The adjective real-time means the kind of computer responsiveness that reflects changes in external processes (such as change in database according to changing website activities) at a speed which a user perceives as immediate.
Following that definition, it is easy to understand the approach of real-time analytics – the dynamic analysis and reporting that is performed on data received less than a minute ago. It is also known as real-time data analytics, real-time intelligence, and real-time data integration.
For CRMs, it is no less than a wonderful gift. Imagine being able to make business decisions on an as and when basis! You could be talking to a customer, and in the span of seconds refresh your dashboard to get the latest updates, and continue your conversation on the basis of the understanding gleaned – all without waiting for a third party to prepare the data for you. For example, you could know the latest conversations about you going in the social media, know what price changes your competitors are doing on best-selling products, whether the latest news from a government agency is going to affect your stocks, and so on.
Real-time analytics makes it possible to respond to ad hoc queries regarding large data sets and see the business changes throughout the day, instantly, and so, you can make your business decisions on the latest status – which, as any professional would know, is absolutely crucial for making fail-safe plans. Let’s take a look at the technologies that make real-time analytics possible.
Processing in memory (PIM): When a processor is integrated with a RAM (generally SRAM or DRAM) on a single chip, the PIM is born. Unlike in traditional BI, where data in the form of multi-dimensional cubes or tables is loaded on to a disk and then queries run against it, PIM loads the data directly into memory, which saves time on data modelling, table design, cube building, and query analysis, thereby speeding up the process incredibly.
In-data analytics: As the name suggests, this technology builds analytics logic right into the database, which cuts the time wasted on transforming data and shuffling it into a separate analytics operation. The result: high-speed analysis.
In-memory analytics: Similar to the above application, in-memory analytics includes querying the data stored in RAM – which means drastically shortened query response time. So when data is stored right into the RAM (PIM) and analyzed there itself, several transactional processes are bypassed and many minutes saved.
Massive parallel programming: MPP in short, this system includes several processors working together on different parts of the same program while communicating with each other through a messaging interface. The setup for such an operation is the hard part, as it requires the thought to divide a single database among different processors and to allot work to each of them. MPP is very useful for applications like data warehouse and decision support system.
As you can see, real-time analytics is supported by various operations that allow superfast analytics and direct contact with data, which makes it suitable for not only e-commerce ventures, but also for a life-threatening situation like a cyclone – whose speed, strength etc., can be analysed in real time with the help of analytics.
Where else can real-time analytics deliver great value? Share your ideas in the comments section below.