Re-use your old smartphone (3 ideas)

May 7, 2014

256px-Assorted_smartphonesIf you are like me then you also have a drawer with the several generations of phones that you went through in the last 15–20 years. Of course you should not let them acquire mold but keep their batteries charged and rather give them to recycling or donate them. (Some links on this subject further down under “Related Articles.”)

But the last smartphone you still own–should you not keep it around–just in case? Well, there is also some good use for your oldie. Here three ideas:

1. Use it as a mobile hotspot

I live in Switzerland, a relatively small country and I live 10 minutes by bicycle from the French and German borders. This means that I am crossing borders quite often and if I am not careful and turn off data roaming I am facing considerable data roaming charges.

On the other hand, most of the countries I visit offer relatively cheap pre-paid SIM cards for voice/data or data only. But if I use those on my regular phone I cannot be reached under my normal phone number for calls or SMS messages any longer.

So here is my solution. I carry my old smartphone with me in a pocket and use it as a mobile hotspot whenever I do not have access to WIFI. These are the steps you can take:

  1. Disable data roaming on your new smartphone–the one that carries your normal SIM card
  2. Get a data SIM card for the country you are traveling to or use one of the international roaming SIM cards. Check on your favorite search engine with the keywords “data SIM” or “roaming data SIM”
  3. Install the SIM card in your old phone
  4. Follow any necessary steps communicated to you by the SIM card provider to activate the card (typically sending a SMS to the provider)
  5. Install Onovo count (Google app store link) or any other data counter app (iPhone data counter app) on this old smartphone to keep track of your data usage
  6. Set up the data counter to display the percentage used on that phone
  7. Go to Settings > Networks > WIFI > Portable Hotspot and turn it on
  8. Set the APN name (usually your phone’s name) and set a password for your hotspot so that others won’t surf using your credits
  9. Put the oldie back into your pocket
  10. On your regular smart phone turn on WIFI
  11. Search for the network APN that you just created for the hotspot and enter the password
  12. You are on and can use data services. And you keep access on your new phone to all your normal calls/SMS/contacts etc.

You can use this hotspot for any other device you or your company are carrying (other phones, tablets, laptops, . . .) but keep an eye on your data usage unless you have an unlimited data plan.

2. Use it as internet radio on your stereo

Before you go out to buy one of the digital radios see if the stations you want to listen to on your fancy stereo system are not broadcasting on the internet. You can find hundreds and hundreds of stations on the internet that are fun to listen to. But maybe the sound out of your computer speakers is not up to your expectations and you don’t want to carry the computer to your living room to connect it to your stereo.


Cable to connect your phone’s headphone outlet to your stereo. Red for the right audio channel.

Here is what I did. I use my old smartphone and one of the internet radio apps and connect the smartphone to my home WIFI network. I use the phone’s headphone plug to connect it by cable to one of the input jacks of my stereo amplifier. Depending on your system you might have to purchase an adapter cable (very cheap e.g. on eBay).

Connect the old phone to your stereo and start the internet radio app and enjoy the superb sound of your stereo system. Similarly you can also stream your music library to your stereo system if that one is not WIFI enabled yet. Use an app like Soundwire or AirBubble to stream from your PC or Mac to your phone.

3. Use it for all those other apps

More apps than your phone can hold. Credits:

More apps than your phone can hold. Credits:

Unless you rooted your new phone you will run out of space for new applications pretty fast. Even if you move apps to the SD card on the phone (if available). Since you might be carrying the old phone with you as a mobile hotspot (see above) you could store some of the rarely used apps on the old phone and remove them from the new phone. There are many of such apps I found that I use very rarely including some useful but rarely used apps and games.

What other uses for your old smartphone did you come up with?

Please share with us by replying below.

5 Ways to Donate Your Old Cell Phone to Charity

Mobile Phone Recycling

Can you stream your PC’s audio through your smartphone?


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A Broken Bone? 3-D Print Your Cast!

October 8, 2013

3-D printed “Cortex” as developed by Jake Evill. Image as shown in the article quoted

Recently there had been some bad vibes around 3-D printers due to all the reports that you can now download files to print your own (working) guns.

But this new development is rather fascinating: As the Daily Mail in its online portal summarized in a recent article,

  • Jake Evill, a media design graduate from New Zealand, has worked with the orthopaedic department of his university to create a next generation cast
  • Named the Cortex, it uses 3D scans and X-rays to provide tailor-made support for an injury and once printed it easily clips on to the affected area
  • Unlike traditional casts it is lightweight, ventilated, washable and thin enough to fit under a sleeve

Read more:

Will we soon have 3-D printers in every (second) home? Will we start to rebuild spare parts not deliverable any longer? What other interesting uses did you encounter?

Update February 2014: Now a novel 3-D printer can print carbon fiber and make very strong structures. See the article on SmartPlanet.

A Mobile Phone Construction Set: The Lego Approach

September 27, 2013

Your phone construction set. Picture from the article quoted

SmartPlanet today reported on a Lego-type approach taken by Dave Hakkens to build phones from modules that fit together to form a phone and that can be replaced for instance to increase storage or to change the display.

Read the full article here:

Q&A: Dave Hakkens, designer of Phonebloks, on reinventing smartphone design | SmartPlanet.

I find Dave Hakkens’ approach both, fascinating and promising. It would really help to avoid this heap of obsolete phones everybody of us finds in his drawer over time.

Watch the video from the article:

Which other examples of such modular designs did you encounter recently?


P.S. This is my 100th post. Thank you all for following.

Doing Well By Doing Good: Re-CAPTCHA at the Cross-Section of Technology, Economics, and Psychology

August 7, 2013

CAPTCHAs can be found everywhere today–even at unusal places. Image at

A colleague of mine at Infosys, Kapil Ashok Jaiswal, posted an article on our company internal blog site. I am thankful that he gave me the permission to re-post his contribution on my blog.

He had titled his blog “Killing Two Birds With One Stone“–being a biologist by training (and vocation) I had to choose a different title.

He observes how CAPTCHAs have been succeeded by reCAPTCHAs and how by solving the task of filling in the CAPTCHA crowd-sourcing of tasks is being achieved.

The term CAPTCHA (for Completely Automated Public Turing Test To Tell Computers and Humans Apart) was coined in 2000 by Luis von Ahn, Manuel Blum, Nicholas Hopper and John Langford of Carnegie Mellon University (source:

Here Kapil’s post:
(Begin Quote)

A few days ago, I came across an innovative model which made quite an impression on me. Since then, I have been mulling over it, again and again, to find more applications of this wonderful model. As crowd-sourcing is in fashion today, I thought “why not source some ideas from my fellow colleagues?”

Let me first explain this innovative business model. I am sure most of you would have come across CAPTCHAs, a distorted sequence of characters, which you had to fill out in some sort of web form. Well, the idea behind it is to ensure that it’s humans who are filling the forms and not some sort of computer program. It turns out that approximately 200 million CAPTCHAs are typed everyday by people around the world. Each time a person types a CAPTCHA, essentially he ends up spending at least 10 seconds of his time to do so.

If you multiply that by 200 million, you see that humanity as a whole is spending about 500,000 hours every day typing CAPTCHAs. Well, I should point out here that a lot of innovation comes from how you see things. The inventor of CAPTCHA had a socialist mindset with, of course, an innovative flavor to it. To capitalize on this huge effort which he saw as wastage, he came up with reCAPTCHA, a revision of the CAPTCHA concept.

Example for a simple CAPTCHA (from


reCAPTCHA is nothing but two strings of words next to each other. Well, while the concept remains the same as in CAPTCHA, there is a unique thing happening behind the scene. Let me take a step back and explain a few missing links. There are a lot of projects out there trying to digitize books. The digitization process uses OCR – optical character recognition – a technique to figure out what is the text in the image of the scanned book. Now the problem is that OCR is not perfect, especially for older books.

For example, for books that were written more than 50 years ago, the OCR technique cannot recognize about 30% of the words. So here is one solution: what if we could have a human being recognize those words? But then the next question is: at what cost?

If we go by behavioral economics, people are more geared towards a task if they are intrinsically motivated rather than extrinsically motivated. For example, when you are writing a complex piece of code, the whole desire of seeing it work is far better a push for you than any tangible reward. So, if we need to pay people to recognize all these words, it’s not only going to be expensive due to the sheer scale of the project, but it will also be difficult to motivate people to carry out this mundane task.

Example of a reCAPTCHA. One of the words could not be dititally recognized by OCR. But you won’t know which one. Source:

So how do we solve this problem? Some brilliant minds got to work and “EUREKA!” – they found the solution. Remember, reCAPTCHA! It’s like killing two birds with one stone. Here’s how it works. One of the words presented in reCAPTCHA is a word from the scanned book which was unrecognized by the OCR. The second word in reCAPTCHA is known to the system. The system doesn’t tell you which one is which, and the user needs to type both words. If the user types the correct word for the one known to the system, it assumes that the user is a human being, and gains confidence that user has also typed the other word correctly. The same process is repeated with 10 different people for the same unrecognized word and if all of them agree on what the new word is, then one more word has been digitized accurately.

Have you heard about DUOLINGO? Guess what, it works on the same concept. DUOLINGO helps people learn different languages like Spanish, French, etc. Behind the scene, it helps translate tons of Wikipedia pages into different languages. If you are interested in learning a foreign language, this is highly recommended.

The strength of the model lies in motivating people towards doing something interesting and, in turn, employing their collective power to solve some real problems. If you analyze this carefully, this model lies at the sweet spot of the intersection of technology, economics and psychology.
Reference : Wikipedia & Ted

(End Quote)

Do you know of other areas where this kind of cooperative effect plays between something that you need to do and while doing so helping the community?

Kaphil wonders: “in a 100,000 person company if everyone gives just 1 minute of his/her time in a day, we have around 200 person days effort. So we have the scale to solve big problems. Let me tell you something about this magic figure – 100,000. It’s said that all these missions – building the pyramids of Egypt or the Panama Canal and putting a man on the Moon – employed this magic number. Now the challenge is to find something meaningful which drives each one of us to give our 1 minute and in turn ‘Make the Mountain Move’.

Would you share any ideas about new areas where this concept could be taken further? Just enter a comment below.

Personalized Medicine and Big Data

July 30, 2013

Modern drug ampoules

Modern drug ampoules (Photo credit: Wikipedia)

While many doomsayers describe the Pharmaceutical industry as one where the golden days are over and where more and more enterprises are bound to be falling off the “Patent Cliff” or get lost in space, many rather visionary companies have ignited their rocket boosters and are catapulting themselves onto firm ground again. This rocket booster consists of treatments for rare diseases and Personalized Medicine. The two chambered booster combines especially targeted diagnostics with targeted therapies and utilises cloud services alongside with Big Data analytics. This new method of transport brings with it new demands on research, clinical trials, production, logistics, information systems and the overall business model.

The pharmaceutical industry has already seen some rapid advancement during the last decade and a half. Who would have thought back in 2000 that in just 13 years researchers would have the capability to gain access to patient’s genome data and tailor treatments for specific genotypes and their specific diseases? Or that they could sift through large amounts of data to detect correlations in drug actions that could be beneficial in other disease contexts (drug re-purposing)?

However, with the advancements have also come challenges, with pharmaceutical companies facing unprecedented difficulties, including shrinking pipelines, early loss of patent protection due to long development and clinical trial times, increase in regulatory bylaws as well as novel demands on compliance and governance.

These challenges and more importantly how Pharma companies tackle them, are shaping the future of the industry, with some key trends already emerging:

  • To solve complex tasks, “coopetition” with other companies will become the norm
  • The journey towards Personalized Medicine
  • Race to Biologics
  • Pairing of drugs with their accompanying diagnostics
  • Stricter regulations on compliance
  • New demands on privacy and security
  • Big Data approaches yielding new insight into drug action correlations

These challenges put new demands on governance, processes, business models and the information systems, which will build the foundation for these new endeavours. New trends in technology adaptation will support and enable these objectives.

From Competition to “Coopetition” and Cooperation

Coopetition — Source:

Whereas in the past, companies kept all operations within their own perimeter; budget limitations, increasingly complex research and development tasks and a lack of available resources will lead to a trend in the future, where increasingly large areas of the drug discovery process will be outsourced to third parties. This may include outsourcing to

  • Small and flexible small research companies where risk taking is easier than in a large corporation;
  • Academic bodies where publicly financed research programs can focus on long-term search for new action mechanisms, drug classes, genotype/phenotype correlations and new substances isolated from natural sources like tropical forests or marine organisms – including the genomic sequencing of identified organisms.

But it may also mean that competitors team up to share the burden of research, something which is already being done in the Malaria research sector. With more companies working together the lines between competitor and partner will be blurred, but the need to maintain competitive advantage will be just as strong.

Simultaneously, an industry wide trend will kick in where rigid hierarchical organizations are giving yield to more loosely coupled competence centers, which will provide their services “in house” as well as on the market. We have seen such trends primarily in areas outside the companies’ core business. Many companies have out-placed their IT operations forming a more or less independent company serving also others in the market.

The challenges for the information systems that will have to support such business models can be answered with approaches including cloud computing, sophisticated identity and access management, and collaboration spaces where birds of a feather from different companies and organisations can securely collaborate in a protected environment. Social media tools (forums, blogs, chats, etc.) will increasingly be used to enable true collaboration in a secured environment in such settings.

Rise of personalized pharmaceuticals

Drugs will become less generic (and low-cost) and more tailored for specific demographics in the future (and more high value/high cost). There are huge benefits to this, including the fact that these drugs are harder to copy so they retain their value better for the producer and they are also more effective as they can meet more specialist needs. While the current trend is looking at groups of people, maybe based on age or ethnicity, this will evolve in the 21st Century into truly personalised drugs on an individual basis reflecting the patient’s genetic predisposition.

The author's depiction of Personalization in the pharmaceutical and health care indiustry. (c) H.Broda 2013

The author’s depiction of Personalization in the pharmaceutical and health care indiustry. (c) H.Broda 2013. Click to enlarge

We are currently observing a significant shift towards both biologics and diagnostic-guided treatment. This increasing personalisation of therapy will significantly benefit patients, by both raising efficacy and reducing side-effects. In order to maximise success in this new era, companies will have to revamp their processes across the entire value chain.

The race to Biologics

Biologics–symbolic picture. Source:

Many common classes of drugs have been shown to be effective in less than half of patients. A new generation of therapies is moving in. Many are biologics, created by biological processes rather than being chemically synthesized, making them much more difficult to copy. Biologics are also likely to dominate over the next years, since legislation allowing bio-similars (unbranded copies of biologics) has only recently been introduced, and bio-similars will have to be marketed as new drugs, unlike traditional generics.

Since biologics require cold-chain distribution, have relatively short shelf-lives, are expensive, and may require a molecular test that also requires cold chain, a highly nimble, secure and traceable supply chain is required. This becomes even more important as companies set their sights on emerging markets, where warm climates and large distances may present additional challenges. Cold-chain needs a multi-functional team including regulatory, quality and logistics, working together with external partners. For example, specialised logistics companies are now providing temperature sensors that detect and transmit data on temperature excursions.

‘Just-In-Time’ and potentially ‘Between-Centre’ product delivery can ensure optimal use of expensive inventory. Compliance with tracking and authentication regulations may be required (for example the E-Pedigree in California, and bar code Track and Trace in Turkey). Deployment of more advanced tracking and anti-counterfeiting technologies may also be considered for these high-value products. Bringing this all together into a robust distribution information system is essential. For emerging markets, comprehensive but simple distribution tracking systems, which provide secondary sales visibility, have successfully been deployed with Consumer Packaged Goods companies, and may be adaptable for pharmaceutical and diagnostic products (1).

Drugs and their diagnostics walk hand-in-hand

Walking hand in hand. Credits: Elora Daphne on Flickr

As therapies are becoming more and more targeted to smaller groups the categorization of patients into such groups is of crucial importance. This will lead to a significant increase in diagnostic capabilities and lead to a change in business models: Today most diagnostic companies are completely independent of the drug producing companies.

Tomorrow we will see drugs packaged with their prerequisite diagnostic sets and many drugs will require a diagnostic test confirming the classification of the patient into the predetermined recipient group for this drug. Pharma might benefit from shifting to a model where drugs and accompanying diagnostics will be co-produced, co-marketed, co-provisioned by the distributors, and sold together at pharmacies and health care providers.

The logistics and information systems supporting such business models will have to be adapted. Frameworks that will support such co-developments of drugs and their diagnostics will need to be based on new collaboration models and sharing of processes, procedures and information.

Regulations on compliance set to increase

Despite the fact that companies are already struggling with existing regulations, the increase in patient data and increased sensitivity of patient records mean that regulations are set to become stricter. The impact on the industry will e two-fold:

  • Requirements on document management, audit traceability, and policies will increase leading to a
  • Demand to hire more people to manage or adopt specialist systems to deal with the volume and complexity of regulation

The increase in sheer data volume in context with Big Data analysis and the handling of privacy sensitive patient data at massive scale will require new procedures, management models and tools that can reduce the load by automating the standard processes and providing relevant information at the right place at the right time.

Increasing demands on privacy and security

Privacy and Security. Source:

As more partnerships and cross-company collaborations form, the security requirements for the industry will be tightened while perimeter security, which we could count on in the past, all but vanishes. This will force companies to reconsider their security priorities. In the future, protecting assets cannot be based on protecting the entire data centre any longer. We will have to protect and control access rights to applications, data, and networks on the fields and islands of collaboration.

Patient data from masked electronic patient records will serve as one of the major sources for Big Data analytics. But trust of the public into such systems has to be earned and managed. While organizationally and technically such obfuscation of personal identifiable information (PII) can be achieved with relative ease due to available methods and solutions, winning public trust in such systems will require an open information policy and public discussions at every level in society. Pharma has to gain a lot from access to electronic patient data and will have to start to engage in building trusted reliable data stores for electronic patient records and become an active member in the public discourse on such endeavors.

To achieve this we will need to implement advanced identity and access management systems that will deal with contributors’ identities, roles in the system, authentication, authorization, and policies on governance while enabling audit tracks to adequately satisfy compliance regulations.

Future innovations will be based on Big Data

Big Data. Symbolic picture at

Though already a trend we can currently observe, the pharmaceutical sector will see rewards in decisions being made more accurately and more effectively thanks to improvements in scalable cloud based information processing, applications and data storage. This new compute model is providing novel analytical capabilities, which utilize and harvest information presented as structured as well as unstructured data. Industry refers to such previously generally unavailable data as “Dark Data.”

Such Dark Data may include sources from (a) research lab notebooks—especially from experiments that did not yield the expected or a (sometimes surprising) different result; from (b) sifting through literature sources detecting side effects in published literature using semi-automated text analysis methods; from (c) sources such as de-personalised (anonymized, pseudonymized or masked) globally accessible patient data depositories (electronic patient’s records).

Analysis with appropriate methods and tools will allow the pharmaceutical industry to unveil correlations between treatments and results with potentially huge sample sizes yielding significant correlations hitherto unattainable: Most “real life” data (on medical conditions, co-morbidities, treatment, response, side-effects) today reside in the healthcare system, in patient notes (paper and electronic). These data, combined with those from clinical trials, could generate valuable novel insights.

In a few years it will become a standard practice to have one’s genome sequenced as cost will come down to the equivalent of a dentist’s check-up visit. The genome sequence might then be stored in the individual’s electronic health record or put (privacy protected) onto a research accessible data base. With 30.000 genes per individual and millions of individuals’ genomes in data stores genomics researchers will have access to billions of genetic markers to better understand correlations between disease and classes and types of patients. Big Data is becoming huge.

To achieve such flexibility in acceptance of types of information for analysis information systems must enable access to such information for those empowered from anywhere and any future end user devices. Information has to be presented such that easy access while preserving the original semantics of the data can be achieved. The focus will lie on discovery, integration, analysis and exploitation of this vast information.

The success of Big Data technologies will depend on natural language processing capabilities, pattern recognition algorithms for image and video sources and on new statistical analysis methodologies, large storage capacities in the cloud and advanced search technologies surpassing the capabilities of HAL in “A Space Odyssey,” which will allow us to find the literal information needle in the haystack of data.

These are interesting times


The pharmaceutical industry will see significant changes in their business models where cooperation balances competition, where networking with others will become the standard model of operation and where the patient is not just consumer but partner.

The health care system will move from a repair shop business to a managed health system where diagnosis and prevention will become as important as therapy.

Specialization to small but high-value niches will be the path leading away from the “Patent Cliff” and co-marketing of drugs and diagnostics will become more and more standard. Big Data analytics will help to find correlations overlooked so far due to relatively small sample sizes and research data will be revisited yielding hitherto hidden drug action mechanisms.

Focusing on the core business and partnering with other companies and industry expert service providers will characterise the winners in this new century – those who did not get lost in space.


  1. Currie E, Broda H., (2013) The Key to Personalized Medicine Success. Pharmaceutical Executive Global Digest, April 2013
  2. This blog is based on an earlier version of this article:
    Broda, H. (2013) Big Pharma 2.0: What Does the Future Hold? European Pharmaceutical Contractor, June 2013
  3. Broda, H. (2013) Big Data Trends–A Basis for Personalized Medicine (Presentation on

Dark Matter in the Enterprise Universe: The “Dark Data” Opportunity

March 5, 2013

Big Data

Big Data (Photo credit: Kevin Krejci)

The “Big Data Tsunami” was the theme of my last post. Today I want to share with you another angle on how to look at Big Data. This angle reflects more the way physicists look at dark matter in the universe: It is there, we can calculate the mass but it eludes our possibilities of observation.

Dark Data is the untapped mass of under- (or un-) utilized data whose existence is widely unknown or unrecognized in business of all trades. But these dark data might contain valuable information if we were only able to tap it.

The problem lies in the fact that most of this Dark Data is present in unstructured (free text descriptions, free text observations, notes, etc.) or non-textual formats (pictures, videos, audio files and more).

Infosys has recently announced their BigDataEdge platform that will radically simplify the task of analyzing Big Data. They published an infographic that nicely explains the issue (see below). Many companies are developing similar systems that will allow businesses to gain valuable information from all these data, which are today hidden in the closet.

The challenge is in how to formalize unstructured data. The Infosys approach includes (quote from the announcement):

  • A rich visual interface, with more than 50 customizable dashboards and 250 built-in algorithms. These algorithms, a set of reusable business rules both function and industry-specific, enable business teams to self-serve the process of building insights while minimizing the need for technical intervention
  • Over 50 data source connectors, which allow easy access to structured and unstructured data residing across enterprise and external sources. This would enable acceleration of discovery of relevant information from existing, underutilized data
  • A powerful collaboration wall and pre-built workflows that allow teams across functions to interact on insights and collectively implement decisions
  • A Logical Data Warehouse providing a virtual data management architecture, eliminates the need for physical availability of data to build and test insights
  • ‘Out-of-the-box’ applications for specific industry needs such as fraud detection and prevention, predictive analytics and monitoring, and customer micro-segmentation that deliver faster returns on investment 

(end quote)

But how do we make all these data accessible? One approach is to have all the data in a virtual data center, a. k. .a Cloud. This way system to system interfaces are not coming into the way of data aggregation.

Just marvel with me what this could mean for research in mining existing research data, observations, notes in lab journals from all the experiments that were filed away since the results had not corroborated the original hypothesis. Would you agree that undetected gold nuggets are still buried  in the mud of unstructured information? Imagine that all this information could be tested against new hypotheses, could be checked for weak correlations and connections undetected before.

We are only at the beginning of a new development here. More interesting inventions and innovations lie ahead of us.

Where do you see interesting new developments coming in this space?

Infographic on Dark Data

Infosys’ depiction of Dark Data (link to the original web site–click to enlarge)

Data Are Growing Up: The Big Data Tsunami

January 29, 2013

Visualization of all editing activity by user ...

Visualization of all editing activity by user “Pearle” on Wikipedia (Pearle is a robot). To find out more about this project, go to: (Photo credit: Wikipedia)

When our information systems started to grow and a few tables were not able to hold the (mostly numerical) information we started to build databases.

When databases became too small to contain all the information we were dealing with or the data were distributed in different (not so compatible) data stores we invented the data warehouse.

But to fit into these warehouses data had to be structured. Life though is different and today we are dealing with tons of poorly structured and unstructured data. So here is the latest trend: Big Data.

The industry successfully termed this as a new buzz word and — as always when a new buzz word hits the market — definitions of the term are different be company, speaker and region (here in Switzerland by canton).

Raj Sabhlok wrote in Forbes: “For example, most organizations have their data in structured relational databases like Oracle, but much of the data generated today is unstructured, high-volume web data or machine data. Technologies like Hadoop and “NoSQL” databases, such as Cassandra and MongoDB, are better designed to support massive data processing and storage. Emerging technologies such asStorm and Kafka are designed to provide real-time streaming analytics, which is critical for volume data feeds such as social networks. Even ad-hoc query tools such as Dremel have been introduced to support Big Data environments with low latency.

“Big Data also brings new skill-set challenges. As companies look to answer the most relevant questions related to their businesses, they will need data analysts or “data scientists” to mine the data. And they should get started soon; according to a recent McKinsey study, the United States alone faces a shortage of up to 190,000 workers with analytical expertise, as well as another 1.5 million managers and analysts that have the skills to understand and make decisions based on Big Data analysis.

“The Big Data movement is the recognition that there’s “gold in them there data stores!” There are tons of real-world examples of Big Data done right — just ask President Obama. However, it’s not something to dive into without first doing some serious soul-searching about your company’s goals. And it’s definitely crucial to have the right tools to support your unique corporate needs. But as professor Clemen always used to ask, “What would you pay for perfect information?”

Dilbert on Big Data

Dilbert on Big Data (image at

One of the newer methods to introduce new terms and to explain novel concepts has been the use of infographics. You can find several such examples in this blog when you enter “infographic” as a search term in the rightmost column.

Infosys just published one of those infographics on Big Data in the enterprise. I like this graphic since it understandably explains the concepts behind the buzz word  (click to enlarge).

Big Data Infographic by Infosys

Infographically speaking: big data in 2013– By Rajeev Nayar (click to enlarge)

Another useful infographic on Big Data was recently published by Muhammed Saleem: Big Data and the future of our health. He maintains that medical diagnoses, general patient care, and medical practices are often more expensive and inferior than they should be. Big Data could revolutionize healthcare by replacing up to 80% of what doctors do while still maintaining over 91% accuracy. The graphic is displayed at  (click to enlarge).

Big Data in Healthcare

Big Data in Healthcare. From the page mentioned above (click to enlarge).

The importance of Big Data analysis has recently been reported in the context of President Obama’s re-election. Crovitz wrote on Nov. 19, 2012 in the Wall Street Journal:

When the Obama campaign emailed supporters to join a $40,000-a-ticket dinner in June at the New York home of actress Sarah Jessica Parker, journalists at ProPublica noticed something odd. They uncovered seven versions of the email solicitation for the fundraiser, some mentioning a second fundraiser that night, a concert by Mariah Carey, others that Ms. Parker is a mother, and still others that Vogue editor Anna Wintour would be at the dinner.

Who got which email depended on “big data”—information about each fundraising prospect and how different people react to different messages. In this year’s election, it looks as if the Obama team’s use of such data was one of its biggest edges over the Romney effort.

[ . . .]

The Obama campaign focused on data showing the “persuadability” of voters. Multivariate tests identified issues and positions that could move undecided voters, ProPublica said: “The persuasion scores allowed the campaign to focus its outreach efforts—and their volunteer calls—on voters who might actually change their minds as the result. It also guided them in what policy messages individual voters should hear.” (Read the full article here)

Big data hold a so far untapped potential. Pharma companies will have to deal with a massive data deluge when comparing genome information of thousands of people to find patterns that correlate to certain diseases and give clues on possible medications.

Are we becoming more transparent? You bet. But we have to learn to mask data in a way that it ceases to be personally identifiable information (PII). See my blog on “You Have Zero Privacy Anyway — Get Over It” (Really?)

Do you want to share some insight or other infographic on the subject? What is your take on Big Data?