5 Best Practices for Real-Time Analytics
Traditional analytics practices are modeled on historical data. They often work in batches, guzzling data from past events and creating meaningful information that enables businesses to make better decisions. However, a key point worth noting is that the ‘decision’ will be better the next time the event happens. For today’s businesses, this ‘next time’ is too late. Customers are agile and may not wait till such time. It has led to the birth of real-time analytics.
“Real-time analytics is the use of, or the capacity to use, data and related resources as soon as the data enters the system.” It is often convenient to visualize the data going in a business’ systems, as a steady stream. With real-time analytics, the approach is not to wait for this stream to be stored elsewhere, analyzed and then presented. Instead, this ‘in-motion’ data is integrated and analyzed for the current context that is even relevant to milliseconds. In this post, we explore certain best practices that an organization can adopt to use this concept.
Capture the Elements Needed Real-Time
Most businesses have constant data feeds. Be it from customer interactions, or even vendor supplies. Before starting on a real-time analytics project, it would be worthwhile to prioritize the needs. That being said, the management should list out top hit areas which would affect the business instantaneously. In other words, “What is it that I need to know right now?” Once this is done, the processes and data elements involved in the same become easier to map out.
Deploy Tools, Dashboards, and Processes
The chosen areas and data elements would be used by the organization’s IT team to establish enterprise reports/ dashboards. These reports/ dashboards, need to be uniformly and widely spread to the user departments. The operations team needs to be upskilled, and standard operating processes (SOPs) will have to be changed to ensure that these real-time reports can be used repeatedly. For e.g. a capital financing company may need to tweak its process so that their customer support looks up a real time report on the customer’s credit history before assigning a ‘customer trust’ score.
Ongoing Monitoring Cock-Pit
While it is true that decisions get taken as soon as the data enters the system, the ‘in-memory models’ that work behind the scenes are also built upon algorithms using past data. This means that there must be system administrators and business leaders who constantly monitor the performance of real-time analytics from an aggregate top level. Any deviation from planned course needs to be manually corrected; this not only helps refine the analytics models but also leads to agile business rules.
E.g. For a production unit that does quality control using real-time analytics, a particular batch of goods that are loaded on the conveyor belt may get rejected because the system hasn’t encountered the same size/ weight before. However, it doesn’t mean that the batch is defective. The line managers would need to check whether it is a new product SKU.
Discover Trends for Which You Were Never Looking in the First Place
Striim (a real-time analytics solutions provider) Founder and CTO Steve Wilkes says, “That’s the real beauty of unsupervised machine learning. It can spot things that a human couldn’t because you’d have to correlate too many different things.” When real time analytics starts yielding patterns that are against conventional logic, it is time to go back to the board. In all likelihood, there is a new dimension that was missed out in the first place. The Business Users and analysts will usually arrive at a new insight altogether. An example could be how editors at BuzzFeed use real-time analytics. At times they find that a particular content piece is generating unexpected user feedback. On a deeper look, they discover the tone of the headline may not be well suited. Using their experience as journalists, they re-write the headline and share it again across their social channels. This closely marries real-time analytics and creative thinking (that computers are still not good at).
Build Discipline Around Real-Time Analytics and Business Process Management
It is important to empower the ground-staff to become ‘smarter’ through close coupling of real-time analytics and business processes. The analytics piece will only point a worker towards a data anomaly or interesting fact. The worker needs to be able to couple it with the relevant business process to take the right decision. For e.g. if a customer service person finds an incoming call from a customer who has mostly had issues related to mobile bills (the real-time CRM systems will flag it off immediately) it would be most appropriate to route it directly to the relevant account manager. Other that swift resolution it would also lead to a cutback of wait time for other calls in queue. Such kind of actions have to be rewarded and framed as model worker conduct. Only then will the right kind of discipline be built. And as in all cases in life, a disciplined approach always works wonders! You can check out how analytics in insurance course to have an in-depth understanding of real-time analytics.
Source : https://www.mygreatlearning.com/blog/artificial-intelligence/