In today’s fast-paced digital world, companies need to stay on the front foot to remain competitive. With more data than ever being generated and collected, it’s vital to have a highly functional system to keep on top of data analysis in real-time.
Without this in place, companies may struggle to make good use of the data they collect, leaving them vulnerable to missed opportunities, poor customer service, and a lack of ability to plan for the future.
What is Real-Time Data?
Real-time data is data that is continuously generated, and can be analyzed as it is received to provide up-to-date information for businesses. Real-time analytics provide data insights straight away, which is especially useful for industries where speed is important and where quick decisions can be the difference between success or failure, profit or loss.
Real-time analytics is also called ‘operational intelligence’, demonstrating the importance of keeping on top of your data to influence how your organization is run and operates.
Why Do I Need Real-Time Data Analysis?
89% of recent survey respondents said a real-time data management platform is valuable to their organization, and 91% say real-time data analysis can have a positive impact on their company’s bottom line. Businesses, then, should optimize their digital architecture for real-time data analytics to really capitalize on the opportunities it presents.
The main benefit of real-time analytics is that it speeds up the whole process, moving away from batch methods of analysis that only happen once data has been gathered and stored. The response time of real-time analytics varies from a few seconds to a few minutes. It can also help your business in other ways.
Real-time data analysis can:
Help with fraud prevention.
Offer insights into risk and change.
Real-time customer data analytics can provide more information about customers’ preferences, behaviors and future purchasing plans. Rule-based or bespoke real-time data analysis can compare patterns from current and historic data, allowing predictions to be made about future customer behaviors.
How do I Optimize my Real-Time Analytics Architecture?
Data architecture describes how data is managed, including how it is collected, stored, analyzed and visualized. As more data is generated and collected, the architecture needed to process it can become ever-more complicated. To ensure you’re getting the most from your data, it’s crucial to make sure that your systems and processes are supportive, speedy and strong.
From serverless data platforms which speed up processes, allowing you to respond to customer demand faster, to automated analytics solutions to crunch the numbers more quickly and accurately than a human, data architecture is expanding to keep businesses competitive. New innovations in AI and machine learning allow companies to take data insights and implement changes faster than ever before, responding to customer preferences, behaviors and needs.
Implementing a real-time analytics architecture and expecting it to stay current and useful doesn’t fit with the fast-paced world of business. Agile, flexible analytics are necessary to remain on top of changing priorities, behaviors or business needs. It’s therefore crucial to maintain an organizational culture where employees are keen to try new data architecture and to stay up-to-speed with new tools. A company-wide data strategy, supported by the C-suite, is key.
Research from McKinsey suggests that optimizing data analytics architecture can produce a huge ROI, with an analytics case study from one US bank reporting ROI of more than $500 million annually, and an oil and gas company reporting a 12 to 15 per cent growth in profit margins. With results like this, it’s clear that businesses who optimize their digital architecture to unlock real-time data analytics capabilities are the ones that will get ahead.
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