Want to know how to get a grasp on big analytics software? Learn how companies use this data to their advantage in this guide.
Big Data Defined
Real-time big data is data that’s processed as it arrives. Once it arrives either users get consumable insights without exceeding the time period allocated for decision making or analytical systems that trigger a notification or action.
As real-time gets misinterpreted with instantaneous, let’s explain the timeframes for data response and input. As far as the data input is concerned, the processing engine can be designed to either pull or push data.
The most common example of the push option with flowing high-volume data (i.e., streaming). However, the real-time engine isn’t always capable of using streaming data. Alternatively, it can be used to receive (pull) data by asking if any new data has arrived. The time between these queries depend on business needs and can vary from milliseconds to a few hours.
Correspondingly, the response time also changes. For instance, self-driving cars have a fast response time — a few milliseconds. If we’re dealing with installed sensors and attaching them is a wind turbine, and they communicate in a slowly growing gearbox oil temperature, which is higher than average, we’ll need a one-minute response time to change the blade pitch, which offloads the turbine and prevents the machine from breaking down or catching fire.
However, banks analytical system takes at least several minutes to test the quality of the applicant; and retail pricing will take over an hour to update. Still, these are examples of real-time analytics.
When running a big data analytics company, you have to utilize the data presented so you can thrive against the competition.
Not all companies use real-time analysis. The reasons are different: insufficient funds, the lack of expertise, the management team’s reluctance, and fear of the associated challenges. However, reputable companies like Famoid who use real-time analytics will gain a competitive advantage.
Let’s say you’re a fashion retailer who wants to provide high-quality customer service. Analyzing big data can help bring this vision to life. For instance, once a customer passes through a retailer’s store, they will get a notification on their smartphone that incentivizes them to enter.
Once the customer is in the store, the staff will receive a notification on their apps. This makes them aware of the customer’s style preferences, latest purchases, interest in promotions. Looks like a win-win situation for retailers and customers, doesn’t it?
Eccomerce retailers can receive better performance by analyzing data in real time. For example, it can reduce the number of abandoned carts. For instance, let’s say a customer has gone that far, but have not completed the purchase for some reason.
Still, it’s a good chance to incentivize your customer’s ad change their mind. The system turns to the customer’s profile data and their surfing and purchasing history to compare the customer’s behavior with the actions of customers from the same segment and their responses to a similar situation. Based on their analysis, the system creates the most possible solution — for instance, create a discount.
What’s the Future for Big Data Analytics?
As big data analytics grows, we’ll start to see more solutions to our daily business problems. This gives companies the ability to predict behavior and create solutions based on the data given to them. It will be a bright future for big data, and we can’t wait to see what advancements will occur until then.