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Before you embark on a detailed enterprise planning process for Big Data, here are 11 critical questions that will help you set your strategic framework:

1. Does your company have a clear view of how Big Data and Intelligent Value Creation is likely to reshape your relevant markets over the next five years?

2. Does your company have an equally clear view of the implications for the changes you will need to make to continue to create value?

3. Are these views shared effectively among your senior managers across the organization?

4. Does senior management recognize the risks and uncertainties as part of the decision-making process?

5. Has your company been sufficiently aggressive in using Big Data and Intelligent Value Creation to enhance your operations?

6. Are there opportunities to use Big Data and Intelligent Value Creation to improve operations around existing products and services?

7. Are their opportunities to use Big Data and Intelligent Value Creation to significantly reduce costs and cycle time in existing work processes?

8. What are the data sources?

9. How will you monitor them?

10. How do you trigger events based on the intelligence gathered from the data?

11. Is there a growth or cost-savings optimization opportunity that is impacted?

Not answering these questions will cause problems down the road (we speak from experience).

The hype surrounding Big Data is inescapable. Fortunately, we are starting to see real world examples of business value to justify the investment. So how does an organization get started? A recent article in the McKinsey Quarterly makes the case for Big Data Planning. The "missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value."

Furthermore, the article states:

In these early days of big-data and analytics planning, companies should address analogous issues: choosing the internal and external data they will integrate; selecting, from a long list of potential analytic models and tools, the ones that will best support their business goals; and building the organizational capabilities needed to exploit this potential.

If only it were that simple.

As we discussed earlier in our Intelligent Value Creation Maturity Model, the adoption of Big Data Analytics is an organizational journey.  Most companies begin their Big Data transformation led by an individual and/or a small group of like-minded individuals. Often viewed as trouble-makers, these groups should be encouraged and brought into technology strategy meetings and recognized for their passion. Skunkworks and research projects are typically not given the credit they deserve.  What is important is that learning is taking place, and even though it is informal, it is to be encouraged.  A path to the mainstream technology strategy for the company should be mapped out for these sorts of initiatives.

The main point is that without executive participation, Big Data analytics will not become a business priority, period. And most executives are already too busy, overstretched, to take on one more enterprise-wide initiative. The key concept is participation. An executive should take ownership of analytics as an organizational competence, and they need to be aware and supportive of the potential impact data-complete models all accross the organization. This involvement will help shift the enterprise from relying on intuition to data-driven decisions.

What about IT? Is this an opportunity to be make IT strategic? Of course it is, but, as is always the case, this has to be business-driven. As we've seen with digital initiatives over the past decade, Big Data will become a core skill requirement across all divisions, and big data budgets will not all originate out of IT.  To remain relevant, IT needs to think strategically.

So who should lead Big Data Analytics? Who has the skills required to understand the business impact of Big Data?  NeuralDude's advice is to listen to Peter Drucker. He said something like this - find the best individual in your company, and then charge them with making the transformation.

Which brings us back to the plan.  How do you plan for Big Data?  Answer: it has to be an integral part of your business strategy.

By strategy, I don't mean just executive-level strategy.  There has to be an educational process which shows and demonstrates the value to all employees - embedded into your operations.

More on how to build a Big Data Strategy in the next post.

It has been a couple weeks since completing FutureMed 2013 at Singularity University, and the dust is finally beginning to settle. Here are a few observations from NeuralDude:

Daniel Kraft, MD, Executive Director of FutureMed, brought together a powerful cross-section of thought leaders, scientists and entrepreneurs to the third year of this incredible program. "The world of health and medicine is at the cusp of radical disruption, with novel applications and the convergence of fast moving technologies ranging from the digitization of cloud-based healthcare data, to mobile health merging with artificial intelligence, to regenerative medicine and 3D printing," said FutureMed Executive Director Daniel Kraft MD, a Stanford and Harvard University trained physician, scientist and Faculty Chair of the SU Medicine Track.  "By understanding the trajectory and potential of rapidly developing technologies, merged with unmet needs and creative thinking, we have the potential to re-invent many aspects of wellness, diagnosis, and intervention- leading to better outcomes and healthier lives, at lower cost."

There are great strides being made in this community in areas spanning from synthetic biology to next generation healthcare practice.  An optimistic crowd to say the least, we all brought our thinking and leadership to the topics shared during this fascinating program.

Here are two health related factoids for you to think about:

• Americans in the early 20th century ate on average 1 lbs of high fructose corn syrup per year.  Now the average American consumes around 50 lbs a year!  Houston, we have a problem!
• By eating ¼ teaspoon less salt per day, Americans can potentially add 20 years to their lifespan.  

For me, the biggest takeaway was the famous quote, "I have seen the enemy, and it is us!" 

We need to think inclusively and beyond compliance to create a healthier future. David Duncan hosted several experts in an intriguing presentation on the future of personalized medicine; we are very quickly approaching the consumable cost for building the personal genome.  Now only several thousand dollars to get your genome sequenced, we will soon see the personal genome sequenced for under $100 bucks!   The personal genome has not changed our lives yet, but stay tuned!

Techniques in Life Sciences to support the growth of personalization are on the move as well.  MEA (e.g. Multi-Electrode Arrays) support 10s to 100s of individual experiments running simultaneously.  An excellent speech on the correlation of Cardiomyocyte tissues to clinical outcomes was presented at the conference; this will also speed drug discovery and the reality of personalized drug therapies.  Neural ID is involved in this area, providing a solution for automating biosignal analysis of MEA content. 

IMHO, the combination of AI with these new drug discovery platforms will speed the delivery of new drug therapies and revolutionize Life Sciences productivity.

In addition, the personal stories at FutureMed are always awe-inspiring!   In the inaugural year I was deeply touched by the personal story of e-patient Dave.  A man who had been diagnosed with late stage cancer; and through the support of his doctor and the internet; Dave continues to inspire us all with his life experience and "beating  the odds" against cancer.  This year it was Eric Rasmussen who provided an interesting truth about global health in defining the condition of a person in need of health services in poor or developing nations.  "Keep in mind the person you are trying to assist is hot, stressed (e.g. may have been attacked or injured) and hungry".   Open your mind to that, then think about how you solve the complex problems of global health.  I found the information Eric shared for work in creating clean water resource through technology of great interest.  Puralytics has several products to offer including the SolarBag (nanotech in a bag)   - it requires no power other than sunlight and the plastic container can be reused up to 500 times.  Way cool!  Another startup has figured out desalinization with a powered system that can purify 20,000 gallons a day!  Keep an eye on Alrafidane, solving a critical problem for global water supply. Houston, we have a solution!!  I need to get this info over to my buddies at the $300 House project...

Big Data will play a major role in understanding the internet of everything (i.e. sensors) connected to the complex world of global health (all of us).  Exponentials is what its all about! Topics on Artificial Intelligence, IT, Global Health, developing countries, Cancer, Body Sensing, Human Genome, and a myriad of others were delivered in a thoughtful and inspiring way.  The number of variables for each human being is a subject of study in and of itself.  Creating personalized health models that drive behavior and education will make a huge difference in the next 10 years. Focusing on the bigger problems and making significant strides each day.  Check out FutureMed and take a glimpse into a healthy future!  Our global health must be all-inclusive.

Gesundheit!

 I encourage anyone in the Life Sciences or Healthcare communities to make your plans now for next year's conference. 

Related articles

• FutureMed 2013 Day 2: Data, Data, and more Data (medgadget.com)
• FutureMed Day 6: Global Health, Brain Power, and...a Second FutureMed! (medgadget.com)

In a previous post, I defined an Intelligent Value Creation Maturity Model for the Enterprise which looked like this:


Stage  One
In this stage companies are not aware of the potential or capabilities of intelligent value creation. They are not looking or are not interested in seeking ways to use technology to interpret their unstructured data. This may be caused by lack of knowledge of what is available on the market. There may be individuals within the organization who are interested, but they don't view it part of their job or responsibility.

Stage Two
Stage two companies are being led by an individual and/or a small group of like-minded individuals. Often viewed as trouble-makers, these groups should be encouraged and brought into technology strategy meetings and recognized for their passion. Skunkworks and research projects are typically not given the credit they deserve.  What is important is that learning is taking place, and even though it is informal, it is to be encouraged.  A path to the mainstream technology strategy for the company should be mapped out for these sorts of initiatives.

Stage Three
Stage three companies are companies in which specific business units have already adopted the technology and are using it to create value in their operations.  Often there is a collaborative effort underway between R&D and the "sponsoring" business unit. Projects are delivering tangible results, and are measured across business units.
 
Stage Four
This is often the hardest leap to make - from Stage 3 to Stage 4. Most companies that make the leap do so because of a senior executive sponsor in the C-suite. In most cases this is the CTO, but there are examples of company-wide intelligent value creation initiatives led by CMOs and even regional executives. Investments are generally capital expenses as part of strategic IT. HR is actively involved in recruiting data-scientists and engineers who possess the rare skills required in the field.

Stage Five
Companies at this stage have achieved mastery in the discipline of intelligent value creation. They view their investments as a competitive differentiator, and have create multi-year strategies to integrate intelligent value creation into their existing work processes and enterprise applications. Progress is tied to business KPIs.

As businesses adopt Intelligent Value Creation as a competitive capability, we have already explained that the future will lead to an explosion of industry-specific applications.  Some of these will be tied directly to the ERP and back-office systems already in place. Others will require new strategic investments.

Here's what we recommend for companies that are getting started:

• Select the strategic, high-impact areas for the application of Big Data Analytics
• Identify the gaps in the business data flow for these areas
• Implement a pattern-based, Learn-Recognize-Act ® process to "mind the gap"
• Manage your Big Data initiatives as discrete, learning projects to build your organization's capabilities
  

I'm speaking at an event on January 23, 2013.  Sponsored by Burr Pilger Mayer, Tatum, and the California Israel Chamber of Commerce, this seminar will cover how CFOs can use big data to get better results from their companies, including an overview of how big data will enable companies to implement more accurate budgeting.

The success of the New York Times' Nate Silver's predictions has clearly changed the game of political analysis. By correctly predicting results in 50 out of 50 states, Silver has made Big Data a popular phrase, as pundits of all stripes get into the discussion. Silver, to his credit, has even gone so far as to analyze the errors of all the polling agencies. 

And the traditional experts? They are now generally "no more accurate than a coin toss" as Silver says. But what lessons can business learn from Silver's success? What Big Data lessons can we glean from this story?

Here are some suggestions:

1. Challenge your assumptions

It's no longer enough to go with your "gut" feel.  Our personal biases inevitably lead us astray. Start, instead, with the data: what insights can be gleaned from the data you are collecting? Challenge your data scientists to give you their feedback. Listen to them.

2. A method to the madness 

Silver's methodology is simple. And he follows it. Your business will need to establish a process for analyzing the data - a methodology which works for you.  Often, this has to be built from the ground up.  Listen to the voice of the technicians, the lab assistants, the workers on the lines. What can Big Data Analytics do for them?

3. Are there any "unknown unknowns"?

Where are the gaps in your knowledge? Where are the gaps between observation and reaction? As we said earlier, in the real-time enterprise, real-time analytics must be integrated with operational metrics to guide intelligent management of activities and processes, identifying risks and opportunities - taking action before the impact on business value. Mind the gap.

4. Reality first

Get used to being surprised by reality.  It's not personal.  Try to attach yourself, zen-like, to the truth in the data.  All other interpretations are projections of your feelings.

5. The conversation should follow the data

Getting ahead of the data is not wise. Let your actions be dictated by the data and not vice-versa. Acknowledge ignorance, both in yourself and others. Manage it.

For the Nate-haters, here's the 538 prediction and actual results side by side twitter.com/cosentino/stat...

-- Michael Cosentino (@cosentino) November 7, 2012

So where does this all lead to? 

Tom Mitchell, head of the Machine Learning Department at Carnegie Mellon University, says that computer model predictions based on historical evidence are "one of more positive trends we're going to see this century."

Data-driven strategies are the foundation to understanding.  At Neural ID, our vision is built on a pattern-based strategy; Through the combination of deductive and inductive analytics, and a comprehensive view, big data becomes an opportunity for intelligent value creation.

This month's edition of Harvard Business Review focuses on Big Data with three articles by respected authors in their fields:

• Big Data: The Management Revolution by Andrew McAfee and Erik Brynjolfsson

• Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport and D.J. Patil

• Making Advanced Analytics Work for You by Dominic Barton and David Court

Big Data is not simply the latest buzzword to hit the management jargon circuit. It is, as we have been saying for some time now, the future of business competition. 

As Andrew McAfee and Erik Brynjolfsson point out:

"As more and more business activity is digitized, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business. Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations."

While they point out the characteristics of a data-driven organization and discuss the volume, velocity and variety of the data-driven revolution under way, we'd like to add the crucial, game-changing factor we see in our daily business: the use of Artificial Intelligence (A.I.) as a real-time tool to harvest insights from the mountains of unstructured data the aiuthors describe so well.

The management revolution is what we call intelligent value creation.

Similarly, in their article on the talent requirements of the "Data Scientist" in this brave new world of Big Data, Tom Davenport and D.J.Patil tell us:

"...thousands of data scientists are already working at both start-ups and well-established companies. Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before. If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a "mashup" of several analytical efforts, you've got a big data opportunity."

Neural Dude would like to point to a likely source for finding your data scientists - the Office of the CTO. In our work with businesses across manufacturing, consumer and life sciences, we find that the folks most capable of understanding the impact of the Big Data Analytics are already in your company, working on problems at the intersection of business, technology and value creation. The process for identifying your data scientists is the same process you use to recruit the talent in your CTO's sandboxes.

Another point from our perspective: your data scientists may not have to know how to code. That's because the next generation of A.I. tools do the pattern identification for you and can be trained to find the insights you are looking for manually.  So bring us your existing scientists, the women and men in the white coats who already analyze your data signals, and we'll help them improve their productivity with new tools.

Finally, we're told by Dominic Barton and David Court that there's data showing that "companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers."   That is a remarkable benchmark. The authors advise executives to "concentrate on targeted efforts to source data, build models, and transform the organizational culture." We agree, and here's what we recommend:

• Select the strategic, high-impact areas for the application of Big Data Analytics
• Identify the gaps in the business data flow for these areas
• Implement a pattern-based, Learn-Recognize-Act ® process to "mind the gap"
• Manage your Big Data initiatives as discrete, learning projects to build your organization's capabilities
  

P.S. If you need a hand, check out our maturity model, and give Neural Dude a call >>

Like all new technologies that promise to transform industry practices, Intelligent Value Creation has its own maturity model.  The best way to describe this maturity model in layman's terms is to view it as an evolutionary process by which the enterprise adopts the strategic principles, technology, processes, and learning required to improve and accelerate business decision-making using artificial/machine intelligence. 

A sustainable business is “any organization that participates in environmentally friendly or green activities to ensure that all processes, products, and manufacturing activities adequately address current environmental concerns while maintaining a profit.” As businesses search for ways to integrate green practices through their processes and supply chains, we’re seeing a growing interest in using artificial intelligence as a value driver. 

ecoATM: Green Recycling of Electronic Products

One such product is ecoATM, an automated self-serve kiosk system that uses patented, advanced machine vision, electronic diagnostics, and artificial intelligence to evaluate and buy-back used electronics directly from consumers for cash or store credit. Co-founder Mark Bowles says he came up with the idea for ecoATM after reading a trade industry survey which stated that only 3 percent of people had ever recycled their old cell phone. He explains: “… in that year there was about a billion mobile phones shipped, so I thought what was happening with the other 990-millon, whatever, phones?”

The green impact is described as follows:

Almost all consumer electronics (mobile phones, computers, monitors, printers, etc.) contain toxic materials such as lead, mercury, arsenic and a broad variety of other materials that pose a threat to the environment and our health. The first and best thing we can do is to extend the life of existing devices as long as possible so that there is no need to build new devices to take their place. To this end, ecoATM is able to find a second life for approximately 75% of the devices we collect. The next best thing we can do is to responsibly reclaim materials from devices that are truly end-of-life. For the 25% of devices that ecoATM collects which we cannot find a second life, we partner with the best eWaste reclamation facilities in the world to ensure those materials, particularly the precious metals, are reclaimed and reused in place of mining new materials and precious metals from the Earth.

Most consumer electronics and mobile phones are retired when they still have about three quarters of their useful life remaining. Many of these products have been designed to last, but have been upgraded by consumers looking for newer features, performance or size. These used devices generally have value in another part of the market. However, the majority rarely find a second life because the reverse supply chains are not yet efficient enough to serve the market well. This value opportunity is precisely what ecoATM is working hard to serve. ecoATM encourages vendors and consumers to heed three basic principles of sustainability:

• OEM’s must consider the environment when selecting manufacturing materials and design the products for re-use.
• Devices must be used to the end of their functional lives.
• All end-of-life consumer electronics must be mined to extract any toxic materials and reclaim and reuse the precious metals.
  

How it Works



The engineers designing ecoATM were faced with the challenge of creating an exceptional customer experience when implementing their Artificial Intelligence (AI) strategy to recognize consumer devices. Starting with cell phones as the first “device type” with the ability to expand to other consumer devices in the future, the goal of the AI strategy from ecoATM was two-fold: build up a database to recognize cell phones that exist in the marketplace today (an estimated billion recyclable devices are in existence), and have an efficient methodology in place to keep up with the onslaught of new cell phones brought to market by handset manufactures (a daily occurrence).

After evaluating both off-the-shelf and in-house approaches, ecoATM selected Neural ID’s machine learning capabilities as the core of this system.

Here’s what Mark Bowles says about Neural ID: “The real difference that Neural ID brought was a system that could almost infinitely expand to handle a huge database of objects, something that can’t be done with traditional machine vision, and the ability for the system to continuously learn and improve the more we use it.”

The rest, as they say, is history.

Related articles

• Eco-ATM: A Kiosk To Take Your Hard-To-Recycle Throwaways
  
• Supply Chain Innovators: ecoATM—an ATM for Used Electronic Products
  
• EcoATM raises $17M for nationwide rollout of e-waste kiosks
• Get money for your crappy old phones
  
• Garbage Maven: Recycling cellphones at the ecoATM