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, Brussels, Corporate Culture, Data Science, European Parliament, European Union, Global Politics, Insights, Linguistics, Lobbying, machine intelligence, Politics, Public Affairs and Communications, U.S. Politics

OSINT One. Experts Zero.

Our traditional institutions, leaders, and experts have shown to be incapable of understanding and accounting for the multi – dimensionality and connectivity of systems and events. The rise of the far-right parties in Europe. The disillusionment of European Parliament elections as evidenced by voter turnout in 2009 and 2014 (despite spending more money than ever), the Brexit and now the election of Donald Trump as president of the United States of America.  In short, there is little reason to trust experts without multiple data streams to contextualize and back up their hypothesis.

How could experts get it wrong? Frankly, it’s time to shift out of the conventional ways that we try to make sense of events in the political, market and business domain. The first variable is reimagining information from a  cognitive linguistic  standpoint. Probably the most neglected area in all of business and politics – at least within the mainstream. The basic idea?  Words have meaning. Meaning generates beliefs. And beliefs create outcomes, which in turn can be quantified. The explosion of mass media, followed by identity driven media, followed by social media, and alternative media. We are at the mercy of media systems that frame our reality. If you doubt this, reference the charts below. Google trends is deadly accurate in illustrating what is on people’s mind the most, bad or good, wins – at least when it comes to U.S. presidential elections. The saying bad press is good press is quantified here. As is George Lakoff’s thinking on framing and repetition (Google search trends can be used to easily see which frame is winning BTW ).

google-search-trends-presidential-candidates-2004-to-present

Google search trends of Democrat and Republican presidential candidates going back from 2004 to 2016. The candidate with the highest search volume won in all political races.

Social media and news mentions of key 2016 presidential candidates. The query used was (

Social media and news mentions of key 2016 presidential candidates per hour within the  query used “Donald Trump” OR “Hillary Clinton” OR hashtags #ElectionDay OR #Election2016.

google-search-trends-nicolas-sarkozy-franc%cc%a7ois-fillon-alain-juppe

Google trends wasn’t caught off guard by François Fillon’s win over Nicolas Sarkozy and Alain Juppé in the French Republican primaries . It was closer than the polls expected all along.

Within this system, there is little reason to challenge one’s beliefs and almost nothing forcing anyone to question their own. Institutions and old media systems used to be able to bottleneck this, they were the only one with a soap box and information was reasonably slow enough. To outthink current systems there is a need for a combination of sharper thinking, being able to quantify unorthodox data such as open source intelligence (OSINT) and creativity that traditional systems of measurement and strategy lack. Business, markets, and people strive, to a fault, for simple, linear and binary solutions or answers. Unfortunately, complex systems i.e. the world we live in doesn’t dashboard into nice simple charts like the one below.  The root causes of issues are ignored, untested, nor contextualized, which creates only superficial understanding on what affects business initiatives.

linear-dashboard

A nice linear BI chart above. Unfortunately, the world is much more complex and connected – like the network graph below, which clusters together new media that covered Hillary Clinton and Donald Trump .

network-graph-of-news-media-about-donald-trump-hillary-clinton

I know this may feel like a reach in terms of how all that is mentioned is connected so more on OSINT, data, framing, information, outcomes, and markets to come.

Cheers, Chandler

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Analytics and Insights, Brussels, Corporate Culture, Data Science, European Parliament, European Union, Global Politics, Insights, Linguistics, Public Affairs and Communications

The Brexit

While investors were in shock, open source signals such as Google trends pointed to “Leave” being predominate the majority of the time, illustrating expert and market biases.  Perhaps they should work on how to integrate these unconventional data streams better (sorry couldn’t help it).  The UK’s decision to exit from the EU is part of a larger global phenomena that could have been understood better with open source, not just market, data.

Leave Remain Google Trends

Google Search Trends on “Leave” or “Remain”

The world is growing more complex. Information is moving faster. Humans were not evolved to retain or understand this mass output of (dis)information in any logical way. As a response, a retreat to simple explanations and self-censorship towards new ideas, that might challenge one’s frame, are ignored and become the norm. Populist decisions are made and embraced, often times reactionary towards the establishment or elite. Multi-national corporations and elites will need to step outside of their bubble and take note of nationalist, albeit sometimes isolationist, such as Donald Trump, Bernie Sanders, President Erdogan of Turkey, Marie Le Pen’s Front National of France, Boris Johnson – Former Mayor of London and Brexit backer (good chance he take David Cameron’s place), Germany’s AFD and the 5 star movement in Italy gain in both popularity and power.

New media online coverage tagging of Leave V Remain

News media dynamics of “Leave” (Red) and “Remain”. Grey and black have no overlying preference. In addition to Google Trends, the majority of the media coverage focused on “Leave” over “Remain”. Those polling should have a look at framing effects to accurately conclude outcomes.

 

Red = People associated with Leave. Blue = people associated with Remain.

In addition to the more media coverage, people were associated more with the leave campaign, which is an advantage. During a political campaign choices and policy lines are anything but logical, they tend to fall on emotional lines, so it’s important that institutional communications have a noticeable figurehead, especially in the age of media. It says something when the top people that are associated with remain are Barak Obama, Janet Yellen and Christine Lagarde. Note that David Cameron is more central with leave.  None the less the pleas by political outsiders and institutions such as the IMF and World Bank for the UK to remain in the EU, potentially caused damage to the “Remain” campaign. UK voters seemed to not want to hear from foreign political elites on the matter. This is illustrated by the connection and proximity of the “Obama Red Cluster” to the French right wing Forest Green cluster (and the results) below. The “Brexit” could lend credence to the possibility of EU exit contagion. There are very real forces in France (led by the Front National) and Italy (led by the 5 Star movement (who just won big in elections) that are driving hard for succession from the EU and or potentially the Eurozone.

This network show the Brexit domain the day prior to the Leave result.

This network shows the Brexit domain the day prior to the Leave result.

 

Ramifications:

  • Seeking shelter from volatility, banks (especially European ones) are fleeing to the gold market. While this is to be expected, dividend based stock, as well as oil, would be attractive to those seeking stability as well.
  • Thursday’s referendum sent global markets into turmoil. The pound plunged by a record and the euro slid by the most since it was introduced in 1999. Historically, the British Pound reached an all-time high of 2.86 in December of 1957 and a record low of 1.05 in February of 1985.
  • Don’t count on US interest rate hikes. Yellen has expressed concern for global volatility on multiple occasions. The Brexit just added to that. The Bank of England could follow the US Fed and drop interest rates on the GBP to account for market uncertainty.
  • If aggressive, European uncertainty could be an opportunity for US companies to gain on European competitors. Due to the somber mood within Europe, companies could either be more conservative with investment, leaving them vulnerable.
  • Alternatively, the Brexit may trigger more aggressive U.S. or global expansion by European Companies while Brexit ramifications are further understood.
  • The political takeaway is the remain campaign was relatively sterile, having no figurehead or clear policy issues directly relating back to the EU. This was reflected by the diversity in associated search terms related to the “Leave” campaign, in addition to Angela Merkel, not an EU leader such as European Commission President Jean-Claude Junker, once again as being seen as the defacto voice of Europe.
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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, Brussels, Data Science, European Union, Global Politics, Insights, Politics, Public Affairs and Communications, Social Media, U.S. Politics

The German election and EU political communications

For this post, I decided to remain old school and mainly rely on search data. It’s pretty basic, but typically offers a great view of what people are interested in. Google’s market share is around 90% in Europe and it’s the most visited site in the world. In my opinion, Google Trends is the largest focus group in the world.

First I looked at the overall interest in Germany between Angela Merkel and Peer Steinbrück, as well as their political affiliations – the CDU and PSD. Initially, I was curious as to how party identity interest compared to interest in the politician. To anchor this chart I did the same with US presidential campaign (the chart below). I have a hunch, and the data seems to be telling me thus far, that the more media-oriented politics becomes (along with everything else in the world), the more important celebrity, authenticity, and individuality becomes. Take a look at this recent brand analysis done by Forbes. Chris Christie wins, having the highest approval rating of over 3,500 “brands” according to BAV (awesome company) at 78%. For those that don’t know, Christie is probably the most straight forward tell-it-like-it-is politician in the country.

So what can we learn from the Google search interest shown below?

Google search data of the 2013 German elections

Google search data of the 2013 German elections

Obama, McCain, Romney, Democrat and Republican search interest.

Obama, McCain, Romney, Democrat and Republican search interest.

  • Politics is still about sheer volume and name recognition. For those that think being novel and unique achieves victory over blasting away nonstop in a strategically framed and coordinated way, think again.  People tune out if they aren’t interested. Irrelevance is almost always worse than bad PR  or sentiment (excluding a case like Anthony Weiner). You simply don’t win if you don’t interest people. If people aren’t talking about you, you’re not interesting. Merkel had more search interest than Steinbrück and over the course of the year probably got 10,000 times more airtime, both good and bad, due to her large role in the euro crisis.  In short, repetition is king.
  • Framing and consistent language strategy is vital. Volume can be shown to equate with recognition of a person, but this can easily enough be analogized to a policy or issue. Give me a choice between a clever social media strategy or consistent language strategy, meaning all the key issues are repeated by the party and coordinated as much as possible, and I’ll take the language strategy any day. It’s amazing how just being consistent in political communications is overlooked by companies and political leaders in Europe. Social media tends to be a framing conduit, not the reason people mobilize or have opinions.
  • The world is growing ever more connected. Look at how global the reporting of the German election was. Obviously, its importance was higher due to Germany’s rising influence, but none the less the amount of sources from all over the world is impressive. A note for the upcoming EU elections: don’t forget to target the USA and other regions to influence specific regions in Europe. A German constituent might read about a policy from the Financial Times, a Frenchman the Wall Street Journal or an American based in Brussels, who knows Europeans who can vote, Bloomberg.

Location of sources reporting on German elections events/happeningsI decided to throw in Twitter market share of the candidates from August 21st to September 21st, the day prior to elections.  I found it interesting to see how closely Belgium and the United State reflect Germany, probably because these countries are looking at the elections from more of a spectator view. Meanwhile, southern Europe, which had a vested interest in the election, was pretty much aligned. France, Spain, and Italy seem to report a bit more, and in a similar way, on Merkel – probably due to sharing the same media sources. Unfortunately, I don’t have the time to look into this pattern too much at the moment, but it’s something I’ll continue to think about in the future.

Market-share of Twitter for Germany Election candidates: USA,DE,BE

Market-share of Twitter for Germany Election candidates: USA,DE,BE,PT,FR,ES,IT

DE Elections Twitter market share south EU

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Analytics and Insights, Brussels, Data Science, European Parliament, European Union, Global Politics, Insights, Lobbying, Politics, Public Affairs and Communications, Social Media, U.S. Politics

Breakdown of TTIP and TAFTA

TTIP/TAFTA is a true game changer for both the EU and US in terms of economic value, especially in a time of crisis for the EU.  To find out exactly what content people consumed and analyze policy trends, we mined the web (big data). At the moment TTIP/TAFTA  is not being met without issues – as we all know in Brussels – #NSAGate, data privacy and IP are slowing down negotiations (we’re looking at you France), and this is generally what the data had to say as well.

Over view: 5,505 mentions of  TTIP/TAFTA in the last 100 days – too large of number for business and institutions to ignore. In short you need to join the conversation if you have something to say about it ASAP (indecision is a decision).

Overview of the last 100 days amplitude.

Overview of the last 100 days amplitude.

The biggest uptick – a total of 300 mentions – came when Obama spoke at the G-8 summit in Ireland on June 17th when trade talks began. The key theme at this time was the potential boost in the economy. The official press release is here.

“The London-based Centre for Economic Policy Research estimates a pact – to be known as the Transatlantic Trade and Investment Partnership – could boost the EU economy by 119 billion euros (101.2 billion pounds) a year, and the U.S. economy by 95 billion euros.However, a report commissioned by Germany’s non-profit Bertelsmann Foundation and published on Monday, said the United States may benefit more than Europe. A deal could increase GDP per capita in the United States by 13 percent over the long term but by only 5 percent on average for the European Union, the study found.”

Given that there is conflicting information we wanted to see whose idea and data wins out – the Centre for Economic Policy Research (CEPR) or Bertelsmann Foundation (BF)? To do this we looked to see which study was referenced most. The chart below shows the mentions of each organization within the TTIP/TAFTA conversation over the last 100 days. The Center for Economic Policy Research is in orange and the Bertelsmann Foundation is in green.

Research cited most for TAFTA and TTIP

In total both studies were cited almost the same amount:

  • Centre for Economic Policy Research: 80 Mentions
  • Bertelsmann Foundation: 83 mentions
  • Both organizations were mentioned together 53 Times.

More recently though the trend seems to show that the Economic Policy Research is being cited most in the last 30 days, including a large uptick on July 8th. This is mainly due to the market share of the sources being located more in the US and the US wanting to get a deal done faster than the more hesitant Europeans. Keep in mind the CEP claims larger benefits of TAFTA/TTIP than the BF study.

Locations of the Center for Economic Policy Research and the Bertelsmann Foundation. Very diverse and more equal location market share.

Locations of the Center for Economic Policy Research and the Bertelsmann Foundation in the last 30 days: Notice the location marketshare is less diverse and dominated by the USA.

Where are the mentions?

  • The  US had 2,743 mentions (49% overall)
  • All of Europe combined total was 1,986 (36% overall)

Image

Of the topics ACTA is still being talked about with, IP and Data Protection top the list. This is not surprising given France’s reluctance to be agreeable because of the former and #prism, so below are those themes plotted.

Breakdown and trend graph of Topics surrounding TTIP TAFTA

Breakdown and trend graph of Topics surrounding TTIP TAFTA

The top stories on Twitter are in the table below. It’s not surprising that the White House is number one, but where are the EU institutions and media on this?

Top Stories Tweets Retweets All Tweets Impressions
White House 37 15 52 197738
Huff Post  26 0 26 54419
Forbes  23 0 23 1950786
JD Supra 15 2 17 42304
Wilson Center  12 7 19 72424
Facebook 12 0 12 910
Italia Futura 8 0 8 16640
BFNA 7 0 7 40246
Citizen.org 7 13 20 69356
Slate  7 0 7 13927

Everybody knows the battle for hearts and minds of people starts with a good acronym so I broke down the market share between TTIP (165) and TAFTA (2197):

Acronym Market Share: TTIP V TAFTA

I may add more in the coming days but those are a few simple bits of info for now.  Nonetheless if you want to join the conversation on Twitter the top hashtags are below.

Top Hashtags for the TTIP and TAFTA debate

Top Hashtags for the TTIP and TAFTA debate

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