Founder & CEO at Datamam.
In the world of research, a significant shift has occurred. Traditional ways of analyzing data, which usually take a while, are being complemented by real-time data analytics. This new method allows analysts to see and use data as soon as it’s available, changing how they work and what they can discover.
However, the challenge shifts toward the efficient capture of real-time data as well as necessitating swift action and the capacity to make immediate decisions. With this shift, we must consider: How do we adapt our strategies to effectively gather instantaneous data, and how do we adjust our understanding of data analysis?
The Foundations Of Real-Time Analytics: Understanding The Basics
People have used data for calculations and record-keeping for thousands of years. It may sound unbelievable, but the earliest datasets are from Mesopotamia around 4,000 B.C. Some are economic records indicating transactions of people, livestock and grain.
However, the evolution of modern data analytics stretches back to only the 19th century, when Herman Hollerith chose the punched card as the basis for storing and processing information and built the first punched-card tabulating and sorting machines as well as the first keypunch. He then established the company that was to be called IBM.
As time went by, the development of computer hardware technologies greatly accelerated the data analytics capabilities, making them faster and more effective. All of these realizations gave birth to one: Data is most useful when it is fresh.
Real-time data analytics have revolutionized the approaches by leveraging data at a point where it holds the greatest value. As businesses come to realize this, they are increasingly turning to real-time analytics—which have proved to be a new governing factor within the business landscape.
Case Studies In Transformation: Success Stories Across Industries
One of the best success stories related to real-time data analytics is Delta Air Lines, which has distinguished itself in the competitive airline industry by significantly investing in and leveraging advanced data analytics to enhance its operations and customer service. Delta invested over $100 million in airport baggage systems to dramatically improve its baggage handling processes.
The integration of real-time flight data has enabled the airline to promptly route connecting baggage and provide passengers with real-time tracking information, resulting in a 71% reduction in mishandled baggage between 2007 and 2014.
Another company, Netflix, used its subscriber data and real-time data analytics to transform into a global streaming company, as its revenues rose from $3.2 billion in 2011 to $33.7 billion by 2023.
Now just imagine Netflix stepping up its game by mixing in information from other similar companies with its own. It wouldn’t just understand what its users like, but it would get a peek into what people around the world are into.
What do you call this type of information, and what are the technologies that would give it access to this type of information?
Internal And External Sources: Tools Powering Instantaneous Insights
Data originating from various sources is categorized into internal or external based on their origin relative to an organization or individual. Internal data is generated within an entity. This category includes data that is generated and maintained within an organization. External data, on the other hand, is generated outside of the organization and offers a broader context and identification of new opportunities or challenges.
Nowadays, the analysis of internal data is a necessary first step for any organization that is attempting to optimize operations. However, it is essential to leverage external data to level the playing field. How can businesses get the external data they need for real-time data analytics?
One of the best and increasingly popular ways of collecting real-time external data is web scraping, which is described as an automated technique to retrieve public information from websites and bring it to a form that is structured and easily accessible.
How To Start With Data Scraping
• Identify your data requirements. Identify the important data that drives your decisions. Be certain of your requirements to give more guidance to the scraping work.
• Tools selection. Select the tools that can be adapted to your technical resources and data needs. Make sure the selected tools can be well-integrated with the existing systems.
• Develop ethical guidelines. Develop the protocols for the responsible scrapping of data within law and site terms in a manner that circumvents any ethical concern or potential legal issues.
• Monitor and maintain. Check the validity and integrity of the data regularly. Periodic alterations are made in such a way that the changing needs for data are met and efficiency in operations is optimized.
• Analyze to inform decisions. Use the analytic tools to allow the findings to drive real-time, practical business action.
The Future Of Decision-Making
Looking ahead, the future of decision-making is getting a major upgrade. The process of web scraping for external real-time data is still pretty new, so not many companies are doing it. However, those who do should see results and advantages. The blend of current and predictive data can enable decision-makers to see the immediate effects of their decisions and anticipate future outcomes.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
