Getting started with IoT

Analytics Experience 2016 logoThe Internet of Things (or IoT, as some like to call it) holds a number of benefits for many organizations: revenue growth, smarter decision making and efficiency. Experts are predicting anywhere from 20 to 50 billion devices will be connected to the internet by 2020. That’s almost here! Are you ready? And how are you getting started with IoT?!

There’s no question that getting into the IoT business is going to be complicated and time consuming.  Amber MacArthur, technology leader and president of Konnekt, believes companies aren’t ready for the next evolution of the Internet of Things. In this video MacArthur explains, “At the core of the Internet of Things, it’s all about data.”

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Stepping up: The rise of the platform economy

The rapid growth of the digital economy has put it on course to account for 25 percent of the world’s entire economy by 2020. Platform business models represent a large proportion of the overall total but what do we mean by platform? MIT Professor Michael Cusumano defines it as follows: “A platform or complement strategy differs from a product strategy in that it requires an external ecosystem to generate complementary product or service innovations and build positive feedback between the complements and the platform.”

The platform economy has transformed the way goods and services are produced, shared and delivered. Gone are the days where individual firms are competing for customers. A newer, flatter and more participatory model has emerged, whereby customers engage directly with each other.

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There are stark differences between the traditional business model, where value creation is linear and one-way, and the platform model. The platform-driven business model demands efficiency, is two-way and continuous.

Take for example, Uber, a company that's thriving in the new digital economy. An Uber driver creates value by announcing their availability and sharing their location. Sharing this information helps match them with the right user. When a user accesses their phone and requests a driver, the user shares his location and is then paired with the driver who's closest to the user’s location when the request is submitted. The platform business model represents a great opportunity to create growth in the digital economy. According to Harvard Business Review: “With a platform model, the critical asset is the community and resources of its members.” There's a clear shift from the control of resources to orchestrating them. Essentially then, Uber is really just a transaction broker. Read More »

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Why IoT deployments need design thinking

470283497Design thinking is, broadly speaking, allowing user experience, or even users, to drive design. It’s a profoundly human-centered process, with commentators using words like ‘collaborate’, ‘experience’, and even ‘empathy’ in their descriptions. Steve Jobs is said to have used it in creating the iPod and iPhone, because it brings together product design and human behavior.

So, what does this have to do with Internet of Things (IoT) deployments? The connection is big data and, perhaps more importantly, how to get the right insights from it. Read More »

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3 ways you can compete in the collaborative economy

Analytics Experience 2016 logoPowered in equal parts by data, mobility and innovation, the collaborative economy has changed the way we think about automobiles, travel accommodations, office space, living space – and so much more.

When anyone can open an app to borrow money from a crowd, order a gourmet dinner from a neighbor, or rent an oceanfront bungalow from a stranger, where does this leave more established businesses?

Can companies built on traditional business-to-consumer models survive in the peer-to-peer economy? More pointedly, will all businesses suffer the fate of the yellow cab companies? Or is there a way for larger enterprises to embrace – and benefit from – the collaborative economy? Read More »

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Turning your data historian into a futurist

Under-utilized technology creates a drag on an organization. The ability to get more out of the tools you already use can increase the value of an existing investment, and that value grows as processes become more efficient and decisions are based on firmer foundations.

Consider the facilities engineer at an oil refinery. She might run 12 weeks of historical data out of her data historian to monitor process performance, examine productivity gains or losses, or understand why an equipment failure occurred. It’s an invaluable tool for examining past performance. But what if she could harne460818751lorezss these massive amounts of process control data, gathered from every sensor in the operation and logged in the historian, to enable forward-looking decisions?

Data under-utilization goes on across the oil and gas value chain. Very large quantities of data are stored – at high cost – but they are used for small tasks, usually by a single user in a spreadsheet. It's a missed opportunity to enable more users to surface a range of outcomes from the same data, using an analytics platform. Read More »

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Community college answers complex ‘why’ questions with data visualization and analytics

Reporting can reveal last year’s graduation rates or this semester’s completion rates at a local community college. But drilling further into that data to ask why students aren’t graduating or why they aren’t enrolling requires more complex analysis.

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Karl Konsdorf, Director of Research, Analytics and Reporting at Sinclair Community College

At Sinclair Community College in Dayton, Ohio, college administrators and professors are using data visualization and analytics to improve completion rates, optimize class sizes and assist graduates with job placements that pay well.

Karl Konsdorf, Director of Research, Analytics and Reporting at Sinclair Community College says, “Every year, we see improvements in the number of students transferring in and earning degrees. And we are seeing fewer dropouts.”

To hear more about Sinclair’s use of analytics, read the full interview with Konsdorf below. 


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Corporate strategy and analytics: The utility leader’s new business partners

115550080 (1)Utilities, like all large organizations, spend a lot of time and resources on strategy. But how do utility analytics leaders ensure that their efforts are connected to this high-level vision?

On June 28th in Los Angeles, a small gathering of utility executives and analytics leaders met to discuss and explore how analytics could be, and should be, aligning with a utility’s corporate strategy. This meeting, known as the West Coast Utility Executive Roundtable, brought together analytics leaders from west coast utilities, as well as thought leaders from the SAS Institute Best Practices group and the Utilities Practice Partner from McKinsey & Company. The following provides a few of the highlights and insights from the roundtable.

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Streaming analytics with SAS Event Stream Processing and Apache Nifi

Do you have a need for real-time, streaming analytics?

What technology are you considering?

How are you going to enable rapid development and deployment of your analytical models all in real-time?

SAS has partnered the the Hortonworks HDF team to develop a nifi processor that allows SAS machine learning models and advanced analytics to be embedded within the nifi process flow. This technology integration connects Apache Nifi to SAS Event Stream Processing. HDF can be used as the platform for data collection and movement, while SAS can leveraged to deploy online/in-stream analytics.

A Nifi flow diagram with ESP paths highlighted

Integrating SAS Event Stream Processing with Apache Nifi: This image shows the two new SAS Processors for Nifi (click to enlarge).

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The three P’s of data monetisation

Data monetisation is a hot topic these days. Especially for people like me watching the movements of early adopters – companies who are using data to create new revenue streams or even create new businesses to capture those revenue streams.

DataStreamX is a notable start-up whose sole business is cashing in on the data monetisation market. Founded in 2014, the Singapore-based company is a data broker, enabling an open market for data value generation.  Their mission is to provide more than insights to customers – they provide a platform that facilitates data exchange. As a stickler for data quality, I’ll note that DQ is one of their founding principles … a smart strategy in a market that offers few second chances.  Data Republic is a similar example in the Australian market.

So you’ve read my post on three ways to monetise your data, and now you’re keen to learn how? Here are my three P’s for developing a successful data monetisation strategy.* Read More »

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A view from Future Camp: SAS, Accenture and Cloudera partner for IoT

Accenture's Digital Innovation Center

The flexible workspace at Accenture's Digital Innovation Center fosters collaboration.

It’s clear from the minute you step into Accenture’s Digital Innovation Center (Future Camp) in Kronberg, Germany, that you're stepping into the future of innovation. The writing is literally on the walls, and everything you need to inspire big ideas – from brightly colored furniture to racks and racks of gadgets – is right at your fingertips. This isn’t a space age workspace or a robotic-powered pod. It’s a hands-on meeting place for design thinking workshops and rapid prototyping, among other creative endeavors.

Some of the hottest tech in this center – and around the world – is powered by the Internet of Things (IoT). And what’s powering the IoT? Data. Lots and lots of data.

This is one of the driving factors in a new collaborative initiative between Accenture, Cloudera and SAS. We'll help organizations apply analytics to realize value from the IoT.

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