Teaser: Internet of Things (IoT) is connecting a new wave of devices – from sensors and wearable devices to video cameras – to the Internet. The data from these devices can reduce costs, increase revenue and improve customer service, but only if that data can be analyzed and acted on quickly in relation to that specific context or situation.
What is Edge Computing?
Edge computing specifically is an analytic proposition/approach to analyze data close to its source (at the edge) instead of sending it to a remote server for cloud-level analysis. Such edge analytics will allow organizations (or even the devices themselves) to act on new insights within milliseconds rather than waiting for the data to be transmitted to a central data center for processing and get action recommendations after few hours / a day post batch processing. If we consider security camera as an example, the edge analytics triggering the immediate alarm could later be combined with camera data from multiple factories to identify long-term security trends leveraging central data warehouse. As the computing capabilities on the edge advance faster than earlier days, it enables a significant shift from the connected device paradigm to the intelligent device paradigm. Edge computing, also known as edge intelligence, is what is driving this shift.
Nowadays High-data-rate sensors are becoming ubiquitous in the Internet of Everything (IoE). The speed and agility benefits are so great that most businesses believe that by 2018, 40% of IoE-created data will be stored, processed, analyzed, and acted upon close to, or at the edge, of the network rather than in centralized EDWs, according to “IDC IoT Predictions” Report.
What is Situational Intelligence?
It is a proven approach to unite disparate data across an organization, ability to correlate, analyze and visualize them and perform spatial analysis (Ex. what are the devices close to it?), temporal analysis (Ex. How this issue has come up?) and nodal analysis (Ex. How this will impact other nearby devices/assets?) for more-informed and real time decision making.
Why Situational Intelligence?
Organizations connecting to Internet of Things (IoT) devices do not always maximize the value of the data, their operation produces. Data volumes from devices & sensors are increasing rapidly. The quantity and velocity of data generated is so large that not all of it may not be stored or analyzed. We can think of flood streams of data like water containing gold nuggets flowing into an ocean – its true value lost forever.
Manufacturing, Mining, Aerospace, Energy production and distribution, Oil & Gas, businesses, as examples, generate and capture very large amounts of data during their day-to-day business operations. These businesses capture data from such smart physical devices, which have embedded sensors and are able to transmit telemetry data. Transportation and logistics providers generate data in real-time from their business operations, especially in-vehicle telematics. Data from such devices is growing at an increasing rate, and much of it is neither captured nor analyzed.
Businesses must embrace not only the IoT, but the ability to harness the valuable information and insight they provide. If we can’t measure it, we can’t manage it. Several interrelated technologies are required to channel, capture and derive actionable insight.
The core technologies needed are analytics capable of operating on very large data sets as well as streaming data in real-time and visualization engines, which are also foundational technologies for situational intelligence.
Some use cases of Situational Intelligence
The insights from these IoT devices can be applied in Energy, Oil & Gas, Aerospace, Manufacturing industries etc.
- In transportation logistics industry, knowing the exact location and use of its assets in the field will save significantly on costs. Field assets are taxed differently when they are on-road versus off-road, so having precise location and time of use information reliably streamed from in-vehicle telematics is essential. Analyzing that information enables lowering their operating costs.
- Traditionally, offshore oil wells have transmitted data such as the status of drill bits through satellite or CDs to data centers for analysis, resulting in delay before the results can be relayed back to the rig. Situational intelligence allows oil well operators to identify problems in a drill bit, even one operating several hundred feet below sea-level, more quickly and take corrective action before a failure damages the bit or the well.
- In Gas transmission & distribution industry more surveillance functionality is pushed out from central servers to the sensors attached to meters or leak detectors, the more desirable it becomes for the meters/leak detectors to perform some kind of analysis of the readings in its field of view and make decisions about what to stream to the server, what to ignore and what actions need to be performed. Device manufacturers are now embedding analysis capability into these sensors firmware to achieve this aim. One advantage of this approach is the reduced demands on network bandwidth and storage requirements which can easily offset the additional cost of having on-board analytics. With improved server software, a matrix of sensors with on-board analytics engines can provide a powerful surveillance presence.
- Security vendors and providers are promising to develop context-aware securitythat adds crucial environmental and other data to help prove identity and rights. For example, a context-aware security platform will examine a variety of factors — from device type and password to the location of the user logging in — to verify if the log-in request is genuine or perhaps is being generated by a hacker who is using another user’s credentials. A range of security options are incorporating context-aware capabilities anchored in device ID matching, reputational analysis and location recognition to protect data in more dynamic environments.
- Adding analytic capabilities to security cameras allow real-time identification of unusual behavior, such as a group of people gathered by an entrance in the middle of the night. Rather than waiting to send that data to the cloud for analysis, the camera could identify the potential threat on-site and trigger an alarm more quickly. An important type of “analytics” supported on intelligent devices (cameras) is automated modification of video streams to preserve privacy. For example, this might involve editing out frames or blurring individual objects within frames. What needs to be removed or altered is highly specific to the owner of a video stream, but no user has time to go through and manually edit video captured on a continuous basis. This automated, owner specific lowering of fidelity of a video stream to preserve privacy is called “denaturing”. But there has to be a balance between “privacy” and “value” of the data.
Bringing relevant real-time information forward in readily digestible formats to end-users and other stakeholders gives business many opportunities to differentiate itself and realize a competitive advantage. As above use cases highlight, the user experience and interactions will increasingly include real-time information and insights from an organization’s physical assets.
Situational Intelligence Solution
Situational intelligence systems commonly comprises of three components:
- Data acquisition and normalization: Organizations own and access many separate sources of internal IT and external data. These sources need to be brought together into a single environment and normalized so that they can be combined and correlated.
- Data correlation and analysis: Once data from appropriate sources is brought together and normalized, innumerable data sets are correlated and analyzed within specific business contexts to solve problems and uncover new opportunities.
- Data visualization: Rendering correlated and analyzed information in a combination of geospatial and tradition analytical formats gives users a fast and intuitive way to recognize what decisions must be made and what actions must be taken.
Situational intelligence solution that collects data from variety of static and streaming data sources, assesses every potential incident, problem and outcome to provide actionable insight using alarms, alerts and intuitive visual presentations. This enables businesses operating smart grids, supply chains and logistics operations to favorably influence future outcomes. The critical insights provided by correlating and analyzing multiple data sets are not just short term but can be used to identify long-term patterns and trends as well. Deciding and prioritizing the actions which must be taken in order to reach the best possible outcome among hundreds of scenarios is enabled by situational intelligence. This is where the golden nuggets of information in ocean of water are really unearthed. Now with this, users can understand the bigger picture and spot inefficiencies in their overall strategy that had previously not been possible to identify from the noise produced by huge volumes of data.
The Industrial Internet is going to transform the industry by making industrial machines more intelligent and enabling services using real-time data coming from sensors and machines. The intelligent devices will be able to take actions (to optimize processes, improve efficiencies, reduce costs etc.) based on insights generated from real-time data and analytics. This requires connectivity and interoperability across machines, fleets, plants, and cloud-based systems. The industrial communication standards will play an important role in providing seamless connectivity and integration between heterogeneous systems so that IoT created data will be stored, processed, analyzed, and acted upon close to, or at the edge, of the network rather than in centralized EDWs. By giving organizations a 360-degree operational view of historical, current and possible future performance, the applications excel at converting big data into actionable insight.
Image Source: situationalintelligence.net
Raghuveeran Sowmyanarayanan is a Vice President at Accenture where he works with clients to help identify and shape the “right” solutions for their various Business Intelligence (BI) and Information Management (IM) needs. Mr. Sowmyanarayanan also actively networks with industry leading BI & IM practitioners. He launched TDWI’s India Chapter in 2006 and served as its VP for 2 years. He has also organized and led multiple TDWI events in cities throughout India. Raghu began his career developing indigenous ERP products. Later, he got attracted to the concepts of Business Intelligence & Data warehousing and focused in those areas. Prior to Accenture, Raghu served as the BI/DW Lead for Energy & Utilities at Wipro Technologies and the BI/DW Lead for Genpact. Raghu is a well known author and speaker having published several articles in international journals like TDWI, DM Review, IT Toolbox & bicorner.com and spoken at multiple Information Management and Big Data conferences.