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Multi-professional approach to Data Analytics

March 21, 2013 Leave a comment

Data analytics, Enterprise Intelligence, Continuous Assurance, Regression Analysis, Data Life Cycle are terms that you may hear when discussing potential approaches to addressing the Big Data Challenge. Unfortunately, the term “Big Data Challenge” is a misleading one, for it implies that there is only a single problem that needs solution, while in fact there is a number of unique circumstances that companies face, each requiring its own tailored approach. In this post I will aim to highlight the main areas of concern for Big Data specialists and some of the tools that have been developed to address these problems.

Before we begin, it is important to understand that a number of professions aim to fill the need for data analytics capability in business. Accountants, Actuaries, Internal Auditors, External Auditors, Statisticians, Mathematicians, Data warehouse specialists, Programmers, Risk Managers and Consultants, all of these professionals feel the need to contribute to the discussion. As you can imagine there is a great variety of problems faced and each profession has developed its own set of tools to cope with these challenges. Many of the professions struggle to adapt, in many cases statistical analysis has become more prominent, with Statisticians and Actuaries taking a lead and fewer professionals in the accounting field or consulting having the necessary skills. In other cases, professions come into conflict , with some professionals feeling that their domain is being taken over. As such, there is no single way to distinguish underlying domains of the Big Data Challenge, but I will try to do my best to reconcile various views.

What is the Big Data Challenge?

Most commonly, Big Data is described as a an explosion in the amount or frequency of data generated by modern enterprises. However, this is not a useful definition for it describes only the fact of occurrence and not repercussions of such a change.

I would postulate that this data explosion affects us in the following ways:

1. It is harder to find relevant information now, than when data was less abundant, because we need to dedicate more resources to searching.

2. It is harder to ensure consistency and compatibility of records, than when data was less abundant, because there are more ways in which data is collected.

3. It is harder to detect meaningful patterns within the data, than when data was less abundant, because the volume and speed of transactions require additional processing capabilities.

What solutions are out there?

As you can imagine, each organisation has its unique challenges, each challenge has several solutions, depending on the type of data, urgency, market conditions, and even people involved. As such, it is very difficult to create discrete rules that would classify each type of problem and advise a particular solution. This framework is aimed to be a rough guide, rather than a prescription.

1. Getting data warehouses in order and enabling easier access

Believe it or not, but data storage, data accuracy and ease of data access have been a topic of discussion in the computer science profession for decades. Database structure has had a considerable evolutionary history over the past 50 years. In short, databases became quicker, more tolerant to errors and more flexible. Unfortunately, not all organisations have cutting edge databases. A great variety of legacy systems and improper ways of using existing systems introduce number of errors into the datasets, errors that need to be remedied if further analysis is to take place. The explosion in data volumes exacerbated the situation by placing additional volume strain, as well as accuracy and operational requirements (as, for example, is the case for distributed databases). A number of new and established firms responded in a variety of ways to this challenge, either by developing new database technologies or by dedicating more processing and accuracy verification resources. This area has traditionally been addressed by IT professional.

Further reading on this topic can be found here.

2. More advanced and specialised search engines

In a way mini Big Data problems have been around for centuries. When the first printing press was invented, an explosion in print media warranted creation of libraries and subsequent catalog systems. Similar experience gave birth to phonebooks. And Google, in its brilliance, brought order to the informational chaos of the early Internet. Since then several new technologies emerged in order to tackle the challenge of finding the correct piece of information within a cluster of related data. Examples of companies involved in this field include IBM (and its famous Watson Computer), Sinequa (with its unified information access tools), and Recommind (with automatic categorisation tools), just to name a few. Each approach uses different underlying technologies, and if your Big Data problem falls into Search Engine category, you need to do additional research to understand which technology would work best in your circumstances.

3. Pattern recognition and detection – new and old data analysis techniques

Another domain of Big Data is the need (or an opportunity?) to detect patterns within the data with a view of making forward-looking predictions or to detect anomalies. A range of situations where this capability might be useful is virtually limitless and varies from pricing, customer management and production planning to fraud detection and equipment monitoring. However, methods that address this issue fall into three main categories.

First method is data visualisation. This method is very intuitive and appealing, since we can perceive visual information very rapidly. Majority of data visualisation techniques focus around enabling rapid prototyping of visual models, some examples can be found here. These techniques allow to pinpoint outliers and trends, but rely heavily on personal interpretation. Additionally, not all phenomena can be expressed in a visual way, with some patterns taking form of multi-dimensional multi-order relationships. Furthermore, a great deal of training and experience is needed for these visual models to be used correctly. Human brain excels at finding visual patterns, however in some cases it is susceptible to finding false positives, Astrology being one example.

Second method is mathematical modelling. This approach leverages a number of well-known statistical techniques starting from various types of regression and drawing heavily on differential equations. This approach has proven to be effective in a number of applications, such as its integration with ERP systems. However, it is very expensive and complex to implement. The level of mathematical expertise required and specialised nature of the models often restrict application of this method to a high value and high impact projects. Furthermore, most models of this type have limited dynamic flexibility, and if underlying relationships change the model becomes obsolete. As such this method is most appropriate for specialised application in relatively stable environments.

Third method is automated software modelling or sometimes called artificial intelligence modelling. Instead of hiring a team of mathematicians to build a model, several companies are developing software packages that are themselves capable of  choosing what factors are most important in modelling a particular environment. Most notable example of a company engaged in this area is Numenta. While this approach can be orders of magnitude cheaper, compared to traditional statistical approaches, its usefulness rests with high velocity temporal data applications, such as modelling electricity usage, credit card transactions or monitoring equipment status. This software is also capable to dynamically adapt to underlying changes in relationships within data.

Final words

As can be seen from the above list of solutions, the Big Data Challenge is a fragmented problem. Each particular situation demands careful problem classification and selecting appropriate tools to address it. I believe that these tools fall into the three categories described above and that each category is experiencing rapid evolution. The challenge facing many businesses today is navigating through this complex environment, and hopefully this article helps them to do so.

~Alexey Mitko

Starting your business is someone else’s business

August 18, 2012 Leave a comment

The title of this blog post is a paradox. On one hand, trying to start a business is a personal adventure, marked by high aspirations and a notable lack of funds. On the other hand, some entrepreneurs figured out that helping other people to start their business can be a business in itself. And that fact adds complexity to the whole process. Our would be entrepreneurs have to distinguish between people who offer genuine help or good value for the money they ask and people such as themselves, who are just starting their business and at the moment cannot truly help would-be entrepreneurs.

For example, you find a person who was able to start a somewhat successful startup and pay them $300 to speak  your event (or even better: get them to do it for free to promote their business), you rent a room for 3 hours ($300), arrange catering ($200), advertise ($200) and sell tickets to 40 wannabe- entrepreneurs for $50. The profit is $2000-$1000=$1000. Considering young entrepreneurs are very eager to chase their dreams, and are more than willing to pay $50 for a promise of networking and “startup tips”. But the real question is do you get value from your money? Or are you draining your start-up funds and waste the most important resource: your time?

I would argue that an entrepreneur should pay only for tangible services like legal services, marketing services, or technical expertise. Networking clubs and startup tips can be found online and for free. The mere fact that someone fills the room with entrepreneurs does not mean that this event will give you the solution to your business, most likely it will leave you $50 short and wanting to pay for other events of this nature. The point I try to get across is that other people made it their business to sell services to people who seek to start their business. The lesson that many entrepreneurs forget is that some people are not trying to help you, but to make money from you. But as an entrepreneur you must be frugal, since your resources are very limited.

Check this website for more startup tips http://frugalentrepreneur.com/

Source: Cartoonstock.com

Alex

Innovate your workplace

April 19, 2012 Leave a comment

I guess everybody knows the feeling you get one day before the due date of an assignment at uni. It is often combined with an increased level of stress from the need to pull an all-nighter to finish the task and meet the timeline. I admit that I have been one of those last-minute crunchers, even though I managed to finish a couple of days early every once in a while.

However, I think everyone agrees that most study lounges with artificial lighting don’t create an atmosphere that fosters creative working. In this post, I don’t want to talk about the issue of time-management, but rather about creating a working-environment that stimulates creativity and innovation.

Winston Churchil said:

“We shape our buildings; thereafter, they shape us.”

Following this quote, it is important to pursue a creative workspace-design to pursue those requirements. From my own experience I can tell you that it is sometimes useful to change locations if possible, even though your workplace might be innovation-itself, to stimulate creativity.

Since change of location is not always an option, because there is a limited amount of available spots around and your dependence on local infrastructure (like IT and stationary inventory), it is crucial to create a working environment that supports your creative needs.

Scott Witthoft and Scott Doorley targeted this specific topic in their post “Five ways to make corporate space more creative”. The most important areas to include are posture, orientation (of people relative to each other), and ambience (the tangibles of a room). Disagreeing with Witthoft and Doorley I say that ambience probably is the most important and obvious of those areas, and therefore gets a lot of credit in the first run of workplace-innovation-improvements. It is not always about the major and cost-intense changes like refurnishing the room with whiteboards and adapting a Google-like environment. Even fairly simple changes like the lighting in your office might have a huge impact on the working-atmosphere. From my own experience, I can tell you that it is much more pleasant to work at a warm-lighted desk, rather than at a cold-lighted, even though cold-light is known to reduce tiredness.

Also the posture is important to your creative outcomes. Witthoft and Doorley stated “[they]’ve noticed time and time again that an upright posture encourages people to stay alert and engaged in problem solving, while a comfortable, ‘lean-back’ posture often turns people into passive critics.” Therefore they suggest to use stools in a seminar-configuration rather than chairs to keep participants stay involved in the discussion. Again, I’ve experienced this myself, when I recently had the opportunity to work at a desk that was high-adjustable. Stand-up working for an hour or so every once in a while was a welcome variation that I used quiet frequently and noticeably increased productivity and courtesy.

In terms of people’s orientation I think offices already implemented a standard. The majority of the ones I’ve visited so far with multiple people working in them, had two desks facing each other with a computer-screen right in the middle of them avoiding direct eye-contact of the colleagues. Personally, I like this configuration; maybe just because I’m used to it. Even though, I would argue that a configuration of desks opposing one another, might enforce the creative-process since it is much easier to quickly look at your colleagues screen to double-check something or to request input.

Returning to my uni-classmates I have to say that I’ve always been amazed to see how different people worked creatively in the most diverse working environments. I support the idea to work in a coffee-shop or on a bench in a park, every once in a while, I’d even recommend it. Since this is not always possible I encourage you to think about what enforces your creative-edge.

What would make you more productive and innovative at your workplace? Don’t just think about the big changes. Also think about minor details that might stimulate you. Sometimes even a photo of your beloved ones might be enough.

Think about it and take small steps to implement your ideas. And tell us…

Sebastian

Baby Techs with Muscle

The world changes rapidly. A few years ago online newspapers and tablet computers were not a big deal, today they are nearly as important as breathing. But technological progress does not stop here, it keeps moving, quickly and  unrelentlessly, pushing forward to new and fascinating destinations. Here are my five favorite baby techs that could have a world changing impact:

1) Raspberry Pi
Status: Small scale industrial production (10,000+ units)
‘Raspberry Pi’ is a $25/$35 programmable computer. The vision that drove this product is allowing kids to learn real programming on their own (not just making of web pages as most kids may try now) and at a reasonable price. The result is ‘Raspberry Pi’ a small computer, capable of quite some muscle. The $35-version has an ethernet port, HDMI Video, RCA Video, USB 2.0. It runs at 700Mhz and has 256MB memory. Sure these stats are not as impressive if we consider that most of our phones run 800Mhz+ (for example Apple A5), but at $35 Raspberry certainly looks like a yummy hobby.

2) MakerBot Industries
Status: Medium Scale Production (>100 Units)
MakerBot is an affordable 3D printer. Well it still costs $1,200 and the more advanced 2 color model is $1,700, but this is well below the tens of thousands of dollars industrial 3D printers cost. What can it make? Well almost anything. It uses a plastic filament that is heated up and moulded into your 3D design. The result is a plastic figure the size of a large coffee mug. These guys also run a website called Thingiverse, where users can share their 3D designs.

3) Numenta
Status: Limited Scale Application
Numenta is a company started by Jeff Hawkins and Donna Dubinsky. It aims to develop machine intelligence. But wait! It is AI; but this term has been sooo overused that it lost all of its meaning. We are not talking about evil or friendly robots, nor we are talking about self-aware machines, instead we are talking about computer programs that can do human tasks, lets say hmmm… distinguish between a cat and a dog in a picture? This task takes a human less than a second to do perfectly, but for current computers it may take hours and they might not succeed at all. Is it because they are not fast enough? No, Jeff Hawkins, argues that it is because we are not using the right approach. Current AI uses calculation as means of achieving its objective. Humans  however, don’t calculate, they predict and approximate, and this is what Numenta is trying to create with the help of Temporal Hierarchical Memory. More can be read in Jeff’s book On Intelligence.

4) Focus Fusion – Lawrenceville Plasma Physics
Status: Experimental
Fusion is the process of combining atoms under tremendous heat and pressure forcing them to fuse. Some of the matter in the process is converted strait into a large amount of energy. In contrast, atomic bomb works by splitting large atoms (namely Uranium) into less massive atoms, releasing a bit (compared to fusion) of the energy. Humans are already doing quite well in terms of nuclear fission. We have many nuclear power plants around the world, but they are problematic due to the production of nuclear waste and radiation. Fusion can be the answer, since this reaction produces more energy, no waste, and only tiny bits of radiation. The hindrance to developing this approach is in the fact that you need to heat up your material to millions of degrees and once it becomes plasma, there really is no way to keep this super hot material contained and still hot (magnetic fields can keep material contained in all but one direction). The beauty of Lawrenceville Plasma Physics is that they exploit plasma instabilities, instead of trying to contain them. As such, their machine costs $150,000 – $300,000 to make, instead of millions required for a tokamak reactor. Why Focus Fusion approach is not developed more and millions are pumped into tokamak? Beats me!

5) New Organ Mprize
Status: Theoretical
Methuselah Foundation aims to advance medical technology and eliminate or even reverse age related degeneration. It does so by creating prizes awarded for milestone achievements in medical technology. One of such prizes is the New Organ Mprize awarded to a team of researchers who can either store an organ for 30 days or create a synthetic organ from persons cells. Since reprogramming adult cells into stem cells and creating a particular kind of tissue is quite easy nowadays, the next step would be creating a whole organ with multiple types of tissue and blood vessels interconnected. This is what Mprize hopes to achieve. Is it possible? Certainly! The real question is “How soon?”

Categories: Alex, Healthcare, Innovation

The power of data

January 26, 2012 1 comment

Within the Healthcare sector, innovative organisations are developing unique approaches of managing data, enabling businesses to make more effective business decisions.

Ginger.io mines and interprets data collected through smartphone usage. The business concept was founded during a PhD study by the Massachusetts Institute of Technology in 2009. During the study students were given smartphones which tracked their behaviour over the next few weeks: where they went, who they texted, and the frequency of calls and texts.

Data obtained during the study indicated when students were ill by signalling when individuals derived from their typical behavioural patterns. This change in behaviour accurately predicted cases of flu and depression among the student body.

The technology supplied by Ginger.io and similar innovative businesses enables the capture and synthesis of large scale real-time data. The scope of the application of this technology is vast and Ginger.io are currently developing applications that can be used within the healthcare, pharmaceutical, and insurance industry. But the technology’s application could extend far beyond these areas.

The technology itself is derived from a branch of computer science called machine learning. Using this approach, applications are built with algorithms allowing computers to predict behaviours based on data obtained through devices – in Ginger.io’s case, smartphones.

As well as the Healthcare industry, this business concept is becoming increasingly more critical for the insurance industry. The insurance industry is data intensive, and organisations are having to deal with continually increasing volumes of information, and at the same time, increase the speed of decision making.

Big data is a term that refers to the massive amounts of data businesses are having to deal with. A study by McKinsey has shown that an organisation optimising their use of big data can improve margins up to 60%. Using technologies, such as Ginger.io’s, organisations can aggregate large data populations and subsets with increasing efficiency. Patterns and trends can be found, and as such, inferences can be made among specific consumer groups allowing businesses to identify groups of individuals at higher levels of risks on a number of different issues.

Accenture have predicted a number of Insurance Industry Technology Trends for 2012, which purports that the use of analytics to gain customer insights will become increasingly important for insurance companies this coming year. Innovative applications, like those developed by Ginger.io will be critical for large insurance organisations to gain and sustain a competitive edge, which is especially relevant in the more developed markets where competition is fierce.

Organisations are already exploiting the data intense nature of the insurance industry. Bupa for example owns Health Dialog, who are a care management, healthcare analytics and decision support company based in Boston, USA. Among the range of business services they offer are analytical solutions which allow their clients to delve into the deep depths of their customers data. Health Dialog’s services can be leveraged to identify cost-savings and areas which could potentially lead to business opportunity.

This increasing challenges being fueled by a data intensive environment also demands specific skills – researching, analysis, synthesis and so on… There has been a proven correlation between grasping big data and business success, so for the foreseeable future organisations will be seeking individuals with proven skills in this field.

Many industries will be responding to the current economic downturn by engaging in cost cutting and efficiency improvements. The bottom line for these organisations, and those situated in more prosperous climates, is that enhanced performance in data analytics allows for the acceleration of data-driven decision making and can facilitate a more accurate understanding of the customer base.

This post is based on an article from Business Week Jan 9-15 2012, titled “The Nurse in Your Pocket”.