As you read this blog, the reality is that the data your organization is collecting is already losing its innate value. The reason for that is because data has the most value for offering business insights and opportunities the second it arrives. The longer data sits in storage, the less useful it becomes. To put it another way, data gets stale, and an organization misses out on opportunities when they are unable to act on the data they receive immediately. A company is only as fast as its slowest data in today’s market. Real-time data is where it’s at.
There are two types of data. One is historical data processing, commonly referred to as batch processing, and fast data or real-time data. Batches let you know what happened at some point in the past. It could be a week, a month, a quarter, or a year. This type of data provides you with historical context. Batch-only has been the predominant approach in businesses for many years. Still, companies are quickly recognizing the limitations around batch processing and the benefits of real-time data to remain competitive. This is especially important as we move further and further toward edge computing and fog computing.
Real-time data, on the other hand, is all about now. By processing data in motion as it streams into the business, real-time data delivers immediate insights and creates business value through analytics. The data can come from anywhere, including customers, IoT sensors, security infrastructure, IT systems, logistics, manufacturing lines, and field operations. The ability to harness and control that real-time data will directly impact business success. Most organizations recognize that the faster one acts, the greater the value you can extract from the data.
Acting quickly and decisively on data is how successful organizations will differentiate themselves now and in the future. In the financial services industry, everything happens in real-time. It is the ability to gather and act on data rapidly that is a prerequisite to establishing a competitive advantage.
Live interaction with customers improves conversion and satisfaction rates. Automated systems can now handle a bulk of customer interactions rapidly and even adjust recommendations based on data analytics. This has had a positive effect on overall customer satisfaction. Immediacy is something that most consumers want.
Even so, a majority of companies are just starting to harness the power of real-time data. It used to be that companies had to spend a lot of money building infrastructure, but today investments are being made on data processing because smaller investments are yielding exponentially greater returns in terms of competitive advantages. In the future, fast data processing will be a prerequisite for some of technology’s most exciting advances.
Most executives understand that implementing machine learning and artificial intelligence to automation, predictive analytics, security, and other use cases to be transformative for their industry. To that end, research and development labs around the world are working to develop and advance underlying data models. The problem continues to be the ability to extract value once the models are placed into production.
Let’s look at how this works in automation. Automation depends on the right data input into the right model at precisely the right time and the ability to “learn” or adjust the model as new data becomes available. The difference between the old robotic assembly line and modern automated systems is the ability to make rapid adjustments based on data analytics. Fast Data is not just data in real-time but about the ability to collect, join and transform data in motion. This is why fast data is fundamental to machine learning and artificial intelligence.
Companies that embrace fast data and the ability to act on data and understand its power will outperform their competition as we move forward into the future.