Analytics and Insights, Change Management, Corporate Culture, Data Science, machine intelligence

Things to consider when scaling machine intelligence in a large organization

While modern day A.I. or machine intelligence (MI) hype revolves around big ”eureka” moments and broad scale “disruption”, expecting these events to occur regularly or a scalable manner is unrealistic. The reality is that working with AI’s on simple or routine tasks will drive better decision making systemically, and as people become comfortable with the questions and strategies that are now possible to ask. And hopefully, it will also build the foundation for those eureka(!) moments downstream. Regardless, technological instruments allow workers to process information at a faster rate while increasing the precision of understanding. Enabling more complex and accurate strategies. In short, it’s s now possible to do in hours what it once took weeks to do. Below are a few things I’ve found helpful to think about  as one tries to drive machine intelligence in a large organization.

Facts and Thoughts:

  • Only 21% of managers who implement strategy actually test how KPI’s actually link back to organizational performance, and many of those tested found their early assumptions flawed.
  • Companies in the top third of data-driven decision making are 5% more productive and 6% more profitable.
  • Decisions generated by simple algorithms are often more accurate than those made by experts, even when the experts have access to more information than the formulas use.
  • Algorithm aversion— humans are more willing to accept flawed human judgment. However, people are very judgmental if a machine makes a mistake – even within the lowest margin or error.
  • Silos  — The value of keeping information a secret as a competitive edge does not outrun the value of potential innovation or insights if data is liberated within the broader organization.
  • Understand the solutions most problems and opportunities in the modern business climate are centered around a combination of creativity, data or technology. To find opportunity or address problems the organization will need to quickly move from linear analytics or business intelligence to weighting/graphing of disparate data sets with artificial intelligence. Machines are far less biased than people and better at weighting the relationships between desperate events, objects, and data sets. Even simple statistics have been shown to outperform even the most experienced analyst.
  • Develop a culture of skill and capacity. The majority of abstract, complex and pressing work is typically outsourced, not built internally.  Managers are willing to spend 42 percent more on the outside competitor’s ideas. “This is why consultants get hired,” Leigh Thompson, a professor of management and organizations at the Kellogg School says. “We bring in outside people to tell us something that we already know,” because it paradoxically means all the wannabe “winners” in the team can avoid losing face. It’s not so much that it is a bad thing to seek external help, but in reality, this translates into a lack of strategic capacity internally, and as a residual,  the organization will loose the opportunity to build those thinking and technological muscles. In short, avoid the temptation to outsource everything because nothing seems to be going anywhere right away. That 100 page power point deck isn’t going to help. Also note that those that generally push for everything to be outsourced are probably doing so to limit exposure of their lacking skills sets or vision in the space, so be skeptical of managers who always want to take this approach opposed to building a solid  foundation in MI. This is the derivative of a strong management culture which emphasizes generalists, not novel, unorthodox or technical thinkers in leadership roles. Research not only shows general managerial styles are less successful in technical or data-driven tasks contrast to technical competence, (and most work is data or technology related these days) but they also foster a culture where exercising intellectual integrity or novelty on subject matters are liability for one’s career. Further, it leads to an environment where technology is appropriated at legacy processes & thinking i.e. not as efficient nor effective. Organizations need to evolve their processes in step with technology. Not the other way around. In return there is “money” left on the table that isn’t even considered or known due to lack of domain depth, which creates hierarchical bottlenecks that inhibit innovation and merit-based thinking. As a result, the talented people in your organization will defect, leaving mediocre skill sets to run general operations which standardizes low common denominator outputs. As well as not maximize the prior investment in capabilities or assets which may have offered a competitive edge had they been seen through sufficiency.
  • Be more introspective and strategic with assets and cash. By integrating sensors (Internet of things), more sophisticated tracking and the understanding and integration of physical and cash assets, organizations have the opportunity to maximize the utility of its assets. In turn, this would foster a “trading desk mentality” where companies would operate more like a hedge fund on its own cash flow and purchases, as well as being mindful of foreign exchange rates & commodities markets. This approach could also take advantage of assets such as shelf space and storage – much like AirBNB did with extra home space. Generally, over the last 10 years, companies with multiple revenue streams and consumer offerings such as Apple and Amazon have weathered the storm better than companies whose focus remains narrow.
  • Create a data markets or indices that your suppliers/vendors can opt in.  In doing so it helps compress prices by sharing the burden of costs associated with market research and or supply chains. Furthermore, how to create value from data quickly will perhaps be the more pressing issue for large organizations. This will inherently drive the need for all of these organizations to be more strategic with their data and technology, not like a commodity to be hoarded. Remember data is only valuable if acted on.
  • Create a social graph of your organization to understand how information becomes action. By leveraging machines to analyze internal information and asset flows & allocation within a graph-based approach, are able to systemize and automate many routine processes such as meetings or approval processes that humans currently do. Furthermore intelligence agents analyzing the graph can bring together people working on disparate projects.
Analytics and Insights, Change Management, Corporate Culture, Data Science, Insights, machine intelligence

Next generation KPI’s

Recently I’ve been thinking of ways to detect bias as well as look into what makes people share. Yes understanding dynamics and trends over time, like the chart below (Topsy is a great simple, free easy tool to get basic Twitter trends), can be helpful – especially with forecasting. None the less they reach their limits when we want to look for deeper meaning  – say at the cognitive or “information flow” level.  

MN Vikings Teddy Bridgewater Adrian Peters Twitter mentions

Enter coordinates and networks.  The advantages are not possible to do with a tradition KPI’s like volume over time. Through mapping out the a network based on entities, extracted locations and similar text and language characteristics it’s possible to map coordinates of how a  headlines, entities or article exists in space within the specific domain. And this happens to deadly representative of the physical world.. especially since more information is reported online everyday.

When put together (shown below), I found a way to detect bias. Using to online news articles and link data, I found articles with less centrality to their domain, which denote variables being left out (of the article), typically got shared the most on social channels. In short some bias = more sharing… Interesting – although more research is needed.

centrality, bias and social sharing - @chandlertwilson

Analytics and Insights, Change Management, Corporate Culture

What data really is

It took a while to come to this conclusion. When I first started consulting some years back, I thought it was about insights and social data. Now people are onto “insights”, “social media” and “big data” (ad nauseam). What consultants and practitioners should really be trying to do is help organizations make more contextual decisions, faster.

You may note that I used to call this blog Intelligence, Communications, Change. The order is not coincidental.

Its reasoning goes as such:

  1. Unfortunately, organizations are not set up to handle the majority of intelligence or “insights” that we may find when mining the web or other data sources.
  2. I gather intelligence, communicate and then try to change a given instance or situation. Always changing and making decision more accurately and faster.
  3. To have the most benefit from analytics and data means turning the insights or analytics unit into an ongoing change management program. With the ultimate goal being to set up the organization to think contextually, as well as be ever cognizant of the “perfect is the enemy of good” principle – an “ok decision” that is fast typically has more value than a perfect one down the line.

Never before has there been so much information available to us. Every day there are 3.6 trillion words created on email and social media — the equivalent of 36 million books. Today more data crosses the internet every second than was stored in the entire internet just 20 years ago. The bigger risk is being tame and will put you in the doghouse faster than any sort fallout a thoughtful experiment would have. Over a long enough time, eventually, people and companies can piece together what ever competitive advantage a person or company may have. Those concerned with protecting IP or “trade secrets” will be irrelevant and miss opportunities simply because of the rapidly deteriorating value they perceive in their IP.

As the great basketball coach John Wooden said:

“If you’re not making mistakes, then you’re not doing anything. I’m positive that a doer makes mistakes.”


Analytics and Insights, Brussels, Change Management, Data Science, European Parliament, European Union, Global Politics, Public Affairs and Communications, Social Media

Barroso and Kerry Analysis

I decided to break down the events of John Kerry visit to Jose Barroso. The reactions are not drastic as I would have thought considering GMO’s and IP are two issues that people care about on both continents, and the Free Trade Agreement is making some actual headway. We will have more on this in the future, but here are small bits of data for starters

map barroso&kerry
We see that the reporting on the John Kerry and Baroso event is reported on in Brussels 33 times – much more than any other area except for the entire US. Below we see events with organizations and people that both of them together are tied to.
Social Network Barroso & Kerry

Cross referencing what each leader said within the context above is where it gets interesting. Questions to ask: Who were their targets? Did they attain any impact?

Twitter overlap Kerry Barroso

When I first arrived to Brussels I was always amazed at how disconnected D.C was from Brussels with that I decided to look at who was following who. We see that only 1.1% follow both. In short they are disconnected networks. For Comparison sake I also did Obama V Barroso but since Obama has over 30 million follower, the tools I was using at the moment couldn’t process such large amounts of data.

Analytics and Insights, Brussels, Change Management, Corporate Culture, European Parliament, European Union

The EU: Hooking up with Technology

Jose is trying to learn how to get a date . There’s a conference at a local hotel on how to pick up women. On his way to the room, Jose’ encounters two doors. One  leads to the conference taught by men on how to pick up girls. The other door has a  sign that says “Successful single women’s conference. Please join us for a drink,  anyone is welcome”.  Jose chooses the first door as he had planned, and continues learning about how to pick up women. The EU relationship with using technology is  like Jose’s approach to trying to pick up women, hesitation and unwillingness to  adapt in real-time, to the peril of the end goal – i.e. institutional.

One day I was talking about online media monitoring to the  institutions “social media expert”.  I was asked  “why do we need to understand what people are saying about us?” I was shocked and had no answer except to point out Interest in the EU has gone down every year since 2004 Specifically alarming was that the Parliament, which is supposed to be the extension of the people, had the lowest interest rate.


Blue – European Union
Red – European Commission
Yellow – European Parliament

Now having worked in US politics,  a good place to start making a more legitimate government, is being more representative of constituents..and understanding what people are saying about you allows you to create better policies and messages that can help engage people, and perhaps increase the voting rates.

Both EU firms and institutions spend way too much time discussing what technology such as social media is, or what it means, but never act. For example Friends of Europe just released a paper about social media . Frankly I found it pointless, uninteresting, and six years too late.

In the globalized future hesitation is dead, improvisation is king, and competition will be fierce…

Thinking about the “social media experts” statement further, I concluded it wasn’t that  online monitoring wasn’t useful for their situation, but it’s use would have created a real-time approach. This is  the antithesis of institutional process Europe is way too familiar and comfortable with.  And incentive for the people working in the institutions wasn’t there either.

In the USA, competition has led to campaigns and politics becoming a  science. And voting rates + political involvement have gone up. 

The 2012 campaigns featured natural language processing, text mining, sentiment analysis, and data scientists. These technologies will marginalize every medium and word. There was  no room for “educated guessing”. This is efficient, saves time and  money, plus leaves the politicians to focus on empathizing more with the electorate. Forward to the EU. The system is not competitive. The money is provided by the public, and the European Commission is in charge of mobilizing people in a non-political way, which is inherently very, very difficult.

The future will embrace non-understanding, chaos and real-time data, you don’t get the luxury of writing a 10,000 word strategy paper. At present the EU mindset is not equipped to handle this transition. It  must remember if it wants to  hang out with future technology, it has to first quit talking, and ask it out on a date.

Ciao, CT

Change Management, Corporate Culture, Insights, Public Affairs and Communications

Making “Corporate Culture” without the parentheses.

Much of this post was taken from a comment I wrote on LinkedIn in which I talked about vision and corporate culture ( While a lot has been said of “corporate culture”, most of the time this is under the guise of  CSR reforms and other similar bullshit jargon. Being a data man who relies on sound principles, I fully agree that an internal focus from leadership is essential. For change that “takes” there needs to be an internal process and protocol set that reinforces the change in addition to clear incentives and goals. In other words, make it so you can’t get out of it. It’s also important to remember that setting out and visualizing a corporate culture is often times different than building a sustainable and profitable culture that makes bleeding edge solutions and products that people and companies want to buy.

What I find painfully obvious, and a huge risk to building a sustainable corporate culture that breeds future relevance, is that lack of perspective and group thinking tend to build off one another. These are the banes of any culture seeking to be relevant and innovative in the highly competitive, interconnected, but often times different enough world.

Think about this: The majority of leaders and directors come from the same schools, have the same frame of mind and generally have had an upward linear career path that was safe. They went to the best schools, got good grades and were very smart. While intelligence and capability are no doubt quality prerequisites and parts of the solution, companies will  inherently lack “grit” and suffer opportunity loss for the intellectual capital gained from it (Good article on grit . Ultimately, the perspective that is essential for good decision making is affected (McKinsey; Kahneman and Klein article on this  I find grit and perspective to be the main bottle necks in building a company culture that embraces disruption and leverages it fast, the holy grail of most of this research.  So with that I challenge leaders to step up and get real with developing “corporate culture” legitimately (diverse/non group think environment) so it isn’t in parentheses as it often is.

Bring in fuck-ups so you don’t fuck up. People you might not be comfortable with and you don’t get right away. They might not have gone to the right schools, and might be a bit crazy, but have succeeded in unique ways that make you question your job, skills and thinking. You might find you are not relevant. This is obviously scary to people, but it’s necessary to motivate them into learning new skills, in addition to creating a “check down” in the corporate process that maintains innovation and relevance.

Thoughts – SWOT away.. CT

Analytics and Insights, Brussels, Change Management, Corporate Culture, Data Science, European Parliament, European Union, Insights, Public Affairs and Communications

Communications as Productivity

By looking at Communications in terms of production and variables, you will save millions. By not understanding variables in communications, you will loose millions and time. Gone where communication firms can pass  Bullshit. Now it’s far from an art form with metrics. Clients should expect decision making based on sound research, in addition to  web analytics and online monitoring data.

The goal: Create the most productive syntax, which could be words, photos,video or interactive digital content. Not the most view and clicks


Output time/date to channel dissemination, and how much time  that takes. It shows language adoption, which is how you win. There’s a saying politics, “Win the language battle, win the war”.  Resonance is a good KPI for knowing if the framing has retention, thus productivity.

Its can also be useful to reverse engineer a past event’s content and framing with machine learning to compare and contrast. Again don’t focus too much on “click and looks”. You might see exponential gain which perhaps are linked to offline events. That way you can also map correlation. I tend to see this pattern a lot working in politics. This reiterates the massive amount of information available on-line by listening.

More Tricks:

Use best practices in cognitive ICT, psychology,linguistics and behavior economics. A real expert will know captology, sentiment analysis and gamification, and not neglect fundamental and technical analysis.

When you understand the basic variables, you see that  each enhances or destroys productivity.

If the person you hired  doesn’t understand what I’m talking about, the strategy will loose productivity. Ask yourself, why we are paying 200-300 plus per hour? Educated guessing is over. The data doesn’t lie.