Big Data Analytics: The Role of IT in Intelligent Value Creation -
An INTERVIEW with Neural ID's Tim Carruthers
by Christian Sarkar
Tim Carruthers is the CEO of Silicon Valley based Neural ID, a provider of the world's leading pattern identification platform, designed to deliver next generation applications for biopharma, consumer products , and the Internet - collectively known as Big Data Analytics. Neural ID delivers Big Data Analytics to solve critical pattern identification challenges and deliver enterprise value in real-time. Clients include: P&G, ecoATM, Kroger, GM, NASA and John Deere. BSMReview caught up with "Neural Dude" and asked him about the future role of IT as a value-creation engine for the business.
How does tomorrow's IT become more strategic? What should IT executives be doing to create business value?
Tomorrow's IT is already here. This is something I recently blogged about. I have to say that now there is a huge opportunity for IT to deliver business value using what some are calling Big Data Analytics.
Can you explain the term "Big Data Analytics"?
Of course, it's been wrongly assumed that 80% of data that IT was collecting was unstructured data and was of no value, or very limited, value. Now, technology brings us unprecedented analytic capabilities to solve critical pattern identification challenges and deliver intelligence into the enterprise.
Have businesses implemented Big Data solutions, or are they still defining Big Data Analytics within their organizations?
Businesses are only just beginning to understand and tap into the possibilities and opportunities available. The road is not well defined; the Neural Dude blog is our response to understanding the challenges and benefits of this new direction. For example, we first saw the search engine providers and e-commerce shops unlock the value in Web clickstream data. Those were the first examples of the successful processing of large volumes of unstructured data.
Now what we call Big Data Analytics examines 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.
What's the difference between deductive and inductive analytics?
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 are driven by probability, causality and correlation. These models cansometimes overlook changes in the foundational data,, 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"!
How do customers of Big Data Analytics define success?
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 theinteraction 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.
How does this help IT? Is this what you mean when you say IT plays a strategic role in the business?
IT can and must become more strategic. Not for IT's sake, but for the sake of the business!
IT executives should ask: "Where is intelligent value being created in our business? Are we taking the time to review our 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.
So how can this be aligned with the business?
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," WalmartLabs is all about intelligent value creation - a term we use to describe what we do at the executive level.
And as we see in the news headlines, the automotive industry has gone through dramatic changes as well. 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.
How is your company making a difference in Big Data Analytics?
At Neural ID, what we’re focused on is the intersection of the business and the data. 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!
If you hyper-link the industry examples I just mentioned, they may prove helpful insights for your readers.
Can you tell us about your clients and how they are using this technology?
Sure, there's actually an article which explains how our technology is used.
But let's talk about the ecoATM. The ecoATM Automated eCycling Station, a kiosk device used for pricing and buy-back of used consumer electronics (cell phones, iPhones, tablets), was recently named to Popular Science’s Top 100 Innovations in the “Green Tech” Category.
Our patented technology is used in the phone identification process within the brains of the ecoATM, Neural ID’s CURE® application plays the role of learning what device has been provided by the consumer and determining the quality of the phone, ipad or mp3 player. CURE® artificial intelligence is creating intelligent value for the ecoATM, and supports the award-winning Automated eCycling Station.
Thanks so much for your time.
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