19 September 2023

Matt McLarty, CTO, Boomi

Moreover, the 'mobile-first' movement continues to sculpt consumer demands. There's an increasing expectation for real-time experiences across digital interactions and transactions, which has only amplified the competitive push among businesses to deliver instantaneous customer experiences. Yet, depending solely on real-time data comes with its own challenges, primarily around areas of accuracy and interpretation.

The dangers of flawed real-time data and analytics

Using flawed or outdated data to produce content like status updates or tailored promotions can lead to misdirected customer services. While real-time data flows usually enhance the speed and accessibility of an organisation's information, any inaccuracies that result in misguided services can erode customer trust and damage brand integrity.

Beyond data inaccuracies, there's also the danger of organisations utilising data without proper authorisation. When customers are met with offers created from details they haven't knowingly shared, they often wonder, "How did this company obtain this info about me?" This suspicion, or even resentment, is not a conducive starting point for cultivating a fruitful customer relationship.

Misinterpretation and the 'hallucination' effect

Another significant risk for organisations arises from decisions made based on partial information. The advantages of speed and accessibility offered by real-time data lose value when the complete context is missing. This can prompt organisations to make hasty decisions that fail to match the nuances of the situation at hand.

While misinterpretation of data can often be attributed to human mistakes, if the initial data is insufficient to begin with, businesses can find themselves in a position that leads to less-than-ideal results.

With growing dependence on AI, the challenges of incomplete data and human oversight are now accompanied by a distinctly contemporary problem. Generative AI, like ChatGPT-based chatbots, can sometimes ‘hallucinate’ when faced with insufficient data, leading them to fabricate details in an attempt to fill in the missing pieces. The danger this poses needs little elaboration.

Tools to optimise real-time data utilization

While real-time data flows have enhanced the speed and accessibility of an organisation's information, they have also prompted a shift from organised, structured data warehouses to chaotic data lakes.

To address this, data sources must be flawlessly merged with the applications that drive an organisation's primary operations and crucial customer interactions. While real-time data streams facilitate the continuous influx of information, supplementary tools like iPaaS, API management, data governance, and AI are critical for ensuring systems operate proficiently and effectively.

Consequently, emphasis has moved from simply gathering data to optimally harnessing existing resources, and many organisations have begun collecting substantial volumes of data for in-depth offline analysis. However, challenges remain in sifting through data, merging data compartments, ensuring data remains up-to-date and high calibre, gleaning accurate conclusions, and embedding insights into on-the-spot customer engagements and systematised business procedures.

As such, pairing data streams with governance tools is essential to maintain data integrity and comprehensiveness. Workflow tools are equally vital, offering the necessary filtering and context to yield accurate insights and reduce the chances of drawing incorrect conclusions. Meanwhile, integration tools are pivotal for real-time data analytics. They enable a smooth exchange of data across diverse systems and platforms and ensure data reaches its intended destinations, lessening the inherent risks tied to real-time data analytics reliance.

Is the infrastructure of organizations geared for real-time implementations?

Though the essential foundations exist, most organisational infrastructures still aren't fully equipped for real-time implementations. However, continued advancements will emerge from the intersection of two domains within enterprise IT: the user-centric application, which operates in real-time, and the analytics domain, which tends to be batch-processed to manage the sheer scale of enterprise data.

The merger of these two domains is propelled by big data technology, designed to manage vast data quantities swiftly and expansively. Coupled with burgeoning advancements in next-generation AI, rooted in analytics but whose potential is realised in applications, this synergy promises to advance the integration between these two domains further.

As the landscape of real-time data analytics progresses, organisations must identify and tackle the inherent risks. By adopting data governance, workflow solutions, and integration approaches, they can effectively tap into the benefits of real-time data while safeguarding against inaccuracies, information gaps, and diminishing customer confidence.