Bringing Context to IoT Data


by Lakshmi Randall

This article is the first in a two-part series. Read the second part here.

Just as each singular ingredient does little to suggest the ultimate flavor of the finished dish, disconnected IoT data provides only a limited perspective on the information available. Ingredients for a meal are selected and combined in a meaningful way to create flavors, textures and other qualities that would otherwise remain latent. Likewise, assorted IoT data must be blended, and combined with other relevant data – such as master data – to bring the context necessary to analyze and expose more meaningful relationships and the comprehensive insights needed for optimal decision making.

Proliferation of Devices Underscores Importance of IoT Integration
The Internet of Things has quickly become a major source of Big Data, and continues to proliferate across all business and consumer sectors, including manufacturing, energy, healthcare, transportation, automotive, entertainment, and home automation. Industry predictions point to 50 billion connected IP devices by 2022 accompanied by a realized value of $14.4 trillion.

IoT products and solutions will undoubtedly have a profound impact on enterprises and has the potential to change the way many companies define their business and relate to their customers. Also, the IoT value proposition encompasses both internal and external benefits. Internally, for example, IoT provides the opportunity for an organization to improve the management and operational efficiency of its assets, such as a refrigeration system, by enhancing the equipment with sensors and connectivity that enable collection of performance data in real time. This data serves as input to predictive maintenance models that assist in determining schedules for planned maintenance and replacement of equipment. Examples of external benefits provided by IoT include improved customer retention through enhanced customer satisfaction, and increased revenue from two sources; directly selling packaged data gathered internally and/or acquired from outside sources, and from the creation of new lines of business.

The Threat of Disparate Information Flows to Comprehensive Insights
Like other data types, IoT data can originate from various sources comprising disparate information flows. These might include business and product data consisting of sensor measurements and performance metrics; operational data such as warranty information, customer profiles and maintenance service history; and external data such as weather and regulatory data. Such disparate data provides restricted, compartmentalized insights at the expense of a holistic view of the combined data and more comprehensive insights.

To fully glean the potential value of IoT data, it must be combined and enriched with other critical data such as master, reference and other contextual data.

The Role of Data Virtualization in IoT
Data virtualization solves the dilemma of gleaning comprehensive insights from disparate data. It provides the best-fit technology since it delivers the means to access data from disparate sources through an abstraction layer and enables a single view of the data.

Data virtualization imparts agility to Big Data and IoT endeavors through its ability to leave all the source data exactly where it is, stored across myriad of heterogeneous systems, and establish a virtual view and abstraction layer for accessing all the data (see architecture in Figure 1). More importantly, the reduction in effort and cost to combine and access information promotes greater exploration of the data which can lead to new insights.

Figure 1.

The benefits of employing this architecture include:

  • Adds context to device data
    • Enrichment and augmentation of IoT data with other data
  • Simplifies publication of data assets
    • Universal semantic model to support analytical and operational decisions
    • Data as a service approach for data monetization
  • Integrated governance
    • Centralized security at a granular level
    • End-to-end data lineage
    • Abstraction of source technologies
    • Advanced data masking

Data Virtualization in the Overall IoT Ecosystem
As the figure depicts, data virtualization is a critical part of the IoT ecosystem, as it ingests, combines and processes detailed or aggregated data from local IoT servers, from centralized servers including cloud and on-premises, and from traditional (DW, CRM, ERP) data sources which it then provisions for analytical or operational uses.

Figure 2.

Data virtualization also plays a crucial role in IoT integration. Although not directly involved in the collection of data, it helps with ingestion, processing, blending and publication of resources for both operational and business analytics initiatives. Data virtualization helps to overcome common IoT challenges such as data security, lack of awareness of available data within the organization, and real-time data integration. Big data generated by IoT is accompanied by promises of real-time responsiveness and real-time insights. Data virtualization delivers on those promises by enabling speed and agility in business.

Editor’s Note: Part two of this article will address how data virtualization imparts governance in IoT solutions by enabling support for data protection and privacy to drive superior customer experience.

Lakshmi Randall is the director of product marketing at Denodo.

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