Tag Archives: Lux Research

Beyond the Solution Stack: Data Management for the Internet of Things

When Lux first introduced a framework and taxonomy for approaching the industrial internet of things (IIoT), the focus was on tools and understanding the various parts of the solution stack. The Lux “IIoT Toolbox” provided clients and readers with a structured view of how to turn data from industrial assets, people, and environments into actionable insights to do things like improve operational efficiency, generate new services and revenue streams, and improve employee health and safety. This was an important and valuable baseline for understanding the IoT, and how to properly construct a solution stack.

However, understanding each of the elements in the IoT toolbox was only the first challenge. As clients and readers have started to figure out how to properly construct an IoT solution, the focus now shifts towards refining, optimizing, and future-proofing such a solution. The ensuing challenge here becomes effectively and efficiently managing all of the data generated by the IoT. For this, we propose a data-centric framework and taxonomy to help clients and readers understand the next layer of capabilities, processes, and approaches to properly managing the glut of data being generated within their respective deployments.

In no particular order, the seven core components to this data-centric framework include the following:

  • Data creation (or data generation) references all of the activities related to capturing and contextualizing sensor data. It is more than just recording simple, discrete sensor readings. It is also determining how data is collected, choosing an appropriate sampling frequency, imposing boundaries and limits on the data to filter spurious events, and other activities like manual annotation to further contextualize the data sample.
  • Data security references all of the activities related to isolating data to a discrete audience of authorized individuals, machines, applications, or processes.It is not just the deployment of software and systems such as firewalls, intrusion detection systems (IDS), and antivirus software to keep data isolated and protected. It is also establishing role-based data access, user authentication schema, data encryption, and the utilization of AI and machine learning to continuously monitor for network intrusion or unauthorized data access. It is also the ongoing remediation and patching of systems to ensure data and the surrounding environment remains protected from illicit activity. Many decisions must be made with regard to data security, and they will always appertain to the decisions made with regard to the other components of the data-centric framework.
  • Data transmission references the connectivity and conveyance of data across the entire end-to-end IoT solution stack. It is more than just a singular event of transmitting data from sensor to end device. Data is often transmitted across multiple devices, among multiple locations, using multiple formats or standards, and using varying physical means. Data may be transmitted using short-range technologies, such as Bluetooth or Wi-Fi, or long-range technologies, such as LPWAN or cellular. Data transmission may also take place at varying intervals, and may only involve subsets or summaries of entire data sets. Decisions regarding what to send and how to send it are always unique to the specific end-to-end solution stack.
  • Data cleanliness references the relative signal to noise ratio of the IoT device data being generated. It is a measure of how much useful information was collected, as a proportion of all data collected. Data often requires cleaning and validation prior to being stored and analyzed. Spurious sensor readings, instrumentation failure, transmission failure (leading to incomplete data sets), environmental interference, and other issues can “contaminate” data sets. “Cleaning” data to remove or repair these types of issues helps to ensure the general accuracy and completeness of IoT analytics and insights.
  • Data analytics references all of the activities related to computational processing and understanding of IoT device data. It is the processes involved with examining data sets in order to draw conclusions about the information they contain. Analytics were traditionally administered by human data scientists, but artificial intelligence (AI) and machine learning have expanded the capabilities, speed, and breadth of analytical capabilities of most modern IoT analytics solutions.
  • Data storage references all of the activities related to storing data across the entire end-to-end IoT solution stack. As with data transmission, data may be stored in multiple locations, in multiple formats, and on varying storage media. It is more than simply storing data in a particular digital or physical format. Data may need to be re-formatted in a particular schema (such as JSON or XML), or stored as a file, object or in a database. It may need to be aggregated, annotated, deduplicated, compressed, encrypted, or archived. Many decisions must be made with regard to data storage, and it often depends on other components of the data-centric framework, such as data analytics, transmission, and security.
  • Data sharing references the imparting of data across separate IoT deployments. It may involve an IoT solution ingesting outside, third-party data to provide better internal analytics capabilities. In this scenario, outside data would be used for training of machine learning algorithms, and improving the accuracy of analytics and insights. Data sharing may also involve an IoT solution sending internal data to an outside, third-party entity to enhance collective analytics capabilities. Some platforms even enable the monetization and sale of internal data, which opens the possibility of generating new revenue streams.

We plan to explore each of these components in greater detail in upcoming journals. For each topic, we will discuss traditional approaches, key innovations, and some specific applications demonstrating the importance of each. Readers should continue to follow the discussion surrounding effectively and efficiently managing all of the data generated by their own respective IoTs.

Data Shows Clean Energy Innovation Has Fallen off a Cliff – but a Few Bright Spots for Growth Are Emerging

The world looks to be underway towards a dramatic energy transition, as it shifts towards more renewables and a sophisticated digitized grid. It is tempting to think of the battle as already decided: Renewable deployments – driven primarily by solar and wind energy – are growing rapidly, albeit from a small base; costs are falling; policy is directionally favorable. However, new research based on big data analysis indicates a worrying trend – innovation interest in renewables is declining, after peaking about four years ago, as shown in the figure below. Without continued innovation momentum, long-term success driven by further clean energy technology improvements is thrown into question.

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Lux Executive Summit Americas – Video Interview – Jim Kirkwood, CSO, General Mills

Jim Kirkwood, Chief Science & Technology Development Officer at General Mills, and VP of its “GTECH” organization, sat down to speak with Lux Research. Jim discusses the past and future of innovation at General Mills with Lux Research’s VP of Consulting, Kevin Pang.

Jim and Kevin discussed:

  • How to deliver enough macronutrients like protein to feed a growing world
  • Leveraging bioprocessing and life sciences and the future role of GMOs
  • General Mills’ changing relationships with suppliers and how supplier business models will be different in the future
  • Getting a deep understanding of customers’ problems, and the importance of “tip of the spear” consumers and customer segmentation
  • How to create flexible talent to meet future challenges

To watch Jim Kirkwood’s Keynote Presentation from the Lux Executive Summit, click here.

Be sure to subscribe to Lux Research on YouTube for more videos, and check out our podcast at iTunes, Stitcher and SoundCloud.

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Seeing the New Theater at the Lux Executive Summit Americas

Lux Chief Research Officer Chris Hartshorn’s opening keynote at this year’s Lux Executive Summit pointed to military innovators from Frederick the Great to Napoleon to Stanley McChrystal, their gifts for “seeing the theater” to determine when a change in strategy was needed – and how their organizations needed to change along with it. General McChrystal’s quote from his book Team of Teams: New Rules of Engagement for a Complex World, “We have moved from data-poor but fairly predictable settings to data-rich, uncertain ones,” demonstrates commonality between the military theater and the theater of emerging technologies. Within the theater of emerging technologies the Lux Research analysts cover, Chris pointed to “redistributed everything” as a critical cross-cutting trend, noting that fields from energy to automotive are seeing shifts between products and services, capex and opex, centralization and distribution, and more.

As one example, Chris highlighted the healthcare market, where the current model for delivering care has hospitals organized into silos focused on particular organs, system, and conditions, from cardiovascular disease to asthma to infertility. In contrast, emerging digital health and wellness technologies provide capabilities like monitoring, predictive analytics, and behavior augmentation that can establish unexpected connections between disparate conditions – identifying a common behavioral thread between diabetes and depression, for instance.

Not all emerging technology theater is the same, so some 300 corporate executives, start-up leaders, investors, and policymakers gathered around an agenda ranging from Energy & Infrastructure to Information Meets Matter to the Future of Resources and the Consumer of the Future (for those who weren’t able to attend, presenter slides and videos are available on the conference website).

The theme of this year’s Summit, Ideation to Integration, had participants exploring not just how to assess the theater in which they operate, but also how to respond to it with the right technical capabilities and strategic partnerships to create and grow new businesses. Many speakers returned to the theme that building a clear view of the overall theater was one of the most critical success factors, both for identifying the right ideas and for building the plan to execute on them.

Bill LaFontaine, GM of Intellectual Property and VP of Research Business at IBM, described how IBM sees the theater through, in part, its Global Technology Outlook. Its researchers nominate important trends or discoveries, and a thorough process of vetting, grouping, and prioritizing ideas leads to a document that helps guide the Research Business and company strategy. Bill described how IBM Research identified the rising importance of data and analytics, leading to the development of the Watson platform that famously mastered Jeopardy and became the foundation of the IBM cognitive computing business.

Jim Kirkwood, Chief Science & Technology Development Officer and VP of R&D at General Mills, spoke about the transformations his firm has gone through in its 150-year history – and how General Mills sees the theater by following consumers’ lead. Jim spoke about developing “deep consumer empathy” in a “three I’s” approach – immersion with consumers to understand the problem, interaction to experience the problem, and idea creation to solve the problem. Jim explained how consumer insight led General Mills to focus on inherent nutrition. It made sure all its “Big G” cereals had at least 10 grams of whole grains per serving, then this year committed to use no artificial colors, no artificial flavors, and no preservatives – necessitating a new toolbox based on bioprocessing rather than chemistry.

Throughout the Summit, Lux analysts and industry speakers alike explored changes to the landscape corporate executives are facing – from Lux’s Katrina Westerhof on the reinvention of the power sector, to Isaac Brown on the future of manufacturing, to Teradata’s Carl Howe and Oracle’s Paul Sonderegger on the digital transformation of industry. Seeing the theater will be critical for allowing companies to capitalize and generate continued growth.

 

For more on Lux Research’s Intelligence services, contact Mike at michael.holman@luxresearchinc.com

Shortening the Chain: How Your Value Chain Determines Your Innovation Potential

Innovation is hard – especially because it is one of those ineffable qualities that often can only be defined after it can be seen. But before we get to the subject of defining what “it” is, we should first consider the degrees of freedom and constraint necessary to even pursue innovation.

Your location in the value chain is the largest determinant of just how much innovation you should pursue and invest in. It is certainly true in the B2B space that if you are part of an extended value chain with multiple components serially working to create a final product, you are constrained not just by your nearest neighbor’s ability to absorb innovation, but the overall chain itself being able to successively incorporate a given innovation.

For example, an investment made to decrease cycle time will not create greater value for you if your component is not critical to the final product manufacture pace. Likewise, an investment in greater product quality will also likely fail to capture greater value if yours is not the key component that determines final assembled product quality.

So your value chain is your innovation plane; the trajectory of which determines the overall pace and direction of absorbable innovation. You cannot manufacture, nor can you invent, faster or more robustly than the rate at which the innovation plane itself rises as a unitary line.

What determines the rate and directionality of the innovation plane? I suggest two roles or positions within the value chain: the pacemaker and the bottleneck. The pacemaker determines how much and how fast a final product is to be assembled and distributed to market, and the bottleneck occupies the key limiting step in that delivery. Being either the pacemaker or the bottleneck (or both!) is a great place to be. The pacemaker drives value chain cadence by finding more buyers faster, and the bottleneck responds to that cadence by either finding ways to speed cycle time, and/or adding valued features and attributes that help the pacemaker capture more buyers. The two work together to establish and grow markets by defining the pace and direction of the value plane – everyone else is along for the ride.

So what do you do if you are neither the pacemaker nor bottleneck in your current markets? The best you can do is to stay close to your nearest neighbors and engage in the necessary incremental innovation required to keep pace with the innovation plane. However, if you would capture dynamically greater returns, you must seek to become either the pacemaker or the bottleneck by either finding more direct markets for your goods and services as a pacemaker, or as the bottleneck by entering new markets that place greater value on your know-how and ability to create new value. When unexpected, this is known as disruptive innovation.

Innovation, like all things deemed beautiful, is best defined through the eyes of the beholder. In the case of innovation, its general qualities of being novel, unexpected, and useful are also best defined by the end user.

 

For more on Lux Research’s Consulting or Life-science membership services, contact Kevin at kevin.pang@luxresearchinc.com