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As the Big Data movement gains momentum we’ll find more and more reasons to rethink how we actually create value out of data.

Not just customer data, but operational data as well. Let’s look at a few predictions for 2012 and then I’ll try to make sense of what we’re seeing at Neural ID.  The future, as they say, is already here, we just have to know where to look for it.

Harlan Smith’s assessment of where Big Data is headed is quite insightful. In particular, he singles out the following industries:

• Supply chain, logistics, and manufacturing — With RFID sensors, handheld scanners, and on-board GPS vehicle and shipment tracking, logistics and manufacturing operations produce vast quantities of information offering significant insight into route optimization, cost savings and operational efficiency
• Online services and web analytics — Internet companies invented Big Data specifically to handle processing information at Internet scale. Implementation of these analytical platforms is now viable for smaller online services companies to provide an edge over competitors for advertising, customer intelligence, capacity planning and more. Companies who don’t offer online services but do have an ecommerce or other online presence will benefit greatly from understanding customer behavior and buying patterns via clickstream, cohort analysis and other advanced analytics.
• Financial services — Financial markets generate immense quantities of stock market and banking transaction data that can help companies maximize trading opportunities or identify potentially fraudulent charges, among various other uses. New regulations also require detailed financial records to be maintained for longer periods.
• Energy and utilities — Smart instrumentation such as “smart grids” and electronic sensors attached to machinery, oil pipelines and equipment generate streams of incoming data that must be stored and analyzed quickly to uncover and fix potential problems before they result in costly or even disastrous failures.
• Media and telecommunications — Streaming media, smartphones, tablets, browsing behavior and text messages are captured at ever-increasing rates all over the world, representing a potential treasure trove of knowledge about user behavior and tastes.
• Health care and life sciences — Electronic medical records systems are some of the most data-intensive systems in the world and making sense of all this data to provide patient treatment options and analyze data for clinical studies can have a dramatic effect for both individual patients and public health management and policy.
• Retail and consumer products — Retailers can analyze vast quantities of sales transaction data to unearth patterns in user behavior and monitor brand awareness and sentiment with social networking data.

Of course, it’s right to look at the vertical applications of the technology.  The enterprise is learning to “sense and respond” as Big Data takes it’s place at the business table.

But there’s more.  The folks at O’Reilly have put together a guide to the key issues in the Big Data universe:

Data issues — The opportunities and ambiguities of the data space are evident in this segment’s discussions around privacy, the implications of data-centric industries, and even in the debate about the phrase “data science” itself.

• The application of data — An exploration of data applications showed that this segment is quickly expanding to include everything from data startups to established enterprises to media/journalism to education and research. A “data product” can emerge from virtually any domain.

Data science and data tools — The tools and technologies that drive data science are, of course, essential to this space, but the varied techniques being applied are also key to understanding the big data arena.

The business of data — This is all about the actions connected to data — the process of finding, organizing, and analyzing data that allows organizations of all sizes to improve and innovate.

What we’re focused on is the intersection of the business and the data - particularly unstructured data. Inductive Analytics is a key solution need for these emerging trends. The only way to deal with the key challenges of big data outlined above is by addressing data completeness, data reduction and  intelligent value creation - addressing the analysis gap between the sensor and the user.

Here are some examples:

• Retail - the use of intelligent learning to improve compliance monitoring, crowd data sourcing, loyalty and other key services enabled through inductive analytics.
  
• Food and Beverage -  automated identification for CPG industries in demand-driven supply chain applications.
• Manufacturing - machine learning employed in trending, stability and quality assurance.
• Automotive - quality assurance on the assembly line.
• BioPharma - can’t say too much about what we’re doing here yet, but stay tuned!

What I’m saying is 2012 will bring us a stunning variety of cutting edge intelligent analytic applications across multiple industries.  The future is already here.  

Join us on the journey >>

Where is intelligent value being created in your business?  Are you taking the time to review your strategy for intelligent solutions? 

This past year, when talking to senior managers in the Fortune 500, I've seen several key indicators of progress in this direction.  Not all motivations are pure, however.  First and foremost, we see decisions driven by fear - the fear of being outsmarted by the competition, fear of not being able to achieve the cost reduction directed by senior management, and the fear that while intelligent value creation may seem like a cool concept on paper, it may not deliver a clear ROI in practice.

Increasingly, we see businesses analyze the micro-details of their operations using unstructured data coming from sensors, devices, third parties, and the Web, sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study live data streams to understand the current state of the business and track aspects such as customer behavior, productivity, quality and compliance. Intelligent value creation capabilities are now being extended to R&D and innovation as well to generate new insights and create value.

Companies can achieve growth and cost reduction through Intelligent Value Creation. 

In retail, we've all seen how Walmart outperforms the competition through advanced logistics and supply chain efficiencies. Intelligent logistics practices have optimized all transportation, distribution and supply chain areas.  Now the company has invested in an acquisition to improve their understanding of social analytics and customer experience.  By analyzing what they call the "Social Genome," Walmart labs is all about intelligent value creation. Here's how they describe the goal:

"The first generation of ecommerce was about bringing the store to the web. The next generation will be about building integrated experiences that leverage the store, the web, and mobile, with social identity being the glue that binds the experience."

And as we see in the news headlines, the automotive industry has gone through dramatic changes.  Toyota has maintained strong growth throughout the last 20 years by adopting intelligent value creation: constant improvement at its core, embraces learning faster about your products, systems and process.  The commitment to embrace change and improve your business is in itself ....intelligent.  Automotive companies that did not embrace constant improvement with a reliance on legacy products (e.g. GM) have suffered tremendous loss of market share.  Cutting costs and hacking away from a finance perspective does not create intelligent value.  GM was amazed at how Ford Motor Company reduced its work force in 2006 - 2008.  They referred to it as gutting the company.  Ford was focused..(pun intended) on creating new product lines that were truly competitive; the market responded in kind.

Improvement and commitment to understanding and exploiting market trends is intelligent.  Protecting existing market share - driven by fear - is not.

The healthcare industry is going through enormous changes as well.  Drug discovery - taking a new discovery to market - is a multi-billion dollar exercise in risk management.  Major pharmas are reducing product development and refocusing their business on much fewer product lines.  (Sounds a little bit like the autos a few years ago...) The only way for these companies to improve their cost metrics and revenues is through intelligent value creation.  Lab coats watching lab rats is not exactly creating systemic intelligent value.  The high cost of labor and a lack of intelligent solutions has forced biopharma into decisions to limit or outsource research initiatives.  But all that is changing.  At Neural ID, we have just completed an extensive survey of the healthcare industry, and we find clear indications that Intelligent Value Creation is at the heart of future plans for all the big players.

Intelligent Value Creation is a decision-making process which can be implemented using our agile methodology; we work with your technology and IT staff to bring a solution from definition to design to production in rapid succession - sometimes within hours, often in less than a week.

Stay tuned for more!

It's November - do you know where Big Data sits on the Gartner Hype Cycle?



In my opinion, Big Data has moved up beyond the point on the chart. We're seeing plenty of mass media coverage - which tells me we're closer to the "peak of inflated expectations."

Big Data analytics analyzes the micro-details of business operations including unstructured data coming from sensors, devices, third parties, Web applications, and social media - much of it sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior.  That's the standard line you'll hear from the analysts.

From our perspective, in the trenches of the world of unstructured data, we see a sizable opportunity between solutions that view data from the top - deductive analytics - or from the bottom - inductive analytics.

Let's explore what we mean by these two approaches.  Deductive analytics consists of a top down understanding of the rules of the business. These rules are drawn from assumptions that business leaders take for granted. Analytics based on deductive approaches tend to miss changes, the new and disruptive customer behaviors or trends - or even changes in trends - that may shift the business rules.  Inductive analytics are driven by observation of real time (or near real time) data, events and behaviors. New shifts, changes from the norm are quickly detected and scrutinized. The resulting new learning results in new business rules - sometimes "on the fly"!

Our customers are eager to derive meaning from data in an automated fashion, hoping to realize intelligent solutions through web services and dashboards that deliver real insights that add business value. Whether viewing these needs from industrial, healthcare or consumer products, the keys to value creation reside in the interaction between inductive, data driven or empirical modeling and deductive modeling from the top of an organization/system. 

For example, great strides have been made in the area of image recognition.  Solutions tend to be vertically focused on a specific area (e.g. facial recognition, fingerprints, or industrial machine vision).  Each of these individually are valuable, but their true value will not be achieved until these systems can interact and interoperate in a more fluid manner.   IP v.6 states that the Internet of everything is now possible.  The "info-operability" gap between sensor and actionable information is the gap to be addressed next.

In his ground-breaking book Analytics at Work, Tom Davenport tells us:

"Unstructured, non-numeric data - the 'last frontier' for data analysis isn't in the formats or content types that databases normally contain... It can take a variety of forms, and companies are increasingly interested in analyzing it."

That is the Big Data challenge.... IMHO.  It is also the challenge for any intelligent system requiring a complete view of the patterns that drive quality and service.

What we are looking at is nothing less than intelligent value creation. It's time to bring both inductive and deductive analytics to Big Data.

Who is Neural Dude?  He's the alter-ego of Neural ID CEO Tim Carruthers. A swashbuckling scientist dedicated to learning about intelligent analytics, big data, and using machine learning and pattern recognition technology to create new value for businesses and institutions. NeuralDude's mission is to engage analytics and AI practitioners focused on unstructured data. He's interested in intelligent value creation for image, video and waveform data types: How do we solve the most demanding unstructured data problems, requiring machine learning and recognition for AI and intelligent analytics applications?  NeuralDude wants to know: "Are you thinking about intelligent analytics?"

Technology brings us unprecedented analytic capabilities to solve critical pattern identification challenges and deliver enterprise value in real-time. Business are only just beginning to understand and tap into the possibilities and opportunities available.  The road is not well defined; this blog is our response to understanding the challenges and benefits of this new direction. NeuralDude wants to ask questions, discuss alternatives and help shed some light on this emerging space. Together, with your help, we'll bring together industry thought-leaders, professionals, and vendors to:

- Advance our collective knowledge of the definitions, trends, and technologies in the space

- Discuss the business impact of intelligent analytics, and pattern recognition applications in particular 

- Examine the strategic alternatives available - how are business models going to change?

- Understand how enterprise level businesses will be served - what are the risks and barriers to adoption?

- Clarify the choices and use cases for large enterprises, in a way that makes sense to business leaders

- Develop recommendations for creating a intelligent analytics discipline within your organization; present sample business justifications supporting intelligent analytics investments

- Define and understand the critical factors that contribute to improving the customer experience 

- Encourage discussions of lessons learned from practitioners

- Collaborate with vendors, businesses, and individuals to exchange ideas, create an online repository of "next" practices, and report on new developments as they occur

- Disseminate information on news, events, and relevant articles on a regular basis

- Create a framework for measuring intelligent analytics performance criteria

- Examine the critical security requirements and applications for intelligent analytics applications

- Invite contributions from experts in the field to answer your questions 

In essence, we're here to help you challenge traditional industry assumptions that are no longer valid.

Will you join the conversation?