Is Corporate Infrastructure Equipped for Real-Time Implementations? Find your answers by exploring this article.
Access to real-time data and insights has become critical to decision-making processes and for delivering customised user experiences. Industry newcomers typically go to market as ‘real-time’ natives, while more established organisations are mostly at some point on the journey toward full and immediate data capability. Adding extra horsepower to this evolution is the growth of ‘mobile-first’ implementations, whose influence over consumer expectations remains formidable.
Nonetheless, sole reliance on real-time data presents challenges, challenges that predominantly circle matters of interpretation and accuracy.
In this article, we explore why inaccurate real-time data and analytics transpire, explain the commonplace misinterpretation of both, and look at some of the tools that help businesses progress toward true real-time data competency.
The Risks of Using Imperfect, Legacy, and Unauthorised Real-Time Data and Analytics
Businesses risk misdirecting or misleading their customers when they inadvertently utilise imperfect or legacy data to create content. Despite real-time capability typically boosting the speed and accessibility of enterprise data, mistakes that deliver inappropriate services can undermine customer relationships.
Elsewhere, organisations invite substantial risk by using data without proper authorisation. Customers will often question how a company knows so much about them when they are presented with content that’s obviously been put together using personal details they didn’t knowingly share. When such questions turn to suspicion, the likelihood of nurturing positive customer relationships shrinks.
Misinterpreting Data and the AI ‘Hallucination’ Effect
Real-time data’s speed and accessibility are also impeded when full contexts are absent and can lead to organisations making hasty and incongruent decisions. Moreover, if the data is deficient from the start, misinterpretation of it becomes rife.
Today, the risks of flawed data and human oversight are exacerbated by a novel problem. Generative AI technology is known to ‘hallucinate’ when fed with incomplete datasets. At significant risk to the organisation, these large language models fill any gaps by inventing information.
The Tools for Optimising Real-Time Data
Though few question real-time data’s ability to increase the accessibility and speed of enterprise data, many have observed that it has promoted a transference from organised data warehouses to muddled data lakes.
Avoiding this transference requires a seamless combining of data sources with those applications that drive core operations and protect customer interactions. Certain auxiliary tools, such as iPaaS, API Management, Data Governance, and AI, are also essential in ensuring real-time data properly facilitates the constant influx of information.
Predictably, a trend is thus developing from a move away from simple data gathering to optimally harnessing existing resources. Yet, challenges remain. Analysing data, merging data silos, ensuring data is new and rich in quality, and embedding insights into live customer engagements and systematised business procedures remain significant hurdles.
However, even these hurdles can be overcome. By coupling data streams and governance tools to preserve data scope and integrity and by deploying workflow tools that offer filtering and context, accurate insights can be generated while the incidence of incorrect conclusions is slashed. Where real-time data analytics are relied upon, integration tools cut risk further by enabling efficient data exchanges across separate systems and ensuring data reaches its expected destinations.
Is Corporate Infrastructure Equipped for Real-Time Implementations?
The bedrock is there, but most corporate infrastructures are not yet equipped for real-time implementations. However, a path is being cleared by emerging advancements from the fusion 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 coming together of these two domains is powered by big data technology, which manages extensive data volumes at speed and scale. Reinforced by exponential advancements in AI that are entrenched in analytics but come to life within applications, the integration between these two domains appears set to tighten.
Real-time data and analytics are evolving at great velocity. To keep up, organisations must identify and confront the inherent risks. By adopting data governance, workflow solutions, and integration approaches, the advantages of real-time data can be successfully accessed, and any inaccuracies, information breaks, and risks to customer confidence can be mitigated.
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