Jay Nanwani

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.

Reverse engineering or Backward engineering is the process of analyzing a product or a device's details to understand its design, function, and performance characteristics by a structured method of dismantling, dissecting, inspecting, and studying the inner profile/structure of the product.
Organizations invest in the exercise of reverse engineering with the sheer goal viz. to identify the scope of improvement in an existing product’s value and create a better version of it.
Reverse engineering supports the business excellence strategy of an organization in the following ways:
a.)    Innovation: Insights derived from reverse engineering enable organizations to understand the strengths and weaknesses of their existing product or competitor’s offering. These insights will become inputs for designers or engineers to improve the existing weaknesses through innovation and create a better offering for the customer.
For example: Tesla has reverse-engineered multiple competitor cars to understand their overall architecture, one of them was the BMW i3 which was a market leader in terms of battery technology. This helped Tesla to improve their electric car’s performance and overall efficiency.
b.)   Cost reduction: Any/every product is the result of the transformation of a raw material to a finished good through various manufacturing processes. Through reverse engineering, one can backtrack all the steps performed to produce that product. Thus, helps in cost reduction by simplifying the production process, optimizing raw material cost, resource deployment, time to deliver, etc.
For example: Mobile manufacturers such as Oppo, and Vivo are constantly reverse engineering their competitor’s mobile devices viz. Samsung and Apple to understand its design, features, and production process. This exercise helps them to offer their product in the market with similar designs and features at reduced cost and gain maximum market share in the Indian market.
c.)    Competitive edge: To stay ahead in the race and continue to be the product market leader companies around the world keep on analyzing their competitor’s products and build features and functions that help them to always have a competitive edge over their competitors.
For example: Nintendo a video game company continuously improves its software and gaming console by reverse engineering competitor’s gaming platforms and hardware architecture.
d.) New Product Development: Many times organizations get stuck in the development stage and it becomes very difficult for designers to bridge the gap between market requirement and their product offerings. By conducting reverse engineering on competitor products, designers and engineers can get influential insights that help them to continue and conclude the development cycle of a new product enabling rapid product launches.
For example: While designing a new aircraft model the Boeing design engineers reverse-engineered competitor’s components such as engines, airframes, avionics systems, etc. which helped them understand the design requirement as well as regulatory compliance. Thus, reverse engineering helped Boeing engineers reduce their development efforts.