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IoT Analytics: How to Leverage for Business Impact

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IoT Analytics: How to Leverage for Business Impact

Learn how you can leverage IoT analytics to impact your business.

· IoT Zone ·
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Learn how you can leverage IoT analytics to impact your business.

The buzz surrounding the Internet of Things (IoT) is sometimes misunderstood. IoT devices aren't limited to popular consumer electronics like smartwatches and voice assistants. They also include incredibly sophisticated devices like tiny biomedical implants or massive water-management systems.

You may also like: What Is Business Intelligence?

IoT devices generate a lot of data. To truly leverage IoT as a transformation agent, an enterprise needs to combine the IoT data from a variety of devices and sensors with data from disparate sources that might carry meaningful data about the business. These data include structured and unstructured data and could range from PDFs, spreadsheets and documents to JSON datasets, XML files, clickstream data, and even images and videos.

Many of these data sources have remained untapped by legacy applications owing to technology limitations. Collectively, they've come to be labeled as dark data. Yet they can be illuminated using the right IoT data analytics solution.

Deriving Maximum Value From IoT Data

Data generated by IoT devices are not always ready to be consumed by ordinary business intelligence (BI) tools. Nor are they ready for running IoT predictive analytics. This is because the semi-structured or unstructured data cannot easily be joined with the data from other enterprise data sources to deliver insight.

Here are a few steps involved in processing data for IoT analytics and getting business value from it.

Ingestion: During this initial step, the data streams from IoT devices are taken in and processed. Real-time notifications can also be generated. In the end, data is saved in its raw format in a centralized repository like a data lake.

Preparation: The IoT data along with those from other sources are combined in the data lake before being cataloged for better understanding and visibility. The data are then sanitized and transformations are applied to make them available to support exploratory data analysis.

Part of the data then can be pushed to an enterprise data warehouse through "extract, transform, load" (ETL) processing. At the end of this stage, the data can be made available to support canned and ad hoc queries.

Discovery: The processed data from the data lake or data warehouse can be fed to the BI tools to create interactive dashboards. These interactive dashboards along with self-service reporting are what help turn data into human-readable insight. This enables decision-makers to see the big picture as well as granular details to make more informed decisions.

Prediction: Business users are no longer stopping at the discovery phase. Predicting future outcomes through various machine-learning techniques is the current norm. As the pool of available data grows over time, IoT predictive analytics can use longitudinal data to recognize trends and forecast future scenarios.

collection of data used for IoT analyticsIoT Analytics in Action: Healthcare Analytics

Let's consider the potential of an IoT analytics platform using healthcare as an example.

In this hypothetical scenario, we see a user's biomedical information gathered by a specialized heart-monitoring IoT device. Data such as heart rate, blood pressure, oxygen saturation, and activity level is transmitted in real-time to a healthcare analytics platform. Additionally, data from third parties such as insurance companies and doctors' offices may be available electronically in a variety of formats.

Your solution needs to be able to pull all available patient data and merge it with permanent (e.g. the user's age) as well as historical data (e.g. changes in heart rate and blood pressure). A healthcare professional can then view this synthesized data as easy-to-understand graphs or charts.

Using IoT predictive analytics, the platform can even alert the healthcare professional to an impending medical emergency. That's how IoT analytics can wind up saving lives.

The first step on your analytics journey begins with equipping yourself with the right tools, platforms, and people to make it happen. What are you doing to better power IoT analytics in your business? Let us know in the comments below.

Further Reading

What Is Business Intelligence?

[DZone Guide] The Internet of Things: Connecting Devices and Data

Topics:
iot ,business analytics ,data ,iot data ,smart data ,data-driven ,analytics

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