Topics of Interest Archives: Predictive Analytics

Augmented, Virtual and Mixed Reality in an IoT-Dominated World

Augmented and Virtual Reality are unstoppable forces. We explain why.

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New Revenue Opportunities at the Intersection of IoT and Analytics

IoTAnalyticsHere at Blue Hill, more and more of our time is spent exploring implications associated with the Internet of Things. Naturally, there has been a great deal of buzz around IoT analytics.

We’ve had the chance to speak with a number of companies who are doing some truly fantastic things with IoT. This includes everything from farms more efficiently watering their crops to cities optimizing traffic patterns – all of which speak to the potential value that analytics can bring on top of a world of connected ‘things’.

In the IoT gold rush, business leaders should not just ask themselves if there are opportunities to build top-line growth from bolstering existing revenue streams; they should also be asking how they can grow top-line performance from net-new revenue streams.

One organization I spoke with, an industrial truck manufacturer, shared insights that the rest of us should learn from. As part of their IoT initiatives, they first began collecting data on their trucks to identify common areas of complications. This eventually evolved into the ability to provide predictive maintenance on their trucks. As part of their warranty support, they now identify imminent problems and schedule service appointments before a breakdown ever happens. This represents a series of meaningful incremental improvements that not only save time and money, but improve the quality of their trucks over time.

However, the manufacturer took a step forward in realizing this service would be highly valuable to their customers as well. They now offer the ability to preemptively identify issues and schedule service appointments as a subscription service to their customers.  In short, truck buyers can purchase this value-added service on an ongoing annual basis.  The result is a better-serviced customer and an entirely new (and recurring) revenue stream.

Any producer of highly valuable capital equipment has a direct lesson to learn from this quick example. The right blend of analytics and IoT enables a new business model – “predictive maintenance as a service.” This is not just an internal business model to improve operational efficiency and maintenance, but an external and client-facing business model that can translate Big Data into Big Money.

More broadly, the lesson is around the usefulness of the vast amount of data that organizations can collect cheaply thanks to the plummeting costs of sensors and data processing power. If the data is valuable in one context, there is at least a chance that it is valuable in another as well.

Business leaders must continue to recognize the transformational shift as every company, whether in manufacturing, retail, or construction, is becoming data centric.  As our abilities to collect and process data about every aspect of our business increases exponentially, we should always be looking for opportunities to repackage the data to drive new and innovative revenue streams.

What new business models do you see from the intersection of IoT and analytics? Join the conversation on Twitter (@James_Haight) or feel free to email me directly ( with your thoughts.

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The Pumpkin Spice School of Big Data

Source: Pumpkin Spice Trident Layers Gum by Mike MozartIn our particular pocket of New England, the leaves are turning golden, and football is replacing baseball on the TVs. This means one thing to coffee drinkers: the re-emergence of the Pumpkin Spice Latte at Starbucks. Over the past ten years, this drink has gone from an odd cult drink to a phenomenon so large that it has earned its own hashtag on Twitter: #PSL.

At the same time, one has to wonder, “What is Pumpkin Spice?” (Other than possibly the long-lost American cousin of the Spice Girls?) Pumpkin spice doesn’t actually have pumpkin in it. And it’s far from the spiciest flavor out there. However, the concept of “pumpkin spice” insinuates the idea of something that’s handmade, traditional, and uniquely American in a way that draws people into the concept of wanting to consume it. Despite its complete lack of pumpkin and relative lack of spice, the flavor created is almost secondary to the cultish conceit that has been constructed around “Pumpkin Spice.”

Unfortunately, the hype, conceptualization, and ubiquitous phenomenon of Pumpkin Spice is matched in the enterprise world through the most overhyped phrase in tech: Big Data.  Like Pumpkin Spice, everybody wants Big Data, everybody wants to invest in Big Data tools, and everybody thinks that we are currently in a season or era of Big Data. And in the past, we’ve explained why we reluctantly think the term “Big Data” is still necessary. But when you go behind the curtain and try to figure out what Big Data is, what do you actually find?

For one thing, “Big Data” often isn’t that big. Although we talk about petabytes of data, there are practitioners that talk about “Big Data” problems that are only hundreds of megabytes. These are still very big portions of data, but these problems are manageable through traditional analytics tools.

And even when Big Data is “big,” this is still a very relative term. For instance, even when Big Data collects terabytes of data, text, and binaries, the data collected is rarely analyzed on a daily basis. In fact, we still lack the sentiment analysis, video analysis, and audio analysis needed to quickly analyze large amounts of data. And we know that data is about to grow by at least one order of magnitude, if not two, as the Internet of Things and the accompanying billions of sensors start to embed themselves into our planet.

Even outside of the Internet of Things, the entirety of the biological ecosystem represents yet another large source of data that we are just starting to tap. We are nowhere close to understanding what happens in each of our organs, much less in each cell of our bodies. To get to this level of detail for any lifeform represents additional orders of magnitude for data.

And then there’s even a higher level of truly Big Data when we track matter, molecules, and atomic behavior on a broad-based level to truly understand the nature of chemical reactions and mechanical physics. Compared to all of this, we are just starting to collect data on Planet Earth. And yet we call it Big Data.

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So, our “Big Data” isn’t big in comparison to the amount of data that actually exists on Earth. And the types of data that we collect are still very limited in nature, since they almost always come from electronic sources, and often lack the level of detail that could legitimately recreate the environment and context of the transaction in question. And yet we are already calling it Big Data and setting ourselves up to start talking about “Bigger Data,” “Enormous Data,” and “Insanely Large Data.”

To get past the hype, we should start thinking about Big Data in terms of the scope that is actually being collected and supported. There is nothing wrong with talking about the scale of “log management data” or “sensor data” or “video data” or “DNA genome data.” For those of us who live in each of these worlds and know that log management gets measured in terabytes per day or that the human genome has 3 billion base pairs and approximately 3 million SNP (single-nucleotide polymorphism) replacements, we start talking about meaningful measurements of data again, rather than simply defaulting to the overused Big Data term.

I will say that there is one big difference between Pumpkin Spice season and Big Data Season. Around the end of the year, I can count on the end of Pumpkin Spice season. However, the imprecise cult of Big Data seems far from over; the community of tech thought leaders continues to push more and more use cases into Big Data, rather than provide clarity on what actually is “Big,” what actually constitutes “Data,” and how to actually use these tools correctly in the Era of Big Data.

In this light, Blue Hill Research promises to keep the usage of the phrase “Big Data” to a minimum. We believe there are more valuable ways to talk about data, such as:

Our primary research in log and machine data management
– Our scheduled research in self-service topics including data quality, business intelligence, predictive analytics, and enterprise performance management
– Tracking the $3 billion spent in analytics over the past five years.
Cognitive and neuroinspired computing

By focusing on the actual data topics that provide financial, operational, and line-of-business value, Blue Hill will do its best to minimize the extension of Big Data season.

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Big Data



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Predictive Analytics

Cognitive Computing

Wearable Tech


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fog computing

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Virtual Reality


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Mobile devices

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Augmented Reality

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Managed Mobiity Services


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Telecom Expense Management