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

Open Sourcing Data

My Thoughts on Instacart’s move to open source their data and the advantages that can bring to a firm…
 
1) It keeps a company on their toes and running fast. If you give away stuff you value, you constantly have to innovate systemically to stay relevant. The more companies force these modifiers through operational design, the more competitive they will be.
 
2) Having an external community help in developing novel approaches to analysis and scaling that the organization did not think of itself. Kaggle, recently acquired by Google, has demonstrated this with great success, as has the algorithm development community (e.g. Torch, Tensorflow, h2o.ai, SciKit).
 
3) Related to the previous point, cheaper and more efficient R&D and insights. Most business intelligence isn’t acted on, despite an estimated 20 billion dollars being spent on it per year. If you open source data analysis and insights gathering, it’s possible to only pay for what you find valuable. While these operations will never reach 100% utility, it would probably be better than it is now, and ultimately lead to lower prices for consumers.
 
4) More efficiency, information sharing, and transparency make a company a more attractive business partner, requiring less “guesswork” on how your offerings align with each other. It’s possible this opens a company up to unorthodox partnerships that generate new revenue streams or solutions.
 
In closing, I’d like to invite you to a thought experiment. And I understand this is a contentious point, which is open for debate. But unless you were operating a hedge fund, what advantage would there be in knowing a competitors sale e.g. Amazon and Target’s sales numbers and products they sold – beyond it being interesting? How would knowing this information help the company you work for take market share or counter their initiatives from doing the same? It’s not obvious. Companies such as Walmart, Amazon, and Target all have different reputations, regional and cultural affinities, as well as combinations of technologies, supply chains, management styles and distribution mechanisms. Most organizations are far from 100% optimal within their internal processes and decision making. At what point does focusing on another company out run the advantage of focusing intensely on making you companies asset allocation more efficient? What is the optimal XY graph here? Due to the complexity of each organization, it’s likely neither large firms such as Walmart, Target or Amazon has any mechanism to operationalize each other information – even if they each had all of it. The only thing you can be 100% confident about is by focusing on the competition is that it’s taking time away from making your own company better. Comments welcome.
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Analytics and Insights, Change Management, Corporate Culture, Data Science, machine intelligence

Things to consider when driving machine intelligence within 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 is unrealistic. The reality is that working with AI’s on simple routine tasks will drive better decision making systemically as people become comfortable with the questions and strategies that are now possible. And hopefully, it will also build the foundation for more of those eureka(!) moments. 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 when drive machine intelligence at a large organization, as well as what is possible.

  • Algorithm Aversion — humans are more willing to accept the flawed human judgment. However, people are very judgmental if a machine makes a mistake – even within the lowest margin or error. 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. For further elaboration on making better predictions, the book Superforecasting is a must read.
  • Silos! The value of keeping your data/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. If this is possible build what I call diplomatic back channels where teams or analysts can sure data with each other.
  • Build a culture of capacity.  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 a bad thing to seek external help, but if this is how most of your work is done and where you go to get new ideas you’ll have problems. As a residual,  the organization will fail to build strategic and technological muscle. And it’s likely to create a culture which emphasizes generalists, not novel technical thinkers in leadership roles. In turn, you end up with an environment where technology is appropriated at legacy processes & thinking. Not the other way around as it needs to be if you want to stay relevant. Avoid the temptation to outsource everything because nothing seems to be going anywhere right away. That 100-page power point deck from your consultant is only going to help in the most superficial of ways if you don’t have the infrastructure to drive the suggested outputs.
  • Organizations need to evolve their processes in step with technology. Not fit new technologies to old/outdated processes. If so “money” is 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 standardize 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.
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Analytics and Insights, Change Management, Corporate Culture, Data Science, Insights, machine intelligence

Next Generation KPIs

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 linear 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 networks.  The advantages in understanding at a much deeper level are not possible to do with standard KPIs like volume, publish count and sentiment over time. Through mapping out the 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 and connect to other entities within the specific domain. In turn, this creates an analog of the physical world with stunning accuracy –  since more information is reported online every day. For example,  using to online news articles and Bit.ly link data, I found articles with less centrality (based on the linguistic similarity of the aggregated on-topic news article) to their domain, which denote variables being left out (of the article), typically got shared the most on social channels. In short, articles that were narrower in focus, and therefore less representative of the broader domain, tended to be shared… This is just the tip of the iceberg. 

centrality, bias and social sharing - @chandlertwilson

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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.”

CT

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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.

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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 http://ow.ly/8w2Gs. Specifically alarming was that the Parliament, which is supposed to be the extension of the people, had the lowest interest rate.

googletrends

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

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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 (http://goo.gl/UIwUv). 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 http://goo.gl/W8Dxj) . Ultimately, the perspective that is essential for good decision making is affected (McKinsey; Kahneman and Klein article on this http://goo.gl/0bFcC).  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

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