Smart grid analytics and what makes IoT special for utilities

sun setting behind utility wiresYesterday I opened up the Wall Street Journal and found the usual mix of ads from major technology vendors touting their IoT (Internet of Things) prowess, and claiming they all have the secret sauce to make all of our IoT dreams come true. Where do I sign up?!

Meanwhile, back here on planet earth, we're all looking at the massive potential for IoT, and figuring out how to best use this new flood of streaming data to benefit our organizations. For many companies in the utilities industry, that means looking at where and how to best leverage the massive IoT instance right in your backyard: the smart grid.

Beyond the hype in yesterday’s Wall Street Journal was an interesting article by one of their columnists, Christopher Mims, who says the internet of things isn’t about things; it’s about services, which of course put me immediately in a defensive posture… “What does this guy know? It’s all about data!”

Read More »

Post a Comment

5 steps to analytic modernization

Modern freeway lit up at nightSome organizations I visit don’t seem to have changed their analytics technology environment much since the early days of IT. I often encounter companies with 70s-era base statistical packages running on mainframes or large servers, data warehouses (originated in the 80s), and lots of reporting applications. These tools usually continue to work, and there is a natural—but dangerous—human tendency to leave well enough alone.

Vendors have produced a variety of new tools for predictive and prescriptive analytics, and they offer many custom analytics solutions for industry-specific problems like anti-money laundering in banking, or churn prevention in telecom. Visual analytics are easier to generate, are much more visually appealing, and even offer recommendations for what visuals would best depict a particular type of data or variable relationship.

New tools are leading to a dramatic increase in the speed and scale with which analytics can be performed, and much greater integration with business processes.  I often refer to this set of changes as “Analytics 3.0,” and many large, established firms have adapted these approaches. They make it possible to make analytical decisions in near-real time, which often yields benefits in terms of increased conversions, optimized operations, or other results. And the process of generating analytical models has become substantially more agile.

Not taking advantage of these analytics modernization opportunities has some substantial implications. The 5 step process I outline below will get you started on the road to analytic modernization. Read More »

Post a Comment

It’s not fair…

EV110-049Gender and race discrimination has been banned in most countries for many years, although gender did have specific exclusions for the insurance industry, where the risk for males and females could be shown to substantially different (e.g. females have a higher life expectancy than males). In the European Union (EU) such discrimination has been prohibited. So what does this mean?

For men, they pay less for life insurance and more for annuities

For women, they pay more for life insurance and less for annuities.

The EU has now turned its attention to the algorithms used for everything from which adverts to show online and product recommendations to image recognition and translation. Pretty much everything we do online has an algorithm working behind the scenes. The EU is consulting as to what should be in these algorithms and how to open them up, so that it’s not just a black box making a decision. Read More »

Post a Comment

3 Steps to big data success with text analytics

Text Analytics 2A huge proportion of big data is unstructured text (such as client interactions, product reviews, call center logs, emails, blogs and tweets). Organizations starting to invest in advanced analytics often overlook the value text analytics could add to the process. But when data scientists or analysts get to work exploring the available data to solve specific business problems, they often find that unstructured text contains the more comprehensive information.

In fact, the demand for text analytics has skyrocketed. Forrester finds that text analytics implementations have doubled since 2012. Every organization in every industry has unmet needs and opportunities – and therefore growing interest in tapping into unstructured text. Organizations across industries have adopted text analytics for a variety of use cases, and the ones that have been most successful followed these steps: Read More »

Post a Comment

Big data analytics had to come before IoT

internet of things

You could say we've been working toward the internet of things (IoT) since computers were first invented. Look at how airplanes have changed from flying by wire to now, quite literally, flying by IoT (or connected plane).

The connected car is another example of how big data analytics is the driving force behind the application of all things IoT. This is why I believe big data analytics had to evolve first, prior to tackling all the cool issues, services and products being driven by IoT.

Looking back through history, you could say that past "IoT" issues were addressed with the technology available at the time, including: the telegraph, telephone, traditional car, the Turing machine vs the German Eniac -- up through present day applications like smart grid, the digital oil field, smart (connected) car, smart appliances, connected factory, connected house, smart building, smart city, etc.

IoT is a driving distributive force because it's bringing, and will continue to bring, change, just like analytics. That makes sense because analytics is the key component for implementing better automation and deriving insights and decisions out of the massive amount of big data and streaming data we can now store and analyze to help drive actions in real-time or near real-time.

But what ultimately drives analytics and IoT is not the computers/robots/sensors or automation. At the heart of all this technology are the people who are necessary to achieve and make use of it all. As two of my colleagues, Tamara Dull and Anne Buff, have previously written to be successful with big data and analytics you need to make sure you get the right people on the bus. I would say this applies to being successful with IoT projects as well.  Learn more by downloading their white paper: Getting the Right People on the Big Data Bus.

Post a Comment

How to Pokemon Go-to-Market

pokemon-go

This Pokemon Go player is catching a pidgey at a local restaurant.

Has there ever been an app that’s captured the world’s imagination as quickly as Pokemon Go? The usage statistics are mind-blowing, and whether or not the world has reached “peak Pokemon Go” yet, this will doubtless be a short-lived fad. But this could be the app that brings augmented reality and location-based marketing into the mainstream.

Already, savvy marketers are using Pokestops to attract (“lure”) prospective Pokemon hunters to their stores. This can provide inexpensive exposure to retailers (a 30 percent boost in sales for a $10 investment for one pizza restaurant!), and is showing real revenue generation results for some. The shrewdest among them will offer additional value-adds for Pokemon hunters, such as free phone charging or discounts for Pokemon fans.

This phenomenon has the mass marketing benefit of providing publicity and boosting foot traffic for a minimal cost. Read More »

Post a Comment

Monetizing data from the IoT

MoneyData monetization, at its simplest, is the process of turning data into bottom-line value for a company -- often through improving efficiency and/or customer experience, and building customer loyalty as a result.

This may sound simple, but in practice, it’s anything but. Good data, advanced analytics and real-time decision making are all required to monetize data successfully.

Three generations of analytics

Analytics has moved a long way from its early stages, with relatively small, structured data sources. Back then, creating models was a time-consuming business. Statisticians and modelers spent most of their time well away from business people. Most importantly, decisions didn’t depend on analytics, and nobody competed on analytics. Read More »

Post a Comment

Dance hall rules: data science ethics today will impact artificial intelligence tomorrow

Artificial Intelligence networks surrounding a human headThere has been much discussion about the relationship between data science and artificial intelligence. It can become a complicated dance when applied data science is partnered with emerging artificial intelligence technologies. Who takes the lead? How do we keep the beat? Can we make sure neither party steps on the other's toes?

I like to think of data science at least partially as being an application of artificial intelligence. Academics (and to some extent even practitioners) create algorithms while data scientists cull data and apply these algorithms. As these algorithms develop more abilities to learn, machines will become more intelligent.

Learning to dance with this new partner will be a delicate balance of directing the algorithms (through informed feature selection, feedback loops, manual model parameter selection and business rule encoding) and letting them lead (through autotuning, optimization techniques and deep learning). These considerations will undoubtedly grow in importance as data science and automated decisioning expand into every corner of the organization and into our daily lives.

However, one area where we, as data scientists, most definitely need to take the lead is in developing and using ethical frameworks. Computers are getting better at simulating intelligence, but so far, lag in simulating human values.

Serious folks like Stephen Hawking, Bill Gates and Elon Musk are investing considerable time and energy in spreading the word about the perceived threat that unconstrained (eg: without ethical frameworks) AI presents.  As chief artificial intelligence practitioners, data scientists need to take the lead in setting up ethical frameworks which will keep AI on the right track.

Let’s start with a reasonable assumption: by the end of the century, machines will be a great deal more self sufficient (indeed, self-driving cars, target seeking military drones and smart coffee machines are already scientific FACT). Self-sufficient in this sense means self-learning and self-modifying, and eventually identifying and acting in contexts for which the machine was not originally designed. If you extrapolate this progression of self sufficiency to a natural conclusion, it could also mean that not even the hard-coding of back doors in such systems would be enough to completely eliminate the chance of undesirable machine behaviors.

Read More »

Post a Comment

Why does your company need an analytics culture?

Analytics Experience 2016 logoThe digital disruption is creating unforeseen events, such as new competitors, products and services that threaten the performance and positioning of consolidated players. Big data and analytics prove themselves, through successful user cases, as the answer to intercept the demand, prevent churn, draw an integrated view of the customer, manage fraud and reinvent the offer. Today, businesses recognize the importance of a data-driven approach and, in fact, investments in business intelligence and analytical technologies are significantly growing worldwide.

In Italy, for example, according to Big Data Analytics & Business Intelligence of Politecnico of Milan, CIOs identify analytics as the main investment priority for 2016, and they see the skills in big data as the greatest barrier to the digital transformation. Read More »

Post a Comment

Are you getting your fair share of my wallet?

Lady grocery shoppingEvery grocery store must be laser focused on those things that maintain a competitive edge in this saturated market. In Bedford, NH, with a population around 21,000, there are six grocery stores and two mass merchants who sell groceries. If Bedford is like the national average, these food retailers are counting on families spending 5.6 percent of their income on groceries. They all want a share of the wallet.

Within the last few years, two major chains closed their doors and three new chains came to town. To my surprise, I found myself shopping at all of them. I love buying produce at Whole Foods, but other than that, I split my spending at Fresh Market, Harvest Market, Market Basket, one of two Hannafords, Walmart and Target. As you can tell, I’m not a loyal follower of any of these retailers.

The reality is, unless the store is running some great promotions, I can go anywhere to stock up on groceries. As a working mom with active kids, my time is limited. I shop at the store that is most convenient at the time. That makes it difficult for retailers to engage with me. I suspect I’m not an anomaly.

So the question is: How can these stores earn a bigger share of my 5.6 percent? Read More »

Post a Comment