Analytics of Things (AoT) is the work of preparing, connecting and analyzing data from sensors and connected devices so that people or systems can use it to make decisions or take action. IoT devices generate observations; AoT supplies the context and analysis that can turn those observations into operational insight.
What is Analytics of Things?
AoT is an industry term, not a sharply standardized technical discipline. A useful working definition is the integration, management and analysis of Internet of Things (IoT) data to produce information that can guide decisions. Richard Hackathorn’s 2016 Bolder Technology report describes it as “the integration, management and analytics of the IoT sensor data required to distill business insights.” The report is an executive perspective sponsored by Teradata, so its definition and case studies should be understood in that context.
IoT refers to physical devices connected to networks that collect or exchange data. AoT is the analytical and decision-making work applied to that data. Simply installing sensors does not make their output useful: readings often need to be checked, interpreted alongside other information and delivered to someone or something able to respond.
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Thomas H. Davenport put the goal this way in a June 18, 2015 Harvard Business Review article: “Analytics are crucial to make connected things smart—and to allow both products and organizations to make these new sources of data actionable.” His framing highlights that analysis may inform people as well as automated systems.
How does IoT data become useful?
A practical AoT workflow links several activities. They do not have to run in one fixed order or on one platform; the right design depends on the question being answered and how quickly a response is needed.
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- Collect observations. Sensors and connected devices record measurements, events or location. A device may also monitor conditions or perform local control.
- Filter and prepare the data. Validate readings, address missing or inconsistent values, and decide what to transmit or retain. A 2018 SAS proceedings paper explains that filtering at the edge can reduce unnecessary transmission and storage, while emphasizing that what to keep depends on the use case.
- Add relevant context. Relate a reading to information such as an asset’s history, operating conditions or data from other sites. A device feed alone may show that a value changed without explaining why the change matters.
- Analyze for the decision at hand. Methods range from dashboards and trend analysis to anomaly detection, prediction, maintenance planning and spatial-temporal analysis.
- Put the result into action. Send an alert to an operator, inform a business decision or trigger a system response. The intended recipient and its ability to act should be part of the design, not an afterthought.
For enterprise analysis, Hackathorn’s report describes curated sensor data being integrated with other information. Teradata’s IoT overview presents a vendor perspective in which relevant data can move from a data lake to a warehouse for analysis alongside business information. These are examples of approaches, not requirements for every AoT project.
What kinds of analytics are used?
- Descriptive analysis: dashboards and summaries show what is happening or has happened, such as energy use over time.
- Anomaly detection: identifies behavior that differs from an expected pattern, which can help flag equipment or readings for investigation.
- Predictive analysis: estimates likely future conditions, such as asset degradation, to inform maintenance planning.
- Spatial-temporal analysis: studies how events relate to both location and time, especially for moving objects and streams of observations.
- Optimization: uses observed conditions and analytical results to improve operations or resource use, subject to the organization’s goals and constraints.
These categories can overlap. A project may begin with a monitoring dashboard and later add anomaly detection or prediction, but a more complex model is not automatically more useful. The method should fit the decision and the quality and characteristics of the available data.
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What are examples of IoT analytics?
Building and energy operations
The SAS paper describes comparing power readings from parking-lot lights to identify a light behaving differently from its peers. The value comes from comparing measurements in context, rather than treating each reading as an isolated number.
Solar-farm monitoring
SAS presents robust principal component analysis as an approach to reducing data and detecting anomalies in solar-farm measurements while preserving regression-model accuracy. This is a technical example from the paper, not a guarantee that the method will suit every installation.
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Aircraft-engine maintenance
The same paper discusses using multivariate sensor data to monitor engine degradation and support maintenance scheduling. The analytical output can help inform a maintenance decision; it does not by itself establish when an organization should act.
Vehicles and moving objects
A 2016 IBM Research paper examines spatial-temporal analytics for streaming data from instrumented moving objects and names autonomous driving, usage-based insurance and fleet management as application areas. It is a technical example published in 2016, not evidence of current commercial deployment.
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Enterprise and city operations
Hackathorn’s 2016 report examines seven corporations and one city government, with examples involving operational efficiency, maintenance, energy and services. The cases were based on public articles and anonymous interviews, and all organizations in the sample were Teradata customers. They illustrate possible uses; they do not establish typical results across organizations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where should IoT data be processed?
Processing can happen close to devices, in a centralized environment, or across both. There is no universally correct edge-versus-central answer in these sources. Compare the options against the decision the system needs to support:
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| Design question | Why it matters |
|---|---|
| How quickly must a response happen? | A time-sensitive response may need analysis near the asset; a question that can wait may suit centralized analysis of longer histories. |
| What data must move and be retained? | Filtering can reduce transmission and storage, but discarded data may be useful for later investigation. Decide based on the required action and evidence. |
| What context is needed? | Asset histories, other sites or business information may be required to interpret a sensor stream meaningfully. |
| What is the analytical objective? | Monitoring, anomaly detection, forecasting, maintenance and spatial-temporal analysis call for different data and methods. |
| What are the data patterns? | Rare failures can make a dataset imbalanced, while seasonal or cyclic behavior can affect how normal patterns should be interpreted. SAS discusses these as relevant modeling considerations. |
| Who owns the response? | Identify the operator, team or system that receives the result and whether it can make the operational change the analysis suggests. |
How should an organization start an AoT project?
- Name the decision first. Specify what a person or system should do differently and what outcome would make the effort worthwhile.
- Choose the signals that can inform it. Determine which device observations are relevant, how often they are needed and what context is necessary to interpret them.
- Set data-quality and retention rules. Establish how readings will be validated and which data must be kept for immediate response, longer-term analysis or later review.
- Select a suitable analytical approach. Start with the simplest method that answers the question, and assess whether the data supports more advanced analysis.
- Connect analysis to an owner and action. Decide how results will be delivered, who is responsible for responding and how the organization will learn whether the response helped.
This sequence reflects the emphasis in the cited sources on business value, data integration and actionable results. It is a planning framework, not a prescribed product stack or a promise of financial return.
Quick Recap
What Analytics of Things does not guarantee
- It is not just sensor installation. Data preparation, context, analysis and a path to action are what distinguish AoT from simply collecting connected-device readings.
- It does not require every calculation to be real-time or at the edge. Timing, data movement, retention and the decision itself determine where processing is useful.
- It does not promise a particular return. The cited materials provide examples and recommendations, not a broadly comparable present-day performance benchmark.
- It has no established current market-size or average-return figure in these sources. Older IoT forecasts or vendor claims should not be treated as comparable AoT statistics.
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