Whitepaper – Albada Jelgersma Conversation

The consequences of a stolen phone

06/01/2014 by Paul

Your wallet is stolen. You wanted to pay for your tall latte and it is gone. You search all of your pockets and looked around, bewildered.

Maybe somebody found it and will hand it to you. No. It’s gone. The nice lady at the counter understands and gives you your coffee anyhow. That’s nice.

It doesn’t change the fact that your wallet is stolen. In your mind you create a list of everything that is in it. Some money, the tickets for the theater, your bank card and your credit card, some pictures of your children and a business card you got while you bumped into an old friend on your way to the coffee shop.

So now you reach for your phone, you have to call the bank to block your cards, you do not want some punk to get his dirty hands on your salary.

Oh wait and sh@#$%^&*, your phone is gone too…

In a recent white paper of the Atos Scientific Community the security aspects of mobile devices is addressed , as well as other aspects in the management of devices in the new bring-your-own-device concept that is being allowed by many companies and full heartedly embraced by employees.

The quotes below are from that white paper.

“Enterprise Mobile Management solutions currently available in the market address different aspects of BYO. Balancing those with network & access as well as data and applications usage will pave the way for a successful BYO implementation…”

Ok. It is gone, you do a quick mental inventory of what is on your phone.

Access to your personal and business email, Twitter and Facebook account. Your contact list of about 400 people with their email addresses, home addresses and telephone number included. On top of that access to your DropBox account with all the info on a recent bid and the complete cost break down of all products.

And because you have a new NFC enabled phone, your credit card is also in digital format on your phone. Now what?

“The key area to support BYO in 2016 will be tablets and their descendants (e.g. wearable computers), along with smartphones. We see these as the two key device segments.”

The white paper does not only cover this case of a stolen phone – it goes into all measures you can take if you adopt the bring-your-own-device scenario in your company.

What to do with applications, data and network access; all these aspects are clearly explained and some best practices are listed for any CxO that is looking into this.

“Security in such dynamic environments as BYO must be built on the assumption that anyone or any device may get access to the data, but that only authorized users should be able to use it for the intended and agreed purpose, and under a defined context.”

“Sir? Is this yours?” When you turn around you see a nice person holding up both your phone and wallet – you start breathing again.

At the same time you think about what you could do to avert the disaster that did not happen this time.

This blog post was previously published at http://blog.atos.net/blog/2013/06/24/watch-this-space-the-consequences-of-a-stolen-phone/

Curiosity drives cloud computing

15/12/2013 by Paul

I like asking questions and I like getting good answers even better. It is because of that, I now have a love / hate relationship with search engines. Most of the time they give me a 50% answer, a kind of direction, a suggestion, a kind of coaching to the real answer. It is like the joke about the consultant; “the right answer must be in there somewhere, because he or she gives me so many responses”.

In spite of all kind of promises, search engines have not really increased their intelligence. Complex questions with multiple variables are still nearly impossible to get answered and the suggestions to improve my question are mostly about my spelling or because the search engine would have liked a different subject to be questioned on.

So nothing really good is coming from search engines then? Well most arguably search engines have brought us cloud computing and a very powerful access to lots and lots and lots of data, otherwise known as ‘the world wide web’.

No wonder I envision that powerful access and cloud computing are the two most important values we want to keep while increasing the capacity and intelligence to do real analytics on large data sets.

In a whitepaper of the Atos Scientific Community, these 2 elements are explored in great depth:

Data Analytics needs cloud computing to create an “Analytics as a Service” – model because that model addresses in the best way how people and organizations want to use analytics.
This Data Analytics as a Service – model (DAaaS) should not behave as an application, but it should be available as a platform for application development.

The first statement on the cloud computing needs suggests we can expect analytics to become easily deployed, widely accessible and not depending on deep investments by single organizations; ‘as a service’ implies relatively low cost and certainly a flexible usage model.

The second statement about the platform capability of data analytics however, has far reaching consequences for the way we implement and build the analytic capabilities for large data collections.

“Architecturally, and due to the intrinsic complexities of analytical processes, the implementation of DAaaS represents an important set of challenges, as it is more similar to a flexible Platform as a Service (PaaS) solution than a more “fixed” Software as a Service (SaaS) application”

It is relatively easy to implement a single application that will give you an answer to a complex question; many of the applications for mobile devices are built on this model (take for example the many applications for public transport departure, arrival times and connections).

This “1-application-1-question” approach is in my opinion not a sustainable business model for business environments; we need some kind of workbench and toolkit that is based on a stable and well defined service.

The white paper describes a proof of concept that has explored such an environment for re-usability, cloud aspects and flexibility. It also points to the technology used and how the technology can work together to create ‘Data Analytics as a Service’.

This blog post was previously published at http://blog.atos.net/blog/2013/03/25/watch-this-space-curiosity-drives-cloud-computing/

A new business model in 3 easy steps

24/11/2013 by Paul

If you like curly fries you are probably intelligent (1).

This insight comes from the University of Cambridge. The researchers analysed the data from Facebook to show that ‘surprisingly accurate estimates of Facebook users’ race, age, IQ, sexuality, personality, substance use and political views can be inferred from the analysis of only their Facebook Likes’.

The possibility to collect large amounts of data from everyday activities by people, factory processes, trains, cars, weather and just about anything else that can be measured, monitored or otherwise observed is a topic that has been discussed in our blogs many times.

Sometimes indicated as ‘The Internet of Things’ or, with a different view ‘Big Data’ or ‘Total Data’, the collection and analysis of data has been a topic for technology observations and a source of concern and a initiator for new technology opportunities.

This blog is not about the concerns, nor is it about the new technologies. Instead it is about a view introduced by a new white paper by the Atos Scientific Community called “The Economy of Internet Applications”; a paper that gives us a different, more economic, view on these new opportunities.

Let’s take a look at a car manufacturer. The car he (or she) builds will contain many sensors and the data from those sensors will support the manufacturer to enable better repairs for that one car, it can provide data from many cars for an analysis to build a better car in the future and it can show information to the user of the car (speed, mileage, gas). The driver generates the data (if a car is not driven, there is no data) and both the driver and the car manufacturer profit from the result.

Now pay attention, because something important is happening: When the car manufacturer provides the data of the driver and the car combined to an insurance company, a new business model is created.

The user still puts in the data by using the car, the car manufacturer sensors in the car still collects the data, but the insurance company gets the possibility to do a better risk analysis on the driver’s behaviour and the cars safety record.

This would allow the insurance company to give the driver a better deal on his insurance, or sponsor some safety equipment in the car so there is less risk for big insurance claims in health or property damage.

It would allow the car manufacturer to create more value from data they already have collected and it would give the driver additional benefits in lower insurance payments or improved safeties.

What just happened is that we created a multi-sided market and it is happening everywhere.

“If you don’t pay for the product, you are the product”

The white paper explains it in more detail but the bottom line is that due to new capabilities in technology, additional data can easily be collected.

This data can be of value for different companies participating in such a data collection and the associated analytics platform.

Based on the economic theory of multisided markets, the different participants can influence each other in a positive way, especially cross sector (the so called network effect).

So there you have it, the simple recipe for a new business model:

Find a place where data is generated. This could be in any business or consumer oriented environment. Understand who is generating the data and why.
Research how: a. that data or the information in that data, can give your business a benefit and b. how data that you own or generate yourself, can enrich the data from the other parties.
Negotiate the usage of the data by yourself or the provisioning of your data to the other parties.

In the end this is about creating multiple win scenarios that are based on bringing multiple data sources together. The manufacturer wins because it improves his product, the service provider wins because it can improve the service and the consumer wins because he is receiving both a better product and a more tailored service.

Some have said that Big Data resembles the gold rush (2) many years ago. Everybody is doing it and it seems very simple; just dig in and find the gold – it was even called ‘data-mining’.

In reality, with data nowadays, it is even better, if you create or participate in the right multi-sided market, that data, and thus the value, will be created for you.

(1) http://www.cam.ac.uk/research/news/digital-records-could-expose-intimate-details-and-personality-traits-of-millions

(2) http://www.forbes.com/sites/bradpeters/2012/06/21/the-big-data-gold-rush/

This blog post was previously published at http://blog.atos.net/blog/2013/03/18/watch-this-space-a-new-business-model-in-3-easy-steps/

Would you like a cup of IT

05/11/2013 by Paul

The change in the IT landscape brought about through the introduction of Cloud Computing is now driving a next generation of IT enablement. You might call it Cloud 2.0, but the term 'Liquid IT' much better covers what is being developed.

In a recently published white paper by the Atos Scientific Community, Liquid IT is positioned not only as a technology or architecture; it is also very much focused on the results of this change on the business you are doing day to day with your customer(s).

"A journey towards Liquid IT is actually rather subtle, and it is much more than a technology journey"

The paper explains in detail how the introduction of more flexible IT provisioning, now done in real time allows for financial transparency and agility. A zero latency provisioning and decommissioning model, complete with genuine utility pricing based on actual resources consumed, enables us to drive the optimal blend of minimizing cost and maximizing agility. Right-sizing capabilities and capacity all of the time to the needs of the users will impact your customer relationship – but, very important, designing such a systems starts with understanding the business needs.

"Liquid IT starts from the business needs: speed, savings, flexibility, and ease of use"

Existing examples of extreme flexibility in IT (think gMail, Hotmail or other consumer oriented cloud offerings) have had to balance between standardization and scale. The more standard the offering, the more results in scaling can be achieved. This has always been a difficult scenario for more business oriented applications. The paper postulates that with proper care for business needs and the right architecture, similar flexibility is achievable for business processes.

Such a journey to 'Liquid IT' indeed includes tough choices in technology and organization, but also forces the providers of such an environment to have an in-depth look at the financial drivers in the IT provisioning and the IT consumption landscape.

"The objectives of financial transparency dictate that all IT services are associated with agreed processes for allocation, charging and invoicing"

There are two other aspects that need to change in parallel with this move to more agility in IT; the role of the CIO will evolve and the SLA that he is either buying or selling will change accordingly.

Change management will transform into Information Management as the use of IT as a business enabler is no longer the concern of the CIO. IT benchmarking will become a more and more important tool to measure the level of achieved agility for the business owners. The focus on the contribution to the business performance will be measured and needs to be managed in line with business forecasts.

The white paper authors conclude that "Business agility is the main result of Liquid IT" – sounds like a plan!

This blog post was previously published at http://blog.atos.net/blog/2013/03/08/watch-this-space-would-you-like-a-cup-of-it/

The Data ‘Explosion’ is real

30/10/2013 by Paul

In a recently published research called Ascent Journey 2016, the Atos Scientific Community considers the massive growth in data and storage as an important trend in IT.

Whilst the concept of Big Data has been around for a number of years and is relatively well understood, it is now becoming clear that everything we do is leaving a trail of data that can be analyzed and used.

Examples include the payments we make on a credit card, the books we read on an e-reader and our energy use by driving an electric car. This will lead to a new era of Total Data that, in turn, will lead to new business models, services and economic growth.

We don't yet understand all the implications of this – for businesses and society – but organizations that are able to harness and make sense of the vast quantities of heterogeneous data from disparate sources will gain valuable insights into market trends and opportunities.

An 'Ecosystem' of new management tools is taking shape, covering the various layers of the data stack in the enterprise and capable of delivering a 'Total Data' approach.

The technology that supports the Information Management Lifecycle in the enterprise is going through a profound change, due to the emergence of new solutions, many from open source background (NoSQL databases, Hadoop, analytical tools like R, visualization tools). To enable the 'Total Data' environment, the new technologies need to connect into and partly replace traditional technologies.

In some scenarios, data must be obtained, processed and correlated with insights being derived and actions initiated as close to real time as possible.

Yesterday's data is not interesting unless it helps predict tomorrow. Yesterday's traffic report isn't helpful in plotting a journey today unless it is known to represent today's pattern as well, and combined with other data can improve future congestion. Pattern Based Strategy enables huge amounts of historical data to be analyzed for previously invisible patterns. These patterns give us the power to start predicting what is likely to happen in the future, so we can plan and improve, both in real-time and in non-real time scenario-planning. For example, real time predictive analysis will plot the route for transporting donor organs across a city safely and quickly, continuously adapting the route to changes in the traffic patterns as they are happening. Another example is a country to make compliance recommendations (and potentially becoming a legal requirement) to companies for maintenance regimes for their infrastructures or industry plants using analytics on historic data and thus establishing an automated "what are the lessons learned" process.

Everything will be digital and everything will be connected.

Everything will be captured; "your life is becoming a video" – you can even replay your actions, thoughts and analyse in various forms and for multiple purposes (see http://quantifiedself.com/ for example); this is not only becoming possible for peoples life's, but anything that can be measured can be tracked, traced and put in a digital context for analysis. The ability of businesses to process this wealth of information is still unclear. What these developments – and others related such as 3-D printers and cognitive computers that will be able to replicate smell and touch for their users – mean for society, laws and concepts such as individual privacy need to be reassessed and will prove a huge challenge to governments, businesses and individuals in the 21st Century; for example long-established laws and concepts such as individual privacy need to be reassessed.

After an initial confusion phase, traditional and 'Big Data' orientated approaches to analytics will converge in a unified 'Total Data' platform.

Big data relies on its sister technologies of optimized IT networks, rapid mobilization communication tools and cloud computing. Data Analytics as a Service could emerge from a combination of Big Data, Pattern Based Strategy and Cloud technologies. Business performance can improve in areas such as increased forecasting and enhanced automation capabilities and buildt new business propositions upon the discoveries they can do using Total Data as a source of undiscovered information.

[This blog post is a rewrite of http://blog.atos.net/blog/2013/02/22/the-data-explosion-is-real/?preview=true&preview_id=1555&preview_nonce=6df2f23c80 ]

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