The explosion of data and unprecedented uptake of technologies and apps have transformed the world of business. The diffusion of new data-driven technological solutions has granted the transient landscape a whole new dimension that was only vaguely present at an early age. On leveraging them proactively, business users or CIOs have harnessed the true potential of data, comprehended their customers’ demographics, and delivered value. Ergo, on many counts, these developments have spurred an epochal transformation that has driven business pursuits, accelerating more value and increasing companies’ ease of doing business.
Even though the internet age has fuelled growth, it has introduced many challenges too. It has amplified and reinforced the rise of connection points and devices online, which isn’t even limited to individuals. With this, the amount of data being exchanged across business boundaries such as customers and suppliers has started proliferating. The common implication of this sudden outburst is complexity.
Let us see a scenario to understand things better. A manufacturing firm is generating tons of data, which it can share with partner businesses for daily transactions. Secure-internet transfer mechanism can support this sharing. Due to the rise in apps and other technologies, even customers’ expectations increase, fueling their interactions and exchange with the firm. Now, in that case, the firm has to deal with a lot of data which is not only difficult but costly. If the company fails to analyze the datasets in unison, it can inhibit their expansion.
The point being, with a multitude of connecting points and virtually everyone getting online and creating a data trail behind them, the data complexity has gone through the roof. The problem amplified further with increasing customer expectations and complex customer data getting complex with more information attributes being exchanged and tracked.
As big data revolution fuelled, the challenges attached especially to the 4Vs - Volume, Velocity, Variety, and Veracity have taken a toll on the majority of companies. This inability of companies to handle these aspects has inhibited growth. And companies that rely on old - age technologies are affected by these hurdles the most. Data has turned more complex with the respect to its semantics. As a result, important tasks such as data mapping has experienced friction.
For quite long, organizations have not been able to harness the true potential of data without first normalizing and preparing it.
Companies who still rely on limited data integration technologies such as EDI or API are not able to handle the challenges posed by the digital age. They fail to handle such high-volume data that have been growing exponentially, had multiple versions, semantics, variety, and veracity. Ultimately, they are not able to meet the customers’ expectations and drive business forward.
Related White Paper: Reimagine your Customer Data Integration
In such situations, self-service-powered customer data integration technologies shine. In fact, it has turned out to be so effective that organizations cannot only use it to handle large volumes of data but also extract insightful information faster for maximum value generation.
With next-gen features such as AI mapping, pre-built application connectors, shared templates, user-friendly dashboards, these solutions enable every business user in the company to onboard, ingest, transform, and integrate big data with speed and precision. In short, the complexities introduced by the rapid advances in the internet and related technologies can be handled by business users without difficulty.