Category Archives: Challenges

Privacy, Safe Harbor and ad blockers: When trust is key to business

Wild net neutrality vs. regulated standardized networks? Privacy for each vs. security for all? Free content with ads vs. pay-per-view? Are we bound to witness the development of the internet with the eyes of a war correspondent?

A few weeks ago, I have been at MeasureCamp in London, a bi-yearly analytics unconference that I find particularly stimulating (more about MeasureCamp here). I have held a session titled “More #Data, less #Privacy: Are we bound to finish naked?”. A summary of the discussion may be found here.

Provocative though the title may have been, the discussion was very constructive, and, as no clear solution seemed to emerge, it ended up with the only possible option, name it common sense, good will, mutual confidence… As for me, I shall keep the word “trust”, especially throughout this post for a good understanding.

Credit: Oleg Dudko via

Trust is is the common ground to the topics that I have named in the title of this post:

  • Privacy policies cannot be operative unless you trust the company storing your data, and in many instances, you may even call for a third party to handle this subject, which usually is called “trusted third-party”… A hot topic everywhere, especially with all the data leaks that have occurred in the recent weeks.
  • Safe Harbor has been put into question, and ultimately terminated by the EU Court of Justice, because trust had been broken between the US and Europe, not only because of the NSA spying scandal, but also of the divergent points of view about what the role of the Governments should be and their right to intrude into the business rules (see the endless debates on Net Neutrality and Right-to-be-forgotten, for instance on the NNSquad site)
Credit: Robert Churchill via
  • Ad blockers also are starting to be an issue, because trust has been mishandled there too. Let us forget the original sin of third-party cookies, when accepting primary cookies from a website, for it to work properly, led to cookies being transferred to scores of third-party users, mostly for ad targeting purposes… My main concern today is about the latest bias in that area, i.e. ad blockers dealing with ad servers so as to generate exceptions for them, and let some ads be displayed, now breaching trust with their own clients.

Let me focus on this specific problem.

OK, ad blockers are standing in the way of advertisers, but so far they are not killing them. Advertising is the key monetisation tool for many free-to-access websites, among which media and news are in the first row, but many models show that such websites still may succeed nevertheless (the Huffington Post, as an example of a pure player, or the New York Times as an example of a redeployment from a paper business model). And, although we all agree that purely free content may only be amateur, plagiary, or infomercial, still ad blockers are not threatening free speech and democracy. And some people may require some rest from all-too intrusive ads, and block them. That’s legitimate.

Still, those same people have to behave accordingly, and understand that not everything may be free. Economy is give and take, debit and credit, buy and sell. These people have to pay for added-value content, beyond Linkedin Pulse summaries, or Mediapost digest e-mails. Pay for an abo at the NY Times on-line for instance. It balances the loss of revenue (ad-blocking) for some websites with paid-for content, for instance news read behind a paywall. This is fair. This are good business rules. This also is all about trust.

Trust is when both sides do their part; trust is when people using ad-blockers buy content somehow; trust is when an ad-blocking software reminds you that not everything may be free on the web, and that you have to pay for some content; trust is when ad-servers are serving relevant ads in relevant quantity, not flooding us until we surrender (or block).

Credit: Maksym Fesenko via

But trust is broken when one of the parties is not playing a fair game; trust is broken with the users when an ad-blocking companies sells, at a very expensive price, bypassing ways for some ad-servers. Trust is broken when little arrangements are made between ad-blockers and ad-servers behind the back of the flock of consumers.

I shall not elaborate more about ad-blockers and especially about the AdBlock deal (more details here). And though this ad-blocker is a tool I have recommended in the past (in this 2014 post, for instance: Data Privacy, between a rock and a hard place), trust is now broken. And breaking trust means losing clients. At least some clients. AdBlock, you lost my trust, you lost me.

Data Strategy, high time to take action!

Way too many companies are still behind schedule, and have the utmost difficulties to set up such data strategies, especially in France. I have then decided to rely on my most significant successes from the past year to review and complete the operational scope of Data Elicitation.

The achievements :

  • The CWA, which makes me the only French data management expert who also is certified in digital analytics;
  • The success of the first Paris MeasureCamp, which I have been co-organizing, the biggest analytics event in France, which is due to happen again on June 27th 2015;
  • The patent validation process going on now at a European level, which validates even more my data management expertise;
  • Les multiples requests I have been addressing, from web analytics to strategic consulting, including text mining, data visualization and big data, many topics for many original inputs.

The restraints :

Still, working on data and digital policies has proved rather difficult; in fact, the restraints in implementing these strategies are clearly more structural than cyclical; McKinsey is summarizing the French situation in 4 items, in this study (only available in French…) published in the fall of 2014 :

  1. organisational issues, and namely the all-too famous vertical organization that I have reported here
  2. a lack of digital competencies
  3. a lack of financial leeway
  4. a lack of clear managerial involvement

The French State could certainly act more and better on two of these issues, education and business taxation. I shall develop more in detail the opportunities for public policies in the digital area in a future blog post.

The two other restraints that McKinsey have identified are more complex, as they are linked to the internal organization of the companies, as well as to their willingness to change.

In my eyes, the French companies have to overcome three biases, which harm their blooming in this data-driven world :

sujet négligeable

  1. Data (and digital) are very often second-class topics, which are handled after sales and financial issues of any kind, when there is time, so to say very rarely as a priority. The website? A necessary evil. Social networks? we have to be able to reach young people! Data management? Sure, we have a CRM. So many prejudicial and sweeping statements: data and digital are downgraded as cost centers, and absolutely ignored as growth drivers.désaccord dans le groupe
  2. Investing into a data strategy is often subject to collective decision, through a board or a project coordination, and seldom the will of a single person. Hence, as for most “collective” decisions, it often is the lowest bidder who wins, the most careful, the conservative. On top of this, the competition between various department, be they marketing, IT, finance or sales, generates a paralysis, where emulation would be required.information sous clef
    1. Finally, and that is a key subject, the various data owners still consider that exclusive information ownership grants them an additional share of power. What a mistake! at the very moment when an information is stored, it is losing all its value, as data only have a meaning as they are enriched by others and used for decision-making.

An example? Three departments, marketing, sales, finance. Three products, A, B and C. Marketing has done some research, and clearly A is the best product. Sales are positive, B is the best-seller. Finance has analyzed the ROI, and C definitely is the most profitable. So two options: the wild-goose chase, and then the quickest, or the most convincing one wins, or one share information in a transverse way, so as to ponder the best mix for the company. One certainly would wish the second option happened more often…

Wherever there are data, there should be first an analysis, then a decision process and eventually an assessment.

The outlooks :

These blocking points have led me to rethink what Data Elicitation core business should be in the short-term.

As a matter of fact, it is vain to try to convince some companies to work on their global data strategy, when they still are burdened by the restraints as depicted above, while they have not realized yet how large their potential could be. therefore, I have created some training modules, so as to make the concerned professionals aware of the necessity to think their data management in a transverse and global way.

You will then find on this website, under the header “training“, a description of modules dedicated to people training, into such topics as data management and analytics, both on the methodological level and through such concrete actions as database maintenance, data sourcing or quality assurance.

Or course, I shall keep on consulting at C-level and Executive levels, when they are willing to handle their most acute data strategy issues.

You know it all, now… Your comments and/or questions about those modules are highly welcome, as well as any suggested improvement.

Now, there only is one thing to do, e.g. share this blog post IMMODERATELY…

I hope to hear from you soon!

[cette note de blog en Français est ici]

Free your data: revoke the precautionary principle!

People who know me are aware that I often complain that applying blindly the so-called “precautionary principle” is leading to inaction.

However, fear does not prevent danger, so tells a popular French saying. Similarly, data should not prevent any decision making.

Word Cloud Precautionary PrincipleIn a paper about the precautionary principle (in French), Gaspard Koenig states that “the precautionary principle central idea, and its most important drawback, lies in the will to avoid any uncertainty whatsoever. Then, when in doubt, forbear.

I do agree. And the ultimate paradox lies in the fact that the more data are to be handled, the more parameters are to be tuned, the less certain the decision is. Big Data leads our leaders not to decide anything anymore. The data overflow paralyzes.

Therefore, the automatic usage of the precautionary principle has to be suppressed, especially when it comes to institutionalizing it (let us not talk here of its addition to the French Constitution, certainly the most astonishing legislative act one has witnessed in the past decades).

Let us investigate the GMO example (also mentioned by Gaspard Koenig in his paper). Many studies and tons of data, most of them rather contradictory, are implying that GMO’s could represent a threat in the end, in 20, 30 or 50 years from now, either through a wave of cancers, or some alteration of our genetic material. Maybe. Until then, thanks to GMO’s, millions of people could have been fed better (or even fed at all), but instead they will starve. To death. So, do we act according to our conscience or do we let data validate our paralysis?

Beyond the comfort of data-backed analysis lies the necessary power of decision-making. Data may only sustain decision processes, whereas making the decision is a privilege of mankind. Besides, in the years when massive data processing systems have emerged (in the eighties), were we not speaking of “Decision Support Systems”?

Hence, one must rely on data, but without hiding behind a stack of them. It must be clear that data sets, even “big” ones, always harbor a part of uncertainty, not about today [we are absolutely sure that using GMO’s at a worldwide scale would reduce global hunger], but about tomorrow [as GMO’s may generate risks for health and environment]. Why? Because even the most refined predictive model based upon Big Data will never reach 100% reliability ever.

the signal and the noiseAnd even Nate Silver, the half-god of predictive models in the US (see a slightly ironical portrait in French, here) starts his cult book – “The Signal and the Noise” – by a foreword basically telling the reader that”the more data, the more problems” there are…

Therefore, people in charge have to take a risk, whatever its height. Give up the sacred precaution. And this to everyone’s benefit, since taking a risk is the only way to open breaches, to make a breakthrough. Thinking about it, with the precautionary principle, the Apollo XI moon landing would never have happened…

So, say yes to Big Data for the Blitzkrieg, and no to the Maginot Line of the precautionary principle. Or, with a balanced point of view, say yes to the D-Day, and no to the Atlantic Wall.

Your data must give rise to movement, not to motionlessness, to action, not to dejection, must help conquer new grounds, not defend one’s turf.

Break the WallYou have data, that is for sure. You want to take action, that is most probable. So, do not hesitate, have your data elicited, so as to break the wall and take the most enlightened decisions!


[French version: Libérez vos données: révoquez le principe de précaution!]

Analytics without cookies? My follow-up to #MeasureCamp IV

As mentioned in my previous post “Giving up cookies for a new internet… The third age of targeting is at your door.“, I have attended the fourth Measure Camp in London (, on March 29th. And my (voluntarily controversial) topic has been: “Web Analytics without cookies?

The subject has been introduced by the following three charts, a short introduction to what I expected to be a discussion, and a hot one it has been!

Measure Camp IV (post)

Basically, the discussion has been getting around three topics:

  • Are really cookies going to disappear, and if yes which ones and how?
  • Are cookies disapproved by the users because of their lack of privacy or rather because of some all-too aggressive third-party cookie strategies?
  • Are there any solutions, and when do we need them at last?

Topic number 1 definitely is the most controversial. It already is difficult to imagine how to deal without what has been the basics of collection, targeting and analysis. On top of this, some valid objections also have been given, such as the necessity to keep first-party cookies for a decent browsing experience as well as the request from a fair share of the users to keep ads, providing they were relevant to them. A very good follow-up has been brought by James Sandoval (Twitter: @checkyourfuel) and the BrightTag team. Thanks to them for their inputs.

Clearly, the participants were all agreeing that a cookie ban would only impact third-party ones, and occur for political reasons (maybe not before 3 to 5 years), lest a huge privacy scandal ignites an accelerated decision process. Still, a fair amount of the internet revenue would then be imperiled.

At this stage, there still remains the acceptance of cookies by the users. There is a wide consensus within the digital community that people browsing the internet accept a reasonable amount of cookie intrusion in their lives, should this generate relevant ads. Actually, I think this view is biased, as nobody has ever asked whether people would rather browse with or without ads… The question always has been between”wild” and “reasoned” ad targeting… It reminds me of an oil company asking if car drivers would rather tank diesel or lead-free, not allowing “electricity” as a valid answer…

So the question of cookie acceptance remains open in my eyes, and this may be a key driver to designing alternative solutions.

What options do we have at hand then?

The first and blatant one is a better regulation of third-party cookies, especially the ability of the user to master how, when and with whom their first-party cookies could and should be shared in an opt-in mode. The law (in the EU) theoretically rules this (see EU rules about cookie consent here), through a warning to the user about cookies, when he or she opens a new website. Still, national transcriptions and various ways of web page developments have made this law non-understandable, and mostly not actionable on a global basis.

A first step would then be to abide by the user’s choice, and give him the ability to manage his or her own cookies, sharing some, all or none of them with third-parties, as they wish. A difficult task, especially when nearly 30 government bodies are to be implied… So why not investigate non-cookie options?

In London, I have introduced two possible ways:

  1. Create a unique Id for each user, somewhat like Google’s unique Id, but managed by an independent body. My suggestion is that such an Id should belong to the whole community, like HTML or HTTP… A huge task.
  2. The other idea is mine… It would consist of the generation of anonymized profiles, based on browsing patterns. This idea I shall develop more in detail in future posts, but the idea is worth thinking, especially when one imagines that today’s user mood may not be tomorrow’s, and require a very dynamic targeting methodology…

So this hot discussion on cookies at least has initiated discussions among the digital community. It also proved that such fresh (and sometimes idealistic) views as mine are necessary to keep the digital community staying on the edge of innovation. So stay tuned, I shall go on providing food for thought so as to “shake the tree” of Measurement…

Telecom Operators: dinosaurs or mutants? Wanna know the answer? Really?

On Monday June 24th in the evening, I have attended a promising conference about the Telecom Operators 2020 agenda, organized by the G9+ think tank (Twitter: @InstitutG9plus). Top flyers on stage, top attendees in the room, alumni from the best French schools, I was expecting high-level discussions and impressive insights for the future.

How can I tell? Except for some bashing on Twitter (hashtag: #G9plus), the talks have been formal and conventional, focused on technical issues, and especially on the carrier mission of the Telecom Operators. As stated by John Stratton, President Verizon Enterprise Solutions, in a pre-recorded interview, “my core is still my core”; blatantly, Verizon does not expect to become some kind of software vendor or content provider, but will remain mostly an infrastructure company.

Same during the panel talks, including namely Thierry Bonhomme, Senior Exec VP Orange Business Services, when 90% of the talks have been focused on the communication pipelines, such as LTE/4G or Fiber, and only in the very last minutes, the contents have been mentioned, mostly to say that this is none of the Operators’ business…

So, are Telecom Operators dinosaurs or mutants? Clearly, undoubtedly, Dinos.

This is a bit scary for the future, I must say. as Telecom Operators currently use most of their power to prevent anything dangerous to happen, and to protect their positions. See Benoit Felten paper on ZDNet (in French…) about the will of the Telecom Industry to promote more Protectionism in Europe, and you will get my point…

Telecom Operators are acting like Automakers who would promote their cars solely by taking the breadth of the road into account, so that more cars would be able to drive at the same time. But clients do not care for broader roads, they care for better cars, less costly, less consuming, more sustainable, more user-friendly…

A lot of possible innovations and development options for Telecom Operators are still open, should they believe that their fiercer competitors in chasing the Telecom client added value are not other Telco’s but are rather named Google, Apple, Samsung, Amazon…

So, what next? Just yelling at Telecom Operators, I shall use Solofo Rafeno’s sentence (adapted from one of his yesterday’s Twit): “Gentlemen ! What if you talked about your future, Added-Value Services, Cloud, Apps, new forms of distribution, affiliation, instead of bits & bytes…”

“Big Data”: new frontier or black hole?

Stéphane Richard (CEO of Orange) at the Avignon Forum in 2012: “Big Data, this is private data business, and it is scaring” (in French: “Le Big Data, c’est le commerce des données personnelles et c’est effrayant”)

In my eyes, there is much more than threat, when thinking of the future of Big Data. First, let us ask the relevant question: what is the definition of “Big Data”? A few hints, picked over the web:

on Wikipedia, the definition tells that ” Big Data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” Simple.

Too simple. As mentioned on Mike2.0 wiki, “not all large datasets are big”. Certainly, some small datasets may be considered “big”, as they are complex, and some large datasets are not “big”, as their structure is well-known and easy to handle. But still, complexity may be different, from one point of view to the other (for my part, I do consider mobile internet data as “big”, whereas Mike2.0 only consider them “large”)… For reference, the link is here:

More elaborated, Gartner’s definition (from their IT Glossary) says that “Big Data in general is defined as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making”. Similarly, IBM says that “Big Data spans four dimensions: Volume, Velocity, Variety and Veracity”. the 4 V’s. Somewhat of a marketing gimmick, but not so bad, after all…

When looking into definitions that are more Digital-Analytics-oriented, I will stay with Avinash Kaushik’s definition: “Big Data is the collection of massive databases of structured and unstructured data”. So basically, a promise for some bright analytics, but that will be hard to find, a classical needle in the haystack, or more exactly, a jewel among tons of rocks.

My own definition will then be a bit more provocative: “Big Data is a set of data, that is too big for any actual processing capacity”. Let me elaborate.

From the start, my career has lied mostly with Retail Tracking Data usage. In this area, bimonthly manual collection of purchases and inventories used to be the norm at the end of the eighties. And then came the first Big Data rupture, e.g. the introduction of weekly electronic data collection, using EAN/UPC-based datasets. 1,000 times more data points. Big data for the early nineties standard. Small beer twenty years later.

Similarly, when the same weekly electronic data collection – still based on samples – switched to daily census at the end of the nineties, data volumes multiplied then again by more than factor 100. Big data again. Now common for any Retail Panel service.

Again, when the same data collections were made available as transactional data records, showing all possible disaggregated data points – especially thanks to the upsurge of retailer fidelity cards – data volumes were again multiplied by factor 1,000. Big data another time. Now about to be handled more or less properly by Market Research companies. Awaiting the next big data frontier?

So definitely, data that are named “big” today are on the edge of our current ability to handle such data sets. Tomorrow, other data sets will overtake this “big” status, maybe with the addition of geo-location information or other causal data (digital journey for instance).

Ever more data for ever more information. Or the new frontier that leads to the black hole. Why? Because too much data may mean too much insights.

That is the drawback of big data. Too much data. Too many interesting things. Too many insights. The black hole of information, fully absorbing our capacity to catch new trends and key insights.

The bigger the data, the more complicated it is to extract the key information that will trigger new ideas, new business, new revenues. As mentioned in this blog post from Mediapost (, the key issue is not any more to find an insight, it is to find THE insight. We are not to break the next frontier any more, we are to find out in which direction we ought to search where to go.

We have to do this. Quickly. Before the black hole of big data swallows what remains of the dimming light of key insights…

So, to close this blog post, and to start the discussion, a very interesting point of view by Jannis Kallinikos from the LSE:

Brave New World…

Hello Brave New World!

It will not be my goal to comment Aldous Huxley’s book in this blog, even though I’d love it, but rather to comment on what has been my core business for the past 20 years: data elicitation.

With the constant growth of the online usage, the world has been processing ever more data than ever; such names as “big data”, “cloud computing”, “social media”, “web analytics” are known to nearly every man-in-the-street. A new world that did not exist a few years ago, and that was known only to happy few up until a few months ago.

A new world; Fine. Why brave? Actually, these words sound like a Terra Incognita, as nobody really has an answer on how to handle them: fear of the gigantic amounts of data points, questions on where to start with these data, lack of operational tools and schemes, the void is near…

So we have to be brave; or this new world will be brave in Aldous Huxley’s way, creating a data hell that only robots and maybe a few dominating companies will be able to handle.

Of course, I do not pretend I have THE answer, or I would not be writing this blog, as I would rather be counting my billions on a Caribbean beach 🙂

But I have experienced several data revolutions through my Market Research experience, from the introduction of Bar Codes for FMCG sales tracking, to Point-of-Sales single data collection and now to my current task on understanding how to cope with the vastness of the internet data…

This blog aims at sharing this experience, and I hope I can bring some tips to whoever is facing the challenge to use such masses of data. I have named this specific task “Data Elicitation”.

So let us elicit the data of this Brave New World!