Q 651. Compare Real-Time Analytics with Traditional Analytics. What are some of the key challenges faced in adopting real time analytics?

 

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	Real-Time Analytics	Traditional Analytics
Definition	The process of preparing and measuring the data after the data enters the database is called Real-time analytics. It allows organizations to quickly respond to changes in the market and operational issues. It also allows organizations to prevent problems before they occur.	Traditional analytics is dependent on historical data which can be days old.
Processing Speed	As soon as the data enters, it gives us information on the occurring events.	It involves batch processing. Here the data is collected and stored and then analyzed in scheduled intervals.
Response Time	It allows for timely decision-making	The response may be delayed which might in turn lead to missed opportunities
Effectiveness of data	It provides us with the most recent and accurate data for real-time analysis	It deals with historical data which might not give us the exact picture of the current state.
Requirement	To provide the most recent and reliable data, it requires specialized infrastructure.	It uses the existing databases.
Nature of Complexity	It is more complex as it manages high volumes and real-time data	It is easier and simpler to manage.

Traditional Analytics (also known as Historical Analytics) is the methodology of interpreting patters of data from historic values. The data that is used for analysis is based on information that has already occurred in the business. Using this data helps companies understand their business performance and the variation of their key metrics over time.

 

Data trends from the past makes it easier to understand how the metrics will perform in the future. The larger the date range, the more accurate the output. This prediction modelling helps businesses plan for the future.

 

For instance, historic call volume in a BPO will help companies predict future call volumes and spikes and help in better capacity planning.

 

Real-time Analytics considers data trends that are live. It gives you insights on how the current performance is and show up results that can be stratified in to different segments. For instance, a real-time dashboard depicting current Service Level performance, quality scores etc. While Traditional analytics is static in nature, Real-Time analytics are dynamic.

 

Real-Time analytics helps teams and individuals monitor their current process performance and make changes as per the requirement. For instance, a manager views a real-time dashboard and sees that there are 3 analysts in skill A who are idle and there is a call queue for analysts in skill B, if the skill A analysts are cross-trained, the manager can utilise them in the skill B queue.

 

With real-time analytics, businesses don't respond to issues after they've occurred, as in the case of traditional analytics. They make decisions basis current data output.

 

Challenges in deploying real-time analytics:

 

While real-time analytics is overwhelmingly more advantageous than traditional analytics, industries face some of the below challenges in implementing them:

 

1. Data sanity - Since data used is real-time, the accuracy of the data becomes of vital importance. If even a single data point is incorrect, it may provide a highly incorrect output. While in traditional analytics, historic data can be re-checked, that is not possible in real-time analytics.

 

2. Change acceptance - Since traditional analytics has been used for years now, changing leadership mindset in adopting real-time analytics can be seen as a challenge.

 

3. Implementing right solutions - The output from real-time analytics is to be scrutinised further and fit well in to the business. Traditional model gives us definitive action point. The same is difficult in the real-time scenario.

 

4. Cost - The cost associated with real-time analytics is much higher than deploying traditional methods.

Traditional analytics is a conventional process of analyzing a batch of data sets collected over time. Usually, processing of the data in conventional processes occurs offline. This method involves longer processing times and delays in getting meaningful insights from the data set.
Decisions are taken in retrospect as the data is processed offline and it is similar to work with historical data.

Real-time analytics is a discipline in which analytics is completed as soon as new data arrives in the database. This method provides rapid insights and allows stakeholders to make timely decisions. This enables organizations to quickly respond to dynamically changing conditions, seize opportunities, and mitigate risk more effectively.

A key distinction between traditional and real-time analytics is in terms of scalability. In the conventional approach, it becomes complicated to accommodate sudden data surges and the required volume to be processed and it will call for expensive resource deployment. Real-time analytics platforms are designed for scalability and these platforms can dynamically utilize resources to accommodate sudden surges in data processing demand, making the analysis consistent and reliable.

Common challenges that data engineers face in real-time data processing are:
a.) Handling large volumes of data: Analytics would yield an optimum result if a large set of data is processed for any given objective. Processing this high-volume data sometimes creates a bottleneck for engineers as they try to figure out how to manage and make use of this large amount of data.
b.) Managing high variety of data: Usually every data source does not always follow a standard template hence data collected from these sources would have a high variety of structures, formats and it becomes difficult to process and transform this unorganized data and make sense of it for the stakeholder
c.) Quality of data: There is a saying that “garbage in is garbage out”. Data will only be useful to derive insight s if that data is accurate. It is imperative that while processing inaccuracies present in the data are identified and reported for the user for effective decision-making. Identifying such noise in real time is also a key challenge for real-time analytics.
d.) Infrastructure requirement: Real-time analytics requires processing complex and high-volume data as soon as it enters the database. This would require creating and managing such advanced infrastructure that can handle such kind of speed and velocity of data processing. The cost of establishing such a level of infrastructure would be very high.
e.) To maintain low latency and high performance: Real-time analytics aims to provide quick meaningful insights and analysis to the user. This can be a key challenge to maintain such low latency and quality of insights in real-time by minimizing processing delays, optimizing data pipelines, and rapid query performance.

All the published answers are correct. The best answer has been provided by Jay. Well done!