The transition

In recent years, there has been an explosion of interest in big data and data-intensive computing. Along with this, there has been a corresponding increase in the use of real-time data processing systems. Real-time data processing systems are those that process data as it is generated, rather than waiting for all of the data to be collected before processing it. This article discusses the opportunities and challenges associated with real-time data processing. Before moving to the real- data, let’s look at some facts:

A few fun facts about data

2.5 quintillion bytes of data are being created every day and less than 0.5 % is used

Cloud is no more able to handle the pressure created by big data and old data storage

Data seems to have a shelf life

Bad data can cost businesses more than $3.5 trillion per year

Structured data helps in better decision-making in businesses

The time to download all data takes about 181 million years

Now that the fun facts were surprising to most of you, let’s look at the actual trends, case studies, and challenges further.

How can organizations choose and adapt to the dynamically changing Data culture?

As per the statistics, in the future, more than 50% of the data is collected or created, analyzed, and stored outside the cloud system. An organization can always start by analyzing its needs and planning the architecture that generates what they are looking for in the future. The increased usage of real-time data is being adopted by an array of industries including but not limited to Banking and finance, Retail, Healthcare, and more industries such as advertising and marketing are poised to adopt this year.

Enterprise data management involves the processing of data that involves various activities associated with processing, checking quality, accuracy, security, etc. The data says Enterprises are self-inhibited due to a lack of data availability as and when required in the form that is required to access and understand easily. Not only this has affected their capabilities but also paralyzes their agility and operational abilities. 

Benefits for an organization:

The real-time benefits are real quick. A few of them are listed below.

  • Increased operational efficiency
  • Quicker automated intelligent decision making
  • An enterprise that can project accurate data metrics
  • Helps in every aspect of the enterprise including their products, sales, strategizing, finance, etc.

The future

There has been a sharp decline in the retail market in the spending of the consumers according to statistics. How is Real-Time data impacting these industries in changing their habits and bringing them back to the usual patterns of shopping? Most retailers are now working on combining real-time data with #ai to give real-time information to the consumer and help in changing the buyer’s mindset in rushing to buy the product. 

When you see this, what do you think it is

‘Only 1 left in stock

It means data and AI are working shoulder to shoulder, which is beyond amazing. This had been the real innovation that created an urgency in a consumer’s mind to get that last item in the stock. 

Not only retail, but another example is also the healthcare sector. A classic example is healthcare devices or devices that monitor your health/heart rate. Another massive sector that uses Real-time data is the Financial sector.

Now, having said all the above, although real-time data is very useful and works like a magic wand, there are certain limitations and challenges when the ‘processing time’

A few but Real Challenges

Although there are a few challenges in Real-time data projects, there are strategic and effective solutions that can make the entire real-time data processing process smooth. A few challenges and solutions are listed below in this article

  1. Quality

The data quality defines the output of the reports for example in the case of financial projection and business analytics. Not all architectures designed and developed can provide the best quality when it comes to real-time data. An organization needs to be extremely careful while collecting, filtering, and strategizing data.

  1. Collection disruptions- data formats

When organizations use IoT- Internet of things with their own data formats, it becomes very confusing to these devices, especially with data coming from different sources and multiple formats. This leads to data disruptions due to interactions caused by firmware updates or APIs

A quick solution to this can be addressed using batch data processing before the pipelines are created for real-time data.

  1. Bad architecture

The important part is designing the architecture. If the architecture designed does not give the right results or does not fulfill the requirements of the organization, it is useless and any business can get into losses when the data is not accurate.

Using a hybrid system with a mix of OLTP- online collection and storing data and OAP- online analytical processing for batch data processing using carefully designed strategic data pipelines helps with building a good architecture and data loss. So everything links back to architecture.

How can we fix this or start with Real-Time data? You can either hire a bunch of data scientists to perform these tasks and build the entire department for change


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