The Internet of Things (IoT) is certainly all the rage these days. Every major technology firm or vertical industry icon claims to be shaking up the norm with the latest and greatest application in this space. With industry analysts predicting 50 billion connected devices and revenue to reach $1.7 trillion by 2020, it seems like just about every major vendor is trying to reposition their products to capitalize on the growing IoT market.
IoT is all about connecting vast numbers of smart machines and autonomous devices across multiple platforms and applications. Only a few examples include:
– Smart highways. Internet-connected roads that monitor traffic engineering, measure pollution areas, develop parking efficiency models, and communicate.
– Smart homes. Sense user behaviors, communicates with other home appliances (heating, plumbing), provides trends on food consumption as well as notifications for food replenishment monitoring and signaling.
– Smart factories and supplies/construction. Networked sensor technology to analyze manufacturing processes, sensors in materials and other substances to track stress and failure along with factory automation and management systems measuring reliability, quality, production and related analytics.
IoT is expected to also make impacts in government, education, finance and transportation as well. The list can go on… And companies are scrambling to capture a portion of this market in a range of additional industries including energy, manufacturing, healthcare, security and surveillance, telecommunications and retail.
The key to a successful implementation and understanding of IoT content is to really understand that it is basically connecting any device with an on-off switch to each other through the internet. A comprehensive IoT environment is when data is removed from siloed applications and is connected and shared between associated products and services within the internet to yield information that not only provides insights, but essentially takes actions from its learnings.
To the Competitive Intelligence (CI) professional, the IoT poses unprecedented scalability and analytical challenges. Think about products or appliances in your everyday lives that will now be transmitting data on usage and application, your (the customer!) behaviors, competitive products preferences, where your buying interests lie, shopping patterns and norms, etc. This information will be crucial for the CI practitioner in understanding the behavior of customers, how their products are used and perceived, and how to advance / position your offerings against your competitors.
Vast numbers of IoT endpoints—sensors, smartphones, tablets, (“edge sensors & networks”)—will generate massive volumes of data that must be gathered and processed in a highly efficient, reliable and secure manner. Furthermore, this enormously large amount of information is perpetual and ever-changing – the information gathered from a traditional competitive landscape is no longer stagnant in nature. To successfully capitalize on the IoT market data paradigm, you’ll need to shift the traditional CI pendulum into a model that allows for efficient gathering of data, clever data analytics, and masterly execution of actions – all within a real-time business environment!
Data ‘Fluid Dynamics’
One of the challenges for the CI practitioner in an IoT-connected world is the fluidity of the data. Traditionally, batch processing of CI efforts was the norm – collection of static (and dated!) data, analysis, and results, all compiled as a “batch” and synthesized by the organization many weeks/months after the data was collected. In the IoT world, this information processing methodology is almost useless. A CI model infrastructure needs to be designed that has the ability to process data that is perpetually fed and insights that are continuously provided and updated in order to stay abreast of the latest market behavior and customer preferences. This goes beyond the traditional “batch” methods and must include world-class collection methods, integrated processing tools, and organizational readiness for flawless execution. There are three main areas related to IoT and data modeling:
– Industry Models redefined – Business and operating models are changing to accommodate a more product service solution scenario that most benefits organizational outcomes
– Value of analytics – New insights and perspectives being generated from shared relationships between users and products & services, supply chains, and cross-industry / markets to create new opportunities for growth and expansion
– Data Automation – Intelligent machines will automate repetitive human tasks. Human capital will be depended upon to provide more creative, sophisticated and complex perspectives to intelligence modeling and will require a different skill set from the traditional practitioners of the past.
The internet has been the life-line for CI research in the public domain. This has and will continue to be a dominant source of content for years to come. However, this source content will now start to proliferate exponentially over time. Hundreds and/or thousands of applicable sources will need to be analyzed and processed quickly and efficiently to determine the relevancy to your objective – and all are immediately changing over very short periods of time. This puts a significant burden on your CI model in its ability to carry out even the most basic lifecycle components of the overall analysis – collect, manage, and report.
Optimistically, there is now a push for large data repositories and information warehouses (such as Knowledge Management Systems) and other multi-faceted data analysis tools being created to capture and analyze data on a large scale and effectively manage this ever changing data environment real time. Additionally, new processing methodologies and techniques that employ software-based algorithms for data analysis, as well as comprehensive CI Portal development and management for analytics archiving and information dissemination are underway. These are critical elements for the 21st century CI practitioner.
Reluctantly, some organizations are viewing the increased use of tools and computer models to synthesize data analytics as a threat – the fear that automation of these functions will dilute the quality of market research output and reduce the level of inherent expertise that goes along with a CI practitioner’s skillset. An additional concern is that automation may contribute to further lowering barriers to entry for “intelligence” and thereby giving a false impression that tools can replace knowledgeable CI experience. The ideal perspective is essentially “none of the above”, but rather a hybrid scenario where experienced CI professionals utilize tools to automate the general data collection and analytics process. The experience and knowledge set of the CI practitioner is critical in formulating model parameters, understanding sequencing of tasks, and generating action-based results to maximize the return on tool investment.
The supersonic speed at which the collection and analysis of data requirements that will drive business decisions will be unprecedented – and the organization’s ability to execute within this environment will ultimately determine its survival. Therefore, regardless of the level of sophisticated analytics tools and world-class CI modeling your organization might have, the key determinant in its success is its ability to immediately bring this data to action. Multi-tiered and embedded sub-layers of a decision-making organizational structure has already been a phased-out practice for many years, and now becomes even more outdated in the IoT information landscape. Today’s organization needs to be in the forefront of adapting to disruptive technology and markets. Surviving disruption is dependent on having stable company management, strategies, systems, culture and long-term customers that are equally agile and adaptable to evolving market conditions. Looking inward will help organizations identify and prepare for these disruptions and be better equipped to embrace these challenges, rather than bracing against them:
– How long does strategic decisions get disseminated throughout the organization? Are there superfluous levels of non-decision makers that are part of the organizational hierarchy? What are those impacts to implementing quick course-changes for large organizations?
– How effective are customer impacts analyzed and reviewed to determine quick and efficient improvements to your customer’s total experience? What are the process and system implications for short and long term impacts to resolving customer problems? How frequent and to what level are customer insights reviewed in the organization?
– What tools and systems capabilities can be augmented to address gap analyses in your analytics model? Is there bandwidth and resource constraints that can limit / impact the quality of the results obtained? How much corner-case manual “brute force” is required to supplement the model to provide a comprehensive action plan?
We are on the threshold of a data-management revolution. Today, IoT is helping to improve productivity, reduce operation costs and provide an exciting and fresh environment for CI practitioners. In the long term, good CI analytics and modeling will enable companies to establish entirely new product and service hybrids that disrupt their own markets and generate fresh revenue streams.
The traditional way information is collected, how it is used, and what actions to take is being challenged and will break under the weight of this data tidal wave. These challenges are straining the conventional CI wisdom and require practitioners to think on a larger, more comprehensive and global scale than ever. Organizations that take a holistic and pragmatic approach to acquisition, management and dissemination of data will be successful in preparation for the data-centric market of the 21st century. Pragmatic CI practitioners can be instrumental in preparing for the dawn of the “outcome organization” – where companies shift from selling products and services, to ones that deliver solutions and outcomes.
Paul Santilli leads the WW Business Intelligence and Customer Insights Organization for Hewlett Packard Enterprise’s OEM Business, and has been with HP for over 20 years. He is responsible for Business and Competitive Intelligence Modeling and Customer Insights analytics, where he is the Chairman of HPE Executive Customer Advisory Boards worldwide. Additionally, Paul heads up the WW OEM Marketing & Evangelism Team, focusing on Marketing & Sales Enablement, WW Communications and Social Media platforms. Paul also is on the Strategic Competitive Intelligence for Professionals (SCIP) Board of Directors and has presented worldwide on various topics related to Intelligence and Insights in both keynote and workshop forums. Paul has a Bachelor’s degree in Engineering from the University of Michigan, and earned a Master’s degree in Engineering and Business at Stanford University.
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