The phrase ‘Big Data’ is more than just a colorful moniker; it’s an understatement. This is because the amount of data available for analysis is growing exponentially. In fact, it is estimated that an average of 2.5 Quintillion bytes of data are created daily. Along with this massive influx of information, come a variety of opportunities for businesses. From improving customer satisfaction, to refining marketing efforts, to reviewing business processes and cyber-security measures, real-time analytics have become an essential factor in business success. However, recognizing the importance of big data, and knowing how to capture and analyze it aren’t necessarily the same thing. They key is to understand data mining, in order to turn those 2.5 quintillion bytes of daily data into something useable.; Data mining is especially important to consider when it comes to real-time analytics. Simply put, data mining is the process by which large amounts of information are collected and analyzed, in order to produce valuable conclusions. For real-time analytics to be effective, it requires the extensive information base provided by competent data mining. Real-time analytic continuously accesses this information, refines it, and uses it to produce completely relevant and current data. This can have a significantly positive impact on business success—in fact, top employers are 3.9 times more likely to have over 80% of their company’s data available for users to make decisions in real time or near real-time.
Providing an in-depth look at how data is being extracted, and how that information is developing over time, real-time analytics ensures that valuable information is easier to review and handle. Real-time analytics gives organizations a definite advantage, by allowing them to better tailor their services to fit the changing needs of customers. Here is a quick guide to help your organization get the most out of real-time analytics.
The alternative to real-time analytics is batch processing. Batch processing data entails the collection of data over a predetermined time frame. The data is then processed at regular intervals—such a weekly or monthly—resulting in ‘batches’ of analytics data. But while this is sufficient for certain tasks (such as payroll and billing), it simply does not provide the same level of timely information that is available from a constant flow of data. Real-time data analytics is able to gather, process, and output data as a continuous flow providing users with only the most-current information. This technology has long been used for systems that need to be kept current at all times (such as radar systems, electronic vehicle controls, and ATMs), but is now being adapted for use in business.
In essence, batch processing takes in data over time, while real-time analysis takes that data and uses it as it comes in. Real-time programs also rely on programs that can handle input, output, and processing all at once, while batch processing relies on separate programs for each of these functions.
Despite the complex processes at work within real-time data analytics, adapting a business to take advantage of this technology is not a difficult endeavor. Many software as a service (SaaS) businesses offer complete real-time analytics solutions that can easily be customized to fit the needs of almost any organization.
That having been said, real-time data may be easier to follow if you have a continuous flow of customers, not limited by peak purchasing hours ( as is often the case when it comes to ecommerce or international business). Still, the benefits of having real-time data analytics are not limited only to those who are actively dealing with clients at all hours of the day. To ensure that you get the most out of your real-time data analytics solution, there are some issues that need to be considered. Real-time analytics has a tendency to provide more data than you might be able to digest. When approaching your data, identify information you want to use at a given time, as this will help keep your data arranged and organized in a way that is accessible. However, the advantages far outweigh the complications, and seeing as how top-performing business leaders value timeliness of their analytics tools, with 59% strongly agreeing that they’re able to derive timely insights, there’s no denying the benefits that come from working with the most up-to-date information available.
Nonetheless, don’t feel as though you need to reorganize your entire existing systems to take advantage of real-time data analysis. Forcing your business to fit into existing data analysis processes can result in backlash within your organization, resulting in a failure to fully commit to the solution. Instead, choose a real-time data analysis solution that can be customized to fit your existing processes.
One of the most-important things to remember is that while real-time data analysis programs are capable of processing and reporting on significant amounts or raw information, in the end, it will be up to you to identify the cause-and-effect relationships behind the numbers. As new campaigns and others factors are introduced, look for possible correlations in your data. And remember that unused data is useless data; if you are simply collecting information in data silos without ever putting that data ‘under the microscope,’ then you might as well just not bother.
Given that real-time data analysis’ most attractive feature is it’s ability to provide up-to-the-minute reporting, be sure to take advantage of it by putting that data to work immediately. Watching your organization’s analytics in real time is like being able to see all of the various cogs and sprockets within a clock working together, and can give you a much more in-depth understanding of what makes your business tick. Doing so will also help you identify and address potential problems before they can get out of control. After all, real-time data is only superior to batch processing when data is used as it arrives.
Real-time data analytics provides very real advantages over batch processing, but it is not a fire-and-forget solution. In order to get the most out of real-time data analysis, organizations need to be committed to doing their part, to help ensure that the information they are capturing is being put to effective and immediate use. And when they do, then business growth is just the natural result.