Optimising Real-time Streams

How to Optimise Real-Time Data Streams Generated with IoT?

The Internet of things, popularly known as IoT, is creating endless possibilities for businesses. But is that all? The answer is No. IoT significantly contributes to the lives of individuals and society as a whole. Big Data plays a vital role in this ecosystem.

IoT and Big Data are not stand-alone concepts and are increasingly becoming interconnected with each other. Businesses heavily rely on a large amount of data that direct their course of action. IoT applications are constantly generating such data. For instance, a footfall tracker in a clothing shop placed during the festive season captures how many customers come in a day, or a food delivery app tracks the delivery time every time you order and customer satisfaction associated with delivery time (through rating systems). All of this serves as crucial data that aids the organisations in choosing the next course of action.

However, as the volume of data keeps rising, the question is, will batch processing be sufficient, and can organisations ensure data quality through comparatively traditional methods? It is unlikely that batch processing will suffice organisations’ information needs today. So what do they need? The answer is streaming analytics or real-time data streaming.

What is Streaming Analytics?

Streaming analytics or real-time data streaming refers to the processing and analysis of fast-moving data generated in real-time. There can be various sources of such data, including IoT devices that generate automatic, real-time actions or alerts. It is essential for fast-paced enterprises looking to extract instantaneous insights from ever-growing volumes of data. With real-time data streaming, one can access, analyse and act on real-time live data from IoT devices.

Simply put, streaming analytics deal with processing a continuous stream of data while it is generated. There are plenty of everyday use cases of real-time data streams, such as bank ATMs and customer service systems. These systems need to instantly process the data to function correctly.

But how does stream processing differ from batch processing? Batch Processing is a longer process where large amounts of data are processed and analysed in a single batch over a fixed period.

The Batch Process Differs from the Streaming Process in Myriad Ways

In batch processing, the volume of data is defined and finite. However, in the case of real-time processing, the data size is unknown and infinite.
The data latency in batch processing can range from hours to days, whereas the latency is only a few milliseconds in real-time processing.
Batch processing needs a standard computer and ample storage space for enormous data volume. On the other hand, stream processing requires sophisticated computer architecture and improved hardware.

The fundamental differences between batch process and streaming analytics raise an important question. Do your organisation stream analytics, and how can you optimise the real-time streams?

Why Do Your Enterprise Need It?

According to the well-acclaimed market researcher MarketsandMarkets, it is believed that the streaming analytics market is likely to grow from $12.5 billion in 2020 up to $38.6 billion by 2025 at a compound annual growth rate of 25.2%. But what is motivating such growth?

Companies need data streaming to attain smooth operations and prepare for future challenges. It allows organisations to take prompt decisions and actions. Furthermore, it improves the company’s competitive edge and significantly enhances customer experience. For instance, today, e-commerce apps for beauty and wellness products have built a feature where customers can virtually try on different products and make purchases only if the product suits them. Such features improve customer experience and help in customer retention.

Similarly, some clothing stores install smart mirrors using which potential customers can virtually try on different dresses or items or get a specific look without any hassle of physically trying them on. It provides a better customer experience.

Customer experience is only a small part of the answer to why streaming analytics is the need of the hour. Some other benefits of live data streaming include:

All of these points provide a glimpse of the immense capabilities of streaming analyses and why an organisation needs to invest in them sooner.

How to Optimise the Live Data Streaming Process?

If you want to incorporate streaming analytics and optimise real-time streams, then there are four practices you can consider to ensure data quality.

1. Choose particular business cases:

As your organisation is just starting, it is best to select narrow business cases and monitor them closely before organisation-wide adoption. Such business cases can include IoT data streaming that gives an insight into efficiencies and leads to customer satisfaction, cost-saving, and increased revenues. That is to say, IoT data can be used to track shipments, identify points of customer dissonance and recognize machines that need immediate attention to continue the smooth operation of the assembly line.

2. Identify the need for real-time data:

Organisations need to identify those areas that truly need real-time data streaming and those areas that can carry on with the batch process. While near-real-time data processing gives insight at regular short-period intervals, batch processing provides analysis overnight. It is crucial to recognize which processes require either of the data processing systems.

3. Make the design:

Companies must focus on simplifying the data streaming architecture to ensure data quality and minimise manual coding. Building a simple design increases the time between insight and data stream.

4. Cleaning the data:

If any organisation wishes to leverage data streaming, then having clean data is essential. The data cleaning process is done during data ingestion. Companies can contact their vendors to avail of data cleaning and meet their needs.

Aceso Analytics in Optimising Real-Time Data Streams

Optimising real-time data streaming is not a cakewalk until the experts walk in. If you are struggling with real-time data streaming and securing data flow, then Aceso Analytics is here for you. We are on a mission to empower every business with the power of AI. Spreading our services all over the globe, we are here to optimise and streamline your operations with expert industry knowledge. Get in touch with us today and embark on a tech-driven journey.