These Fascinating Examples Show Why Streaming Data And Real-Time Analytics Matter More Than Ever

With the explosion of data in recent years, there’s more emphasis on data-based decision-making for all companies. But what if we could process data and act on it in real-time? What if we could be proactive instead of reactive to improve performance? Streaming data and streaming analytics now make that possible for almost every company in every industry to drive intelligence and action in real-time. And with an accelerated push for automation and digitization in a post-coronavirus world, it is clear that those companies that leverage real-time data are much more likely to gain a competitive edge on their competitors.

What is Streaming Data?

Dynamic data that is generated continuously from a variety of sources is considered streaming data. Whether that data comes from social media feeds or sensors and cameras, each record needs to be processed in a way that preserves its relation to other data and sequence in time. Log files, e-commerce purchases, weather events, utility service usage, geo-location of people and things, server activity, and more are all examples where real-time streaming data is created.

When companies are able to analyze streaming data they receive, they can get real-time insights to understand exactly what is happening at any given point in time. This enables better decision-making as well as provide customers with better and more personalized services. Nearly every company is or can use streaming data.

What is Streaming Analytics?

When there is a constant stream of data being generated and gathered, there needs to be streaming analytics to make sense of the data in real-time as well. Streaming analytics is when analytics is brought to the data in order to generate insights while the data is in the "stream" instead of stored. Traditionally, the analysis of data occurs once it's been captured and stored, and then any insights are "pushed out." Streaming analytics makes companies more agile and responsive to the realities and environments where they operate. It's important to recognize that streaming analytics allows organizations to be proactive with real-time processing and decision making instead of being reactive to the data.

Use Cases for Real-Time and Streaming Data

Any data-driven organization, which today is nearly all of them, can use real-time and streaming data to improve results. Here are just a few use cases across multiple industries:

Predictive maintenance: When companies can identify maintenance issues prior to breakdowns or system failure, they will save time, money, and other potentially catastrophic effects on the business. Any company that has equipment of any kind that has sensors or cameras—again, that’s most equipment these days—will create streaming data. From monitoring the performance of trucks, and airplanes, to predicting issues with complex manufacturing equipment, real-time data and analytics is becoming critical to modern enterprises today.

Healthcare: Just like in a manufacturing environment, wearables, and healthcare equipment such as glucometers, connected scales, heart rate and blood pressure monitors have sensors that monitor a patient’s vitals and essential body functions.  These equipment are also crucial for effective remote patient monitoring that supports clinicians who don’t have the bandwidth to be everywhere all the time. It’s literally a matter of life or death. Immediate insights can improve patient outcomes and experiences.

Retail: Real-time data streaming from IoT sensors and video are driving a modern retail renaissance. Brick-and-mortar retail stores can engage customers in the moment thanks to streaming data. Location-based marketing, trend insights, and improvements to operational efficiency, such as product movement or product freshness, are all possible with real-time insights. Understanding what a consumer wants when they want it “in the moment” is not only valuable in retail. Any company that is able to understand and respond immediately to what its customer wants in micro-moments will have a better chance of being successful, whether it's to deliver something a consumer wants to learn, discover, watch or buy.

Social media: With cries of “fake news” and instances of social media bullying continuing to rise, the need for real-time monitoring of posts to quickly take action on offensive and “fake news” is more important than ever. Under mounting pressure, social media platforms are creating tools to be able to process the huge volume of data created quickly and efficiently to be able to take action as immediately as possible, especially to prevent bullying.

Finance: On the trading floor, it's easy to see how understanding and acting on information in real-time is vital, but streaming data also helps the financial functions of any company by processing transactional information, identify fraudulent actions, and more. For example, MasterCard is using data and analytics to helping financial organizations quickly and easily identify fraudulent merchants to reduce risk. Similarly, by gaining the ability to process real-time data, Rabobank is able to detect warning signals in extremely early stages of where clients may go into default.

Energy and power: In the energy sector, companies are working to optimize fossil fuels and adopt more sustainable power systems. A continuous flow of data helps with predictive maintenance on equipment as well as to better understand consumer demand and improve business and operations.

Personalization of products and services: Companies can better respond to consumers’ demands to have what they want (and even what they don’t know they want yet) thanks to streaming data. From online news publications that serve up content a particular reader is most interested in to streaming services that recommend the next things to watch, personalization adds value to the customer experience but is only possible in real-time because of streaming data.

Transportation and supply-chain: Streaming data powers the internet of trains, makes connected and autonomous vehicles possible and safer, and is crucial in making fleet operations more efficient.

KPIs: Leaders can make decisions based on real-time KPIs such as financial, customer, or operational performance data. Previously, this analysis was reactive and looked back at past performance. Today, real-time data can be compared with historical information to give leaders a perspective on business that informs real-time decisions.

As you can see, streaming data is increasingly important to most companies in most industries. Successful companies are integrating streaming analytics to move their data analytics from a reactive to a more proactive real-time approach. The best ones will be thinking about integrating their real-time data with predictive models and scenario analysis to gain strategic foresight.

However, in order to harness fast and streaming data, organizations today need an end-to-end data management and analytics platform that can collect, process, manage, and analyze data in real-time to drive insights and enable machine learning to implement some of the most compelling use cases. Most importantly, they need to be able to do these with the robust security, governance, data protection, and management capabilities that enterprises require.

Join me on Aug 13th for a virtual event with Cloudera titled -- Streaming Analytics in the Real World -- where we will be highlighting top real-time use cases across 8 key industries.