Tag Archives: bigdata

Where Does Privacy in the Internet end? – On Global Differences And What to Learn from Prism and the Sauna

Rolf Schwartmann, Professor at the Cologne Research Institute for Media Laws, wrote on Friday 12th July 2013 in the FAZ (Frankfurter Allgemeneine Zeitung, “Freies Surfen”) about the considerable legal differences between different countries.

In Germany, every citizen has to approve to any processing or storage of personal information. This processing or storage has to be tied to a specific usage. This is secured by consitutional laws. Exceptions to this need to be authorized by a court on an individual basis and require important reasons by the law enforcement agencies.

In the U.S. on the other hand, privacy is by the consitution only secured as an appropriate expectation (“angemessene Erwartung”, Schwartmann). As soon as information is handed over deliberately to a third person it is no  longer considered as private. Moreover, data privacy of U.S. citizens is overruled by national security concerns. All non-U.S. citizens do not have any data privacy rights from a U.S. perspective.

Three things are obvious here:

  1. There are cultural differences regarding data privacy
  2. There are legal differences regarding data privacy
  3. There’s differences in how internet users from different countries are treated

The cultural and legal differences may have to do with historic experience of Germans – e.g. with the Gestapo or Stasi. Americans on the other hand put more weight on national security.

With respect to the european view on data privacy, a comment from Jeff Jarvis is quite interesting. In 2010, when data privacy and Google Streetview were discussed, Jarvis ridiculed the Germans as beeing paradox, as they have no problem to go to the Sauna naked but do have a problem to have a picture of their house on the internet. Here, the interesting point is asymmetry: In the Sauna you see what I see and no one of us will take something seen outside in form of a picture. But you don’t know who will be looking at your house on Streetview.

Social networks for professionals, like linkedIn and XING, deal with that idea of symmetry: If you want to see who looked at you, you have to pay.

Conclusion: Data Privacy is always about a perceived symmetry between give and take (e.g. take privacy for security) which has to be accepted by the individual. For a global business it means, that data privacy has to be dealt with “glocally”. Data privacy has to be considered not only from a legal but above all from a cultural perspective and cultural differences have to be understood and dealt with.

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Connected Car – What Does it Mean to Primary Insurance?

In-car telematics will grow considerably in adoption over the next couple of years. The automotive industry is embracing it and the European Union has put it on its Digital Agenda – under the name of eCall.

A recent overview of what the connected car means for the Insurance Industry can be found here. Some of the ideas and numbers in there date back to 2011. Despite the fact that these ideas are around for a while, Insurance – at least in Germany – is slow in embracing this new model. Here’s two main reasons that are given:

  1. Fear that the new tariff would cannibalize existing revenues
  2. Experience or market insight that drivers would not trade a limited reduction in price for the inconvenience to adjust their driving behaviour.

Maybe there’s huge regional / national differences with respect to this attitudinal issue. In any case it is interesting to see that there’s successful insurance startups leveraging the Pay-how-you-drive business model… They obviously don’t have to cannibalize their existing offerings – and they have a good value proposition for young drivers – who would pay much higher premiums elsewhere.

What if the bigger concern might be even deeper? Imagine a world of intelligent cars that avoid the majority of crashes? What’s the future of the car insurance then? As premiums have to cover the insured loss this would at least have an effect on overall revenues… unless there’s new markets where there’s still a growth in automotive sales to drive more revenues.

Troublesome Big Data Experiences Related to Privacy

Big data has already permeated our every-day life. The most recent news however deal with growing concerns about privacy. Most prominently the NSA prism story uncovered by the Guardian.

Other news didn’t catch the same amount of public attention although they go in the same direction. To name a few examples that I came across recently: Cisco annoyed users last summer with a new anti-porn service which created privacy concerns, see e.g. here. Cisco listened to its customers and changed the policies accordingly. More recently, Microsoft was alleged to read Skype chat messages (see e.g.: Is Skype snooping on your conversations? as well as Microsoft liest heimlich Skype-Chats mit). And then we read the Xbox is suspected to spy in our livingroom: What we think we know about what Microsoft isn’t saying about the Xbox One. 

Interestingly, we could learn in May that Whatsapp had reached 250 million subscribers, despite the fact that it is not in line with international privacy rules and laws. It transfers and stores the complete list of contacts of its users to its servers. See e.g.: WhatsApp in violation of privacy law

The difference in the amount of public concern in these cases seems to correlate with the different amount of perceived benefits. Consumers seem less scared or pay less attentionif they see the benefits. (The Cisco case wouldn’t probably have made such a relatively big story if it wasn’t for the added inconvenience of configuring the device)

But this is by no means a simple recipe for corporate success – if searching for deeper customer insight with the help of big data. The damage to reputation might be considerable. Each company is well advised to follow a well-planned, responsible and sustainable strategy regarding the use of personal information. Consumers and legislation will pay attention and even if your company took corrective action, the negative consumer reviews would still be out there on the web for a long time to come and influence other’s buying decisions.

Comments on Big Data Market Forecast 2012-2017 and Big Data Adoption Barriers

Wikibon just came out with a forecast (Big Data Vendor Revenue and Market Forecast) which underlines my last post: The hype is over, big data is getting real.

Quote:

“In the enterprise space in particular, the combination of a better understanding of the use cases for Big Data and more mature product and service offerings resulted in a significant percentage of Big Data early adopters graduating from small, proof-of-concept projects to large-scale, production-level deployments.”

It also talks about the adoption barriers. These revolve around three major themes:

  1. Lack of Data Scientists
  2. Moving to higher levels of maturity as an analytic enterprise
  3. Lack of application development tools and services

It’s not a suprise that all these difficulties still persist as we’re still in an early phase of adoption from an innovation perspective. Over time all the adoption barriers mentioned there will be overcome. However, I do not believe we will get there by focusing on these barriers per se. Let’s re-frame it this way: In the early days of the automobile, every driver needed to be its own mechanic. In the early days of the PC, the early adopters were extremely knowledgeable about everything – they even built their systems by sticking together the components (as I did, too ;-)). This kind of capability is analogous to what is expected from a data scientist: He’s a Jack of all trades with a scientific foundation in Math, Statistics, Computer Science, programming with a diverse set of tools and languages and specific insights into the topic at hand. Over time we will not need that many Data Scientists of that profile, as technology will mature and the market will consolidate.

Till then, two options for the enterprise: sit and wait…. until others took care of making big data adoptioin more accessible or palpatable – OR: relentlessly focus on some kind of business scenario where going beyond the data that was analysed so far will expand the analytic capabilities. Pick the solution or technology to make it work now, but do not expect to define your big data standards NOW and for ever. It may well be that you will have to enlarge or change the technology foundation in 2-3 years from now. Till then you’ll have earned some early benefits and you’ll have developed a staff with far more experience to build on for the next phase in your big data journey.

Concluding remark: If you go through the above mentioned adoption barriers, it is obvious that the focus is on big data – per se. That focus is wrong. The focus has to be on business opportunities  that can be exploited by advancing our analytic capabilities. Technology considerations are an afterthought. This helped the early adopters to move from a big data pilot to large scale implementations.

 

Big Data Adoption – Skepticism is not a bad sign

I’m just back from IBM’s Analyst Insight (see: http://storify.com/ragtag/ibm-analyst-insights-madrid-dec-4-5) – a meeting with analysts where IBM shares key insights about what we see happening in the marketplace, how IBM is positioned and where it’s heading.

A key question that came up from several analysts is what are the “real” use cases, what is it that clients pay for. And indeed there’s a number of Big Data projects already under way and we have a number of compelling stories to share.

However, it seems there’s still a lot of skepticism in the market and also among some analysts. Which goes back to the fact that a majority of companies still doesn’t see the value and asks for proof points.
In fact this is what we would expect with any technology driven innovation: First, there’s the innovators and then the early adopters that explore how the new technology could create value for them. Together, these two segements account for about 15% of the whole market. The rest, i.e. the majority, is waiting to see the proof points and asks these skeptical questions. Which means a majority of skeptics is not really a negative signal about neither the trend nor the market. It’s just natural in the early adoption phase.  

What makes this phase exciting and interesting though, is that the early adopters are looking for fundamental change and big transformations. And there we see indeed a couple of good stories unfolding. More on that in a subsequent blog.

Getting Started

Just returned from the CMO CIO Leadership Exchange in Paris. Had a lot of good and thought-provocing discussions there. Time for me to finally get started with a blog on what I care about in business. Which has always been innovation, strategy and transformation. Big Data is my current focus and I will share with you what I learn from my clients, the research I’m doing and I’m looking forward to learn from you and your responses, too. Would be great to get a dialogue started…

One final word: I do work for IBM. I’m the Big Data Leader for Global Business Services. But what I’m posting here is my point of view and does not necessarily represent IBM’s opinion or view.