Is Real-Time Analytics for Businesses a Boon or a Ban?

In the business world, real-time analytics is having a moment, as it is a vital operational tool for organizations across industries to make better and faster decisions. Unlike traditional data analytics, real-time analytics focuses more on informing future decision-making and enabling a new way of operating businesses by letting the data team take immediate action at the moment. Today, the AI TechPark newsletter will guide you through the highlights and challenges of implementing real-time analytics in B2B business and also provide you with a solution to overcome the demerits of this technology.

We are quite acquainted with the fact that analytics is one of the most critical developments in the IT field and has the potential to automate any business operations in a few minutes. By automating business operations, organizations can get faster backtracking times on key judgments, enhance efficiency, and save extra costs on hiring. Real-time analytics has also proven itself to be an accurate and reliable technology when compared with human-run analysis. 

To support organizations in implementing real-time analytics, numerous tech giants have invented serverless platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Companies, which will manage different data tools without having to provision or operate physical hardware, offering scalability on demand and eventually aiding in increasing the processing power when needed.

However, with every technological advancement comes tons of challenges. 

A recent article on AI‐TechPark by Matt McLarty highlights that the sole reliance on real-time data presents challenges that predominantly revolve around matters of interpretation and accuracy. Numerous businesses risk misdirecting or misleading their customers when they inadvertently utilize insufficient or old data to create content. Despite the capacity to boost the speed and accessibility of enterprise data, real-time data analytics overlooks the true essence, which ultimately delivers inappropriate services that can undermine customer relationships. Apart from that, organizations often invite risk by using data without proper authorization, leading customers to doubt how a company knows so much about them when they are presented with content that’s been put together using personal details they didn’t knowingly share. 

We are well aware of the speed and accessibility that real-time data provides to the data team, which helps in accelerating business operations, but when the data is inconsistent and doubtful about the source, it often leads to hasty and incongruent decision-making. However, with more dependence on technology, the risk of flawed data and human oversight is aggravated by the problem of false information. 

But we must remember that every problem has a solution. 

Therefore, before implementing real-time analytics in your organization, don’t forget to adopt data governance, workflow solutions, and integration approaches. By keeping these three solutions in mind, you can easily preserve genuine data, create a streamlined workflow that will filter out low-quality data, and develop an accurate insight that will help you make the correct decisions. 

In the end, data is the new oil in the business landscape and requires the utmost protection and governance, which can only be possible when businesses start to make radical decisions when using data.

Let us know your thoughts in the comments section below, or contact Techtopia for deeper insight into the most recent AI advancements and how they affect the IT industry.