Real-time data analytics, one of the most crucial parts of Big data analytics today, is the most challenging part when it comes to the question of implementation for enterprises. Though real-time data analytics gives you a more in-depth insight into the business data by handling streaming data sources, there are multiple challenges associated with it. Because storing such a huge volume of data and analyzing them on a real-time basis is entirely a different ball game.

Additionally, real-time data processing often requires scalability, fault-tolerance, predictability, resiliency against stream imperfections and must be extensible. Before we dive into knowing what all challenges are there and their possible solution, let’s discuss real-time analytics definitions.

What is Real-time data analytics?

Real-time data analytics architecture allows analysis of data once the data becomes available. Hence, users can obtain insights immediately after the data enters their system. Thus it is all about the high availability of data and low response time. Furthermore, when analytics through batch data processing takes a longer time, like hours or even days, we can get instant insights using real-time analytics to yield results.

It saves both money and time for the business as the business can react without delay and prevent any unforeseen problems. Real-time analytics mainly follows lambda architecture or kappa architecture. However, one size may not fit all. Tools like Apache Flink or Spark streaming, Amazon Kinesis is a real-time analytics platform.

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Challenges of Real-time data analytics

Challenge #1 Real-time data analytics architecture

Real-time data analytics architecture must be capable of processing data at high speed. But depending on the data source and type of data, the speed may vary from milliseconds to minutes, and the architecture should be built similarly. The second most important thing is that the architecture should handle the spike of data volume and be scaled up as and when required. Besides, the architecture should be able to capture real-time as well as offline analytics of data. At the same time, running real-time analytics and offline analytics may create conflicts. So, the designed architecture must be able to handle this conflict and address the fundamental architectural issues.

Challenge #2 Real-time is a contradictory term itself

'Real-time' is a confusing as well contradictory term. It may be for instantaneous results for some stakeholders, and for others, it is okay to wait for several minutes. Thus, it is necessary to invest significant time and effort to clarify the requirement. Besides, it is necessary to bring your team at the same line so that they unanimously agree on the requirement of real-time, the type of data required for analysis, and the sources to be used for the data. This is the utmost required because unless the interpretations are clear, it may cause inconsistent requirements.

Challenge # 3 Understanding the need for internal process

When an organization looks for or invest in real-time data analytics, there must be some internal motivation behind it. Ultimately it is to improve the internal process. However, when a team gets involved in real-time analytics, several tasks line up with it. Some of them are like –

• requirements gathering
• designing the solution's architecture,
• selecting the right technology stack,
• solving issues related to hardware and software

Thus to maximize the benefits of real-time data analytics, it is necessary to keep in mind that the whole process improves the internal process, and not the analysis is the ultimate goal.

Challenge #4 Change in an organization comes with many resistances

Implementing real-time data analytics in an organization following traditional intelligence methods could sometimes be a huge challenge. Primarily the challenge comes from the end of existing employees. As real-time data analytics may open up new directions towards organizational goals and new opportunities, sometimes it seems like a disruption for the existing employees. Consequently, it results in resistance from the employee end towards the new change.

To avoid disruption, management should clarify the reasons for the shift to real-time analytics and the possible opportunities it brings, and convince the employees of these ideas. It is quite natural that many technical obstacles can come out during the change. Hence, appropriate training should be organized to build confidence among the employees. Furthermore, the organization must make sure that its employees understand the real-time data analytics system's benefits and prepare themselves to work with it successfully.

Challenge #5 New way of working must be implemented

Real-time data analytics is a whole new model of working. In a traditional analytics system where organizations usually get their insights once in a week, real-time data analytics gives you insights every second. So, it is an entirely different approach to working and analysis. Similarly, the organization's work culture should be in line with this faster analysis method so that it can affect the business appropriately.

Final thought:

Actionable metrics, like real-time data analytics, always help us to make better and smarter decisions. In real-time data analytics, action can be taken on data immediately so that data can be accessed within a few minutes after an event takes place. However, when you implement real-time analytics, you will face a variety of challenges. These challenges, as mentioned in the above sections, are not simple in reality. Moreover, they will not be solved unless proper action items are considered wisely.