Using data for business is no longer optional if you hope to keep up. Every day, over 2 exabytes of data are generated. There are a lot of valuable insights that can be garnered from accessing and analyzing some of that data.
Part of the reason for Big Data's power and popularity is the speed with which it empowers your business decisions. What used to take months can now take mere seconds to pull up, analyze, and inform your decisions.
It's difficult to overstate the power and possibilities of real-time data like what you can get from streaming data architecture.
To help give you more ideas of what you might do with stream data, we've put together a guide about data streaming architecture so you can learn how to use it in your organization!
To understand streaming data architecture, first, you need to understand streaming data itself. Streaming data is defined as the continuous flow of data from multiple sources. This continual flow of data requires special processing software to process, store, and analyze data as it comes in real-time.
Streaming data presents some unique challenges to working with it properly, however. First of all, streaming data tends to come from all over and all at once. This means your streaming data architecture needs to be set up to process data in many different formats.
Once the data has been consolidated into a consistent format, it can be analyzed, sorted, processed, and stored through one central command console.
Streaming data can come from a wide variety of sources. Some common sources of streaming data might be:
This means you need to put some solutions in place to work with streaming data technologies like streaming data analytics.
Most traditional systems are made up of two main components - storage and processing. For data streaming technologies, storage needs to be equipped to store large quantities of data consistently and logically, for starters.
Data streaming processing needs to be powerful enough to consume, sort, and act on the data you've stored from your data streaming technologies.
These two requirements can cause problems when working with traditional technology like legacy databases. This is part of why you need to put streaming data architecture in place.
One real-world example of streaming data would be data from a ride-sharing app. The ride-sharing app consolidates data from different sources to deliver a smooth, continuous experience for all involved. Some of this disparate data might include:
These data streams are necessary to determine how much a ride will cost, which driver to assign, and deliver an estimated travel time.
Setting up this kind of system requires making some decisions. Streaming analytics can be set up to analyze real-time data, for instance. For example, sometimes developers choose a Batch Processing setup for data streaming analytics, which is slightly less resource-intensive.
Batch Processing involves pulling a predetermined amount of data which is then analyzed, sorted, and processed in the storage component of your analytics system.
Collecting data is only the beginning of a data-driven business strategy. In fact, it can even be detrimental as accruing data is just more busywork unless you're actually able to use it. The data you collect is unlikely to be of much use months after the fact.
In today's accelerated business world, you don't have time to wait for batches of data to be collected, stored, and processed. Much of your competition is likely already using real-time data analytics, giving them a competitive edge if you're still relying on slow, cumbersome data strategies.
Streaming data also empowers you and your organization to stay up to the second with your customers and in your industry. Data streaming analytics alert you to potential problems as they're happening.
Say you've got a filter set up to process data from your social media accounts, for instance. You might configure this filter to monitor your social media accounts for any negative mentions of your brand. If this filter is set off, you might have a bot send an automated message with a special incentive like a discount.
This way, you've curtailed any possible bad press and negative reputation your company might gather from such an incident and spin it into positive PR instead.
That's just one possible application of data streaming analytics, as well.
Data streaming technologies can also improve your customer's experience. Real-time data analytics are a critical component in product recommendations, for instance. If you have a streaming service, like an on-demand video app, putting real-time analytics in place can give your viewers recommendations based on things they've just watched.
Any of these features will help to give your company and products a competitive edge. And in today's crowded marketplace, we all need as many advantages as we can get.