What is Real Time Data? Real-time analytics help businesses to respond before a incident occurs. These techniques doesn't allow the disaster to happen. When comparing with batch process analytics may take hours or even days to process the results. Subsequently, batch analytical applications respond just before or after the incidents occur.
While real-time analytics and big data are both trending, it seems that real-time big data analytics, which is their combination, should be a very promising initiative, and many businesses should be desirous of it. Let’s find out if this is really so.
You will find this article richly supplied with the examples of real-time customer big data analytics. We’ve done so for the reasons of ease and consistency. Though there are more areas where real-time data analytics can be applied.
If you are going to skip this section because you think there can’t be two definitions of real-time, please don’t be surprised – there are. In fact, the definition of real-time is extremely vague and it differs a lot from company to company or, to be more exact, from business task to business task.
Our consulting team has come up with the following definition:
Real-time big data analytics means that big data is processed as it arrives and either a business user gets consumable insights without exceeding a time period allocated for decision-making or an analytical system triggers an action or a notification.
As real-time is often confused with instantaneous, let’s clarify the time frames for data input and response. As far as data input is concerned, the real-time processing engine can be designed to either push or pull data. The most widespread example is a push option with an incessantly flowing high-volume data (also known as streaming). However, the real-time processing engine is not always capable of ingesting streaming data. Alternatively, it can be designed to pull data by asking if any new data has arrived. The time between such queries depends on business needs and can vary from milliseconds to hours.
Correspondingly, the response time also varies. For instance, a self-driving car requires a very fast response time – just several milliseconds. If we deal with sensors installed, say, to a wind turbine and they communicate a slowly growing gearbox oil temperature, which is still below the critical level but higher than normal, we need one-minute response time to change blade pitch, thus offloading the turbine and preventing machine breakdown or even fire. However, a bank’s analytical system would allow several minutes to assess the creditworthiness of an applicant; and a retailer’s dynamic pricing can take up to an hour to update. Still, all these examples are considered real-time.
Not all the companies go for real-time big data analytics. The reasons could be different: the lack of expertise or insufficient funds, the fear of the associated challenges or overall management team’s reluctance. However, those companies who implement real-time analytics can gain a competitive advantage.
Let’s say you are a fashion retailer who would like to take the advantage by delivering a top-notch customer service.Analyzing big data in real time can help bring this great initiative into life. For example, once a customer is passing by a retailer’s store, they get a push notification on their smartphones that serves to incentivize them to enter. Usually, it’s a personalized promo offer that is based on the customer’s purchasing or even surfing history on the website. Once a customer is in the store, the staff gets a notification in their mobile apps.This makes them aware of the customer’s latest purchases, overall style preferences, interest in promotions,a typical spend, etc. It looks like a win-win situation for both customers and retailers,doesn’t it?
An ecommerce retailer can also achieve better performance by analyzing big data in real time. For instance, they can reduce the number of abandoned carts. Say, a customer has gone that far, but for some reason, they’ve decided not to finalize their purchase. Still, there are good chances to incentivize them to change their mind. The system is turning to the customer’s profile data, as well as the purchasing and surfing history to compare the customer’s behavior with the conduct of other customers from the same segment and their response to different actions in a similar situation.