Futures According to Laura… Convergence of Cloud and Neural Networking with Mobility and Big Data

It’s been longer and longer between my posts and as always, life can be inferred as the reason for my delay.

But I was also struggling with feeling a sense of “what now” as it relates to Business Intelligence.

Many years ago, when I first started blogging, I would write about where I thought BI needed to move in order to remain relevant in the future. And those futures have come to fruition lately. Gamuts ranging from merging social networking datasets into traditional BI frameworks to a more common use case of applying composite visualizations to data (microcharts, as an example). Perhaps more esoteric was my staunch stance on the Mobile BI marriage which when iPhone 1 was released was a future many disputed with me. In fact, most did not own the first release of the iPhone, and many were still RIM subscribers. And it was hard for the Blackberry crowd to fathom a world unbounded by keyboards and scroll wheels and how that would be a game changer for mobile BI. And of course, once the iPad was introduced, it was a game over moment. Execs everywhere wanted their iPads to have the latest and greatest dashboards/KPIs/apps. From Angry Birds to their Daily Sales trend, CEOs and the like had new brain candy to distract them during those drawn out meetings. And instead of wanting that PDF or PowerPoint update, they wanted to receive the same data on their iPad. Once they did, they realized that having the “WHAT” is happening understanding was only the crack to get them hooked for a while. Unfortunately, the efficacy of KPI colors and related numbers only satisfies the one person show – but as we know, it isn’t the CEO who analyzes why a RED KPI indicator shows up. Thus, more levels of information (beyond the “WHAT” and  “HOW OFTEN”)  were needed to answer the “WHY” and “HOW TO FIX” the underlying / root cause issue.

The mobile app was born.

It is the reborn mobile dashboard that has been transformed into a new mobile workflow, more akin to the mobile app. 

But it took time for people to understand the marriage between BI dashboards, the mobile wave, especially the game change that Apple introduced with it’s swipe and pinch to zoom gestures, the revolution of the App Stores for the “need to have access to it now” generation of Execs, the capability to write-back from mobile devices to any number of source systems and how functionally, each of these seemingly unrelated functions would and could be weaved together to create the next generation of Mobile Apps for Business Intelligence. 

But that’s not what I wanted to write about today. It was a dream of the past that has come to fruition. 

Coming into 2013, cloud went from being something that very few understood to another game changer in terms of how CIOs are thinking about application support of the future. And that future is now.

But there are still limitations that we are bound by. Either we have a mobile device or not, either it is on 3 or 4G or wifi. Add to that our laptops (yes, something I believe will not dominate the business world in a future someday). And compound that with other devices like smartphones, eReaders, desktop computers et al. 

So, I started thinking about some of the latest research regarding Neural Networks (another set of posts I have made about the future of communication via Neural networks) published recently by Cornell University here (link points to http://arxiv.org/abs/1301.3605).

And my nature “plinko” thought process (before you ask, search for the Price is Right game and you will understand “Plinko Thoughts”) bounced from Neural Networks to Cloud Networks and from Cloud Networks to the idea of a Personal Cloud. 

A cloud of such personal nature that all of our unique devices are forever connected in our own personal sphere and all times when on our person. We walk around and we each have our own personal clouds. Instead of a mass world wide web, we have our own personal wide area network and our own personal wide web.

When we interact with other people, those people can choose to share their Personal networks with us via Neural Networking or some other sentient process, or in the example, where we bump into a friend and we want to share details with them, all of our devices have the capability to interlink to each other via our Personal Clouds. 

Devices are always connected to your Personal Cloud which is authenticated to your person, so that passwords which are already reaching their shelf life (see: article for more information on this point), are no longer the annoying constraint when we try to seamlessly use our mobile devices while on the go. Instead, they are authenticated to our Personal Cloud following similar principles as where IAM (Identity and Access Management) is moving towards in future. And changes in IAM are not only necessary for this idea to come to fruition but are on the horizon.

In fact, Gartner published an article in July 2012, called “Hype Cycle for Identity and Access Management Technologies, 2012” in which Gartner recognized that the growing adoption of mobile devices, cloud computing, social media and big data were converging to help drive significant changes in the identity and access management market.

For background purposes, IAM processes and technologies work across multiple systems to manage:

■ Multiple digital identities representing individual users, each comprising an identifier (name or key) and a set of data that represent attributes, preferences and traits

■ The relationship of those digital identities to each user’s civil identity

■ How digital user identities communicate or otherwise interact with those systems to handle
information or gain knowledge about the information contained in the systems

If you extrapolate that 3rd bullet out, and weave in what you might or might not know/understand about Neural Networking or brain-to-brain communication (see recent Duke findings by Dr. Miguel Nicolelis here) (BTW – the link points to http://www.nicolelislab.net/), one can start to fathom the world of our future. Add in cloud networking, big data, social data and mobility, and perhaps, the Personal Cloud concept I extol is not as far fetched as you initially thought when you read this post. Think about it.

My dream like with my other posts is to be able to refer back to this entry years from now with a sense of pride and “I told you so.” 

Come on – any blogger who makes predictions which come true years later deserves some bragging rites. 

Or at least, I think so…

To Start Quilting, One Just Needs a Set of Patterns: Deconstructing Neural Networks (my favorite topic de la journée, semaine ou année)

 

How a Neural Network Works:

Neural NetworkA neural network (#neuralnetwork) uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.

This is not your mom’s apple pie or the good old days of case-based reasoning or fuzzy logic. (Although, the latter is still one of my favorite terms to say. Try it: fuzzzzyyyy logic. Rolls off the tongue, right?)…But I digress…

And, now, we’re back.

To give you a quick refresher:

image

Case based reasoning represents knowledge as a database of past cases and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user.

We’re talking old school, folks…Think to yourself, frustrating FAQ pages, where you type a question into a search box, only to have follow on questions prompt you for further clarification and with each one, further frustration. Oh and BTW, the same FAQ pages which e-commerce sites laughably call ‘customer support’ –

“ And, I wonder why your ASCI customer service scores are soo low Mr. or Mrs. e-Retailer :),” says this blogger facetiously, to her audience .

 

 

 

And, we’re not talking about fuzzy logic either – Simply put, fuzzy logic is fun to say, yes, and technically is:

fuzzy logic

–> Rule-based technology with exceptions (see arrow 4)

–> Represents linguistic categories (for example, “warm”, “hot”) as ranges of values

–> Describes a particular phenomenon or process and then represents in a diminutive number of flexible rules

–> Provides solutions to scenarios typically difficult to represent with succinct IF-THEN rules

(Graphic: Take a thermostat in your home and assign membership functions for the input called temperature. This becomes part of the logic of the thermostat to control the room temperature. Membership functions translate linguistic expressions such as “warm” or “cool” into quantifiable numbers that computer systems can then consume and manipulate.)

 

Nope, we are talking Neural Networks – the absolute Bees-Knees in my mind, right up there with social intelligence and my family (in no specific order :):

–> Find patterns and relationships in massive amounts of data that are too complicated for human to analyze

–> “Learn” patterns by searching for relationships, building models, and correcting over and over again model’s own mistakes

–> Humans “train” network by feeding it training data for which inputs produce known set of outputs or conclusions, to help neural network learn correct solution by example

–> Neural network applications in medicine, science, and business address problems in pattern classification, prediction, financial analysis, and control and optimization

 

Remember folks: Knowledge is power and definitely an asset. Want to know more? I discuss this and other intangibles further in part 1 of a multi-part study I am conducting called:

weemee Measuring Our Intangible Assets, by Laura Edell