Azure Streaming Analytics:
5 cross-sector use cases

The faster you can react to data, the faster you can make business changes that improve revenue, tighten up efficiency and deal with challenges or crises. With data streaming, you can process high volumes of data in real-time to create outputs based on IoT generated data almost instantly – giving you the ability to monitor, analyse, visualise and, ultimately, react to incoming information in an instant.

It’s a game-changing data functionality with practical applications in virtually every sector, but before you jump into data streaming it’s important to understand the possible use cases for your own organisation, and whether they line up with your commercial objectives – and your data strategy.

Is data streaming the right solution for your data?

Data streaming isn’t compatible, or commercially viable, for every data source or outcome: other latencies may be more appropriate or cost effective, depending on what you plan to use it for or the data you have available. For instance, if you’re managing a large number of IoT enabled fridges, data streaming can be perfect for monitoring and managing their performance. However, if you’re managing smaller data sets or have less ‘urgent’ data analysis requirements, there may be better alternatives.

Azure Stream Analytics is a popular choice for organisations who are looking to explore or expand their data streaming capabilities. In a recent webinar, Agile Solution’s Graeme King, Geoff Jones and Wendy Hutton gave an in-depth demonstration on how to use the platform effectively: from configuring inputs (such as data streams from multiple IoT-enabled assets) to creating outputs (like data-driven actions and engaging visualisations).

So, before you jump into the deep end and start discovering exactly how to use the platform, how do you know whether Data streaming through Azure Stream Analytics could be right for you?

Here, we outline some potential use cases. It’s not a comprehensive list, as there are so many possibilities, but it should give you some idea of how streaming might bring benefits to your organisation.

Tracking visits to a website

Who can use it: e-commerce, SaaS providers or any business operating a website.

Tracking visitors to a website is traditionally a data-intensive process, but if it can be configured to output via a streaming messaging system like Azure Stream Analytics, you can get much faster insight into how your website is being used and take steps to improve customer experience as a result.

When site performance and visitor data is extracted from platforms like google analytics – a large deeply nested source of data, which is difficult to process – it can take a long time to gain insights. Streaming can enable you to cut your time to insight down significantly, to almost real time, benefiting web-based businesses like e-commerce retailers, SaaS providers or any business with a web presence.

Monitoring physical assets

Who can use it: organisations that need up-to-date information on asset performance.

Data streaming is an excellent way to monitor physical assets, such as devices in data centres, warehouses or factories. With data streaming into the analytics platform in real time, it’s easier to control and monitor physical situations and detect failures or problems in assembly lines, warehouses or environment-controlled facilities, like food storage areas or data centres. With instant alerts based on incoming data – such as temperature fluctuations or performance statistics – you can make sure that the right people are in the right place, at the right time to respond to issues when they arise.

Managing moving parts

Who can use it: businesses operating logistics, transportation or managing moveable assets.

Azure Stream Analytics can process geo-spatial data, allowing you to stream data from GPS sensors, helping you to keep track of any asset that can be fitted with a GPS device. Many organisations are using this to track assets within a physical space, such as warehouse-based vehicles, but it can also be applied to much wider geographical applications, like fleet management, remote vehicle monitoring, route validation and logistics.

Geo-spatial data can be collected from custom IoT devices, dedicated GPS devices or even just mobile phones enabled with GPS service, but what device you use to collect data can affect the quality and accuracy of the data you collect, so be aware of how much precision is needed before you start.

Monitoring physical health

Who can use it: Healthcare providers and care homes.

Data streaming can be used to improve monitoring in a healthcare scenario, enabling detailed data to be collected and analysed quickly when people are undergoing treatment, are under supervision in a care home, or are being monitored in their own home. By analysing data in real time and generating the right outputs in response to changes in data, dangerous conditions can be quickly flagged and responded to.

Machine learning

Who can use it: any business with the capability to create machine learning algorithms.

Azure Stream Analytics can be integrated with Azure Machine Learning to unlock some exciting outputs, such as analysing data streamed from human interactions like social media, customer chat or forum data. Pairing these algorithms with streamed data can be incredibly powerful.

If you want to see how Azure Streaming Analytics works, along with more detailed use-cases, watch the webinar and see how data streaming could support your commercial goals.