Real-time big data analytics is a mouthful, but it represents capabilities that are redefining an organization’s ability to make quick, proactive, and insightful business decisions. This emerging technology is used to analyze large and complex sets of data as it comes into a storage system and has the potential to drive a huge business impact. To better understand why it’s such a game-changer, we need to first unpack its complicated phraseology.
Real-time analytics refers to the process of analyzing data as soon as it becomes available in a system. Real-time analytics systems apply logic and math to provide faster insights on that data, leading to a streamlined and more informed decision-making process.
While this is a seemingly simple definition, there is some ambiguity. First, “real-time” can mean different things to different people. Depending on the use case and application, this could have some inconsistencies, but it ranges anywhere from a fraction of a second up to a few minutes. Despite these nuances, the important takeaway is that the analytical findings are shared quickly enough to influence a decision. Compare this with historical reporting, which may have highly granular data—and even important insights—but the data comes too late to act upon it. In contrast, real-time analytics grant visibility quickly enough to respond to issues, minimizing their negative impact, and in some cases, avoid them entirely.
When it comes to using this type of analytics, consumers can interact with the process findings in multiple ways. On-demand real-time analytics refers to when a user must request the findings. In contrast, continuous real-time analytics actively alerts users of the findings or will trigger a response as it comes across certain findings.
Big data is by no means a new concept and has been developing throughout the digital age, so to fully understand this concept, let's first take a step back.
Since the start of the digital age, organizations have been collecting data and attempting to analyze it to guide the decision-making process. As the digital environment developed, the widespread use of digital systems led to massive amounts of data that continues to grow exponentially.
As data sets became increasingly large and complex, they eventually overtook traditional methods of processing and analyzing data. These computational challenges spurred the evolution of analytics that use high levels of computer processing power to extract accurate insights from huge variable data sets.
This process, termed big data analytics, meant that organizations could analyze datasets that were previously inaccessible due to their sheer size. Analyzing increasingly large datasets has unlocked new, crucial insights into a business’s processes, customers, market, and more.
Big data analytics is a broad term that can be broken down into a few different sub types. Each has value and is uniquely suited for different scenarios.
Applying real-time data analytics to big data has transformed previously retrospective processes, enabling into immediate action. An enterprise can now access (and act upon) valuable insights as soon as information enters the big data infrastructure.
Organizations can now understand why something is happening as the system processes live information, and accurately predict the outcome of events, equipped with immediate prescriptive advice—even when the magnitude of data is immense. The result: enterprises can gain actionable insights faster which in turn leads to a shorter and more accurate decision-making process.
Organizations may be hesitant to implement real-time big data analytics due to the perceived initial lift and cost; however, in the long run, there are many benefits that outweigh these due to their impact on business efficiency and profitability. Real-time big data analytics is also often easier to implement than many companies may assume.
Introducing real-time data analytics to big data means significantly less time between the time information is processed to when it can help an organization make changes or critical decisions.
By combining real-time insights on the market, target audience, and competitor actions, organizations can remain innovative and gain a competitive advantage.
More insight into customer sentiment allows organizations to factor customer feedback in the decisions and evolution of their services and product. Since this data is constantly changing, it’s important to act on it while it is still relevant.
Real-time big data analytics allow organizations to recognize trends in business processes and gain specific insights into how to drive greater efficiencies across the organization.
The ability to detect patterns in real time means that organizations can proactively identify and minimize risks—instead of identifying vulnerabilities after the damage is already done. This is especially applicable in identifying potential customer churn, manufacturing operational inefficiencies, and financial vulnerabilities.
When looking long term, there is the potential to significantly cut costs and improve profit margins by adding real-time big data analytics to a tech stack. By creating new business process efficiencies, organizations can free up resources and drive cost savings.
The addition of real-time analytics in big data redefines an organization’s ability to derive thoughtful insights in time for them to impact business outcomes. Without real time analytics the difficulties in transferring and leveraging mass volumes of data often leads to an unnecessary lag in the decision-making process. The benefits of this technology pair are endless and many of them are rooted in their ability to unlock analytical and operational efficiencies in different areas of an organization.
Learn how a leading U.S. E&P Company is unlocking analytical & operational efficiencies.