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We know that the ability to derive actionable insights from Big Data is a major challenge facing enterprise decision-makers. 

In particular, we see this frustration in industries where real-time intelligence can make a significant difference not just in operational cost savings, but also in proactive, optimization of business value.  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.

We know there's a gap between the data and action - a gap which can be defined as a semantic gap between the sensor and the pattern-based strategy that makes sense of the unstructured data - images, video feeds, waveforms, etc.

As more businesses embrace Big Data and the need for inductive analytics, CIOs and their counterparts are realizing the need for what Gartner calls a Pattern Based Strategy, a strategy that Neural Dude says: "proactively addresses real-time signals in operations to create intelligent value for the enterprise."

And, as Neural Dude has explained on this blog, the next generation of Big Data Analytics addresses the "gap" that exists between sensor or raw data and actionable information.  At Neural ID, we've delivered solutions to John Deere, GM and NASA that address the subjective nature of unstructured data.  Our Learn-Recognize-Act ® framework is an example of a pattern based strategy for unstructured data. And the "gap" in this context is making sense of data created by operational processes or  consumer behavioral data in order to create business value.  As this operational data and content grows exponentially, companies that can make sense of this data to take intelligent action will be competitive leaders.

Gartner VP and Distinguished Analyst Yvonne Genovese explains the importance of applying a Pattern-Based Strategy (PBS) approach to seek, model and adapt to patterns contained in big data. According to Genovese, this is the "ability to seek patterns, model their impact on the enterprise, and to adapt the enterprise pursuant to the needs of the pattern."  It involves using data in real-time to ask what is happening right now, and what is likely to happen in the future.

Watch:



In this presentation, Genovese explains how a Pattern-Based Strategy provides an approach to proactively Seek, Model, and Adapt to patterns that may have a positive or negative impact on your strategy or operations across many sources of current and evolving information.



Interestingly, our Learn-Recognize-Act ® framework parallels the Gartner approach. For those of you who have been following our progress at Neural ID, you know that we are developing industry-specific applications using our pattern seeking technology driven not by the "needs of the pattern" but rather what we call "intelligent value creation." 

Our solutions are gaining worldwide acceptance as retail, health-care, manufacturing and other application developers can build their pattern recognition applications and offerings using Neural ID's CURE technology.

The future, as we mentioned in our previous blog post,  is already here!

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 >>

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?