As we move deeper into a digital age, access to real-time data and insights has become critical to the decision-making process and for delivering customised user experiences.

Indeed, most established organisations are at some stage toward harnessing immediate data capabilities, while newer startups typically start life as ‘real-time’ natives.

Adding to the momentum is the ‘mobile-first’ crusade that continues to influence consumer expectations. The demand for real-time experiences across digital interactions has only intensified the corporate race to meet it.

Nonetheless, sole reliance on real-time data is not without challenges, and most of these orbit the spheres of interpretation and accuracy.

Let’s take a deep dive into the reasons inaccurate real-time data and analytics occur, explain the complexities around the interpretation of both, and cover some of the available tools that can help businesses move to a place of true real-time capability.

The risks of using defective or obsolete real-time data

By using defective or obsolete data to create content, businesses risk misdirecting their customers.

Though real-time capability typically augments the speed and accessibility of enterprise data, inaccuracies that produce invalid services can dent customer trust and brand reputation.

Aside from inaccurate data, organisations that use data without proper authorisation also invite significant risks. When a customer is presented with an offer or promotion that’s been put together using details they didn’t knowingly share, they will often speculate as to how the company came to possess this information. Speculation can soon turn to suspicion or even resentment, neither of which aids the cultivation of positive customer relationships.

To further mitigate the risks of relying on real-time data and enhance data quality, understanding what is vector search and its application in improving search accuracy and relevance could offer significant benefits.

Misinterpretation of data and AI ‘hallucination’

Adding to an environment rife with risk are decisions formed on the back of incomplete data. Real- time data’s speed and accessibility are stunted when the full context is absent and can trigger organisations to make rushed decisions that do not correspond with given situations. And, if data is inadequate from the start, misinterpretation of it becomes both easy and commonplace.

Today, the inherent hazards of incomplete data and human oversight are complemented by a most contemporary challenge. Generative AI, like ChatGPT-based chatbots, have been known to ‘hallucinate’ when supplied with insufficient data and will literally invent information to fill gaps.

Little elaboration is needed to convey the danger this establishes.

AI helps us to create human product experiences

Optimising real-time data with the right tools

Real-time data flows have undoubtedly increased the speed and accessibility of enterprise data. However, they have also provoked a shift from organised, structured data warehouses to disordered data lakes.

Preventing this shift from occurring requires data sources to be seamlessly combined with the applications responsible for driving primary operations and protecting customer interactions. Real-time data will facilitate the constant influx of information, but supplementary tools, such as iPaaS, API Management, Data Governance, and AI, also play an essential role in ensuring systems operate effectively.

As such, emphasis is moving from simple data gathering to optimally harnessing existing resources, and many organisations are now collecting large volumes of data for in-depth offline analysis. Yet, challenges remain. Scrutinising data, merging data compartments, ensuring data remains current and high quality, assembling accurate conclusions, and embedding insights into live customer engagements and systematised business procedures remain significant hurdles.

Nevertheless, they are hurdles that can be overcome by pairing data streams with governance tools that preserve data integrity and breadth. Elsewhere, workflow tools that offer the necessary filtering and context to generate accurate insights and cut the incidence of incorrect conclusions are similarly vital. Where there is a reliance on real-time data analytics, integration tools lessen risk by enabling smooth exchanges of data across different systems and platforms and ensuring data reaches its intended destinations.

Is enterprise infrastructure ready for real-time implementations?

Although the basis is there, most corporate infrastructures are not properly equipped for real-time implementations. However, optimism abounds following emerging advancements from the intersection of two domains within enterprise IT: the user-centric application, which operates in real-time, and the analytics domain, which is largely batch-processed.

The intersection of these two domains is powered by big data technology that’s designed to manage vast data volumes at both speed and scale. Add the exponential advancements in AI that are rooted in analytics but show the greatest potential within applications, and the integration between these two domains looks set to deepen.

The real-time data and analytics landscape is evolving at pace, and to keep up, organisations must identify and confront the inherent risks. By adopting data governance, workflow solutions and integration approaches, the benefits of real-time data can be effectively accessed, and any inaccuracies, information gaps, and declines in customer confidence can be kept at bay.

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