentially more valuable.

Speed to market and competitive advantage

How are businesses harnessing AI and other technologies to lead the way ?

Studies show experts consistently fail at forecasting and traditionally perform worse than random guessing in businesses as diverse as medicine, real-estate valuation, and political elections. This is because traditionally people weight experiences and information in very biased ways. In the knowledge economy this is detrimental to strategy and business decisions.

Working with machines enables businesses to learn and quantify connections and influence in a way humans cannot. Rarely is an issue isolated to the confines of a specific domain, and part of Walmart’s analytics strategy is to focus on key variables in the context of other variables that are connected. This can be done in extremely high resolution by taking a machine based approach to mine disparate data sets, which ultimately allows for flexibility and higher resolution KPIs to make business decisions with.

What are the effects of digital disintermediation and the sharing economy on productivity growth?

Machines have increased humans ability to synthesize multiple information streams simultaneously, while connecting communication, this could lead to a higher utility on assets. It’s likely that businesses in the future will have to be more focused on opportunity cost and re-imagine asset allocation with increased competition due to lower barriers on entry. Inherently intelligence and insights is about decisions.  A residual of that characteristic makes a good or ok decision’s value exponentially highest in the beginning – and often times more valuable than a perfect decision. To address this trend, organizations need to focus on developing process and internal communication that foster faster “information-to-action” transaction times, much like how traders look at financial markets.

Is this the beginning of the end?

Frameworks driven by machines will allow humans to focus on more meaningful and creative strategies that cut through noise to find what variables that can actually be controlled, mitigating superficial processes and problems. As a result, it is the end for people and companies that rely on information and routine for work. And the beginning for those that can solve abstract problems with creative and unorthodox thinking within tight margins. Those that do so will also be able to scale those skills globally with advancements in communication technology and the sharing economy, which will speed up liquidity  on hard and knowledge-based assets considerably.



Nobody Cares

The first three rules of communications.

Nobody cares.
Nobody cares.
Nobody cares.

It’s time marketers realize people are exposed so much media noise and syntax that to stand out, it’s time to start just being direct and have a good product. Talking a lot and being “social” is most of the time fake and annoying. No one cares outside of something that is 98% relevant or actually extraordinary. Now’s the time to only be extraordinary.

– CT


Funneling Your Brand

Pinterest and  Instagram.

Both simple. At most, do a couple of things well. Proper UI design, much like popular mobile apps, are following this trend.

How to communicate this as the company grows? An XY chart between brand/Product segregation and or consolidation. Then funneling down all that you do, to a few words such as “it just works”. Take note from minimalist art.



Much focus is on generating the most content possible without questioning the impact of the syntax/semantics/meaning. Simply, two words with well thought out connotation will have more sustainability than one thousand with a bad frame.

The reason why campaigns are successful is because they are empathetic, built on identity and play into our innate cognitive make up. Not because of the channel it was on.

Data Science, Public Affairs and Communications, Uncategorized

NLP: Value Greater than just Positive and Negative Sentiment.

Positive (P) and negative (N) sentiment are just transmitting two results based on many other variables. This in itself contains little knowledge. For creating strategy, it can only bench mark after the fact.

Inherently the use of data is to make better decisions for the future. Production is expensive and time consuming. So is deductive reasoning.  The main goals should be to move away from adjusting to the end reaction and migrate to a predictive model. Look at the context in higher resolution i.e. control for  variables which lead to PN sentiment.

Here are some suggestions

  • Time of day
  • Medium (Twitter,Facebook ,Blogs, Mainstream news)
  • Comments PN sentiment
  • New dissemination to comment count (time, PN)

If you are advanced, use NLP tools that allow for custom taxonomies – within the NLP,  to create rules on varibels like types of framing. On a medium level this is very good at prediction, but more on that later.