In the pursuit of advanced analytics maturity, some organizations focus a little too much on the "big" aspect of big data. Certainly, big data analytics requires the collection of vast amounts of information, but that's only one part of the equation.
The "3 Vs" of big data — volume, variety and velocity — indicate that success is not measured by the amount of available information alone.
The other two "Vs" suggest that sophisticated analytics projects are characterized by a wealth of disparate data sets and the ability to quickly gather and process incoming information to produce valuable insights.
Hitting all three of these marks is challenging, but businesses should still give each one their due respect within big data strategies. To cover the velocity requirement, project leaders should consider implementing real-time stream processing solutions.
"Businesses require in-the-moment analysis of information as it flows in."
Data analysis in real-time
There's a common misconception that advanced analytics involves gathering data, storing it in a large repository and then combing through it to wring out fresh insights. Although that approach is certainly valid in some cases, other instances require in-the-moment analysis of information as it flows in. As LinkedIn Influencer Hossein Eslambolchi explained, capturing and analyzing data while it's in motion is, if anything, more important than doing the same for static data.
"Data in rest loses its value exponentially over time, thus make it not as effective as data in motion which requires real time processing," Eslambolchi wrote.
Real-time stream processing can facilitate in-motion data capture, allowing organizations to analyze massive quantities of information instantaneously without delay.
What does real-time stream processing look like?
When executed correctly, real-time stream processing can help organizations keep up with the current of incoming data and pull meaningful insights at a moment's notice. One of the more compelling use cases for this technology is in the financial services sector. Suspicious patterns that could indicate the presence of fraudulent activity may develop within a matter of seconds. In that scenario, by the time analysts get to their at-rest data, it's already too late. Real-time stream processing allows these specialists to pull in that information as it develops, vastly enhancing fraud detection efforts in the process.
How to make stream processing a success
Project teams can take steps to optimize their real-time stream processing setup and get the best return on investment from it. The first is to have a reliable system in place to properly prioritize data based on current and ongoing needs. Speaking with TechTarget, industry veteran Steve Wilkes noted that analytics teams need to determine what real-time data they absolutely need in the moment and tune out the rest of the noise.
An important note to keep in mind is that some data will lose its value almost immediately. So project leaders must know when it's time to prioritize more volatile data and when it's time to focus on information with a longer shelf life.
Another critical facet of real-time stream processing is integration. To truly succeed here, organizations will need to be able to integrate disparate systems and facilitate real-time data processing across each one. In this way, analytics teams can be sure they have access to the most sets of data possible and incorporate whatever information needed to drive projects forward.
Implementing real-time stream processing within your data warehouse strategies is becoming more important every day. The number of information sources and connected devices continues to grow, driving the need for faster and more responsive data collection and analysis capabilities. Organizations that put off these changes run the risk of falling behind the competition. Now is the time to overhaul data warehouses to incorporate this technology and put your business in the best position possible for future success. Clarity can help your transformation!