Real Time Analytics: Facets, Insights, and Use Cases

Say you are the owner of a gaming analytics company or an online eCommerce store. You would naturally want to know how many users you are getting at a given point. How many of those users are regular ones, what product are they using and how would you be able to retain them. What is real time data analytics that does this for you? Let's find out.

Gone are the days when overnight dashboards used to be prepared in the morning. Now, every millisecond matters. This is why companies are now turning to real time data analytics. As is the name, real time analytics gives you a data-driven picture of your business in real time. Let's understand how it is rewriting the analytics game and why it is the future.

What is Real Time Analytics?

Real time analytics can be understood as a form of analytics spanning the entire analytics journey right from capture to consumption in just a few milliseconds. What it means is that right from data ingestion to cleaning and enriching it to providing the end-users, real time data analytics covers the entire process in a matter of few milliseconds, giving the impression that it is happening simultaneously.

While in traditional analytics, data is ingested from batches of data which are basically past treasure troves of data stored somewhere, in real time analytics data is fed from almost real time data. The same happens with the entire data analytics process and the end result is you get a real time analytics dashboard. In AI data analytics, this happens with the help of artificial intelligence.

What are the Facets of Real Time Analytics?

Real time analytics has some characteristic facets namely;

Data Freshness

Data freshness is an important aspect of the data analytics journey. The value of data is inversely proportional to the time passed after its creation.

The earlier you catch that data, the better numbers you can figure. Data freshness in this form of analytics is a given. Real time data analytics ingests data that is at the peak of its freshness.

Low Query Latency

Low query latency is a must in user-facing data applications. Anything that takes more than 50 milliseconds to respond to queries is ultimately degrading the user experience.

High Query Complexity

Real time data analytics is able to handle complex queries with low latency. Since, it has to deal with such large datasets, this particular feature is a must.

Query Concurrency

In a game, you might get thousands or even millions of requests concurrently. You should be able to handle those requests concurrently. Only then, it can be called real time data analytics.

How is Real Time Data Analytics Different?

Real time analytics is different from both batch analytics and streaming data analytics in the sense that it offers analytics truly in real time.

Batch data analytics works on ETL (Extract, Transform, Load), sourcing data from data sources (files, tables, applications), storing them in cloud warehouses and querying that data.

Real time analytics, on the other hand, does all this on an hour-to-hour and minute-to-minute basis, substantially affecting the operating business, except that it uses a mechanism other than ETL. The real time analytics database practically stores data as it streams.

Streaming data analytics also falls short of real time data analytics when it comes to high concurrency, low latency and long data retention capabilities of real time applications. There are several real time analytics tools that make it happen.

Real Time Data Analytics Use Cases

Sports Betting and Gaming

Real time data analytics can fairly help betting and gaming companies ramp-up the customer experience. It can help reduce the time-to-first bet and maintain leaderboards quite efficiently. The real time personalization feature is what gives the industry an edge ahead.

Inventory and Stock Management

It can immensely help online retailers stock up their inventory in real time. How much stock you need at a particular point of time can be answered with real time analytics, optimizing the customer experience further.

Improving Operational Efficiencies

Companies can monitor and optimize operational performance in real time, allowing them to detect and remedy the issues as and when they appear. Google analytics real time data analysis is a great example of data analysis of Google data.

Fraud Detection in Finance

Real time data analytics can be used to detect and prevent fraud right when they are happening. Incidences of fraud can be compared with historical events and stopped before they go through. Predictive analytics in finance is a great example of it.

Takeaway

Real time analytics is transforming the way traditional data analysis works. Seeping fresh data in real time, this form of analytics is helping various industry use cases. With a minimum query latency and high query complexity, real time analytics is creating new paradigms in the analytics world.

If you're looking for help on a user-facing analytics project, you want to try BluEnt. Our engineers are equipped to innovate faster and bring costs down as they achieve latency in milliseconds, delivering a snappy yet effective experience. Do take a look at our demo dashboards before you get in touch with our domain experts.