Almost every organization is embarking on some sort of digital transformation initiative, reimagining relationships between customers and leveraging new technology that supports them. At the heart of any digitally transformed operation is data – data that comes from operations, customers and suppliers, to name a few.
What makes the digital transformation so impactful is that it enables companies to transform the way they leverage data and ultimately do business with their partners. In doing so, they use technology to integrate myriad data sources in support of processes and decision-making.
Ergo, to map a successful digital transformation journey, data integration is a prerequisite.
Many companies, still, rely on legacy data integration solutions like point-to-point to create data connections and exchange data across partner ecosystems. However, using point-to-point integration can take your digital transformation initiatives down the rubble. In this blog post, you’ll find out why point-to-point integration acts as a roadblock and how reimagining your integration approach can enable you to achieve desired business outcomes.
Why Does Point-To-Point Integration Fall Short?
Gartner defines point-to-point as “…a tightly coupled integration between two or more endpoints.” In point-to-point integration, IT teams have to write long hours of custom code to create data connections and integrate new customers. This takes a lot of time, sometimes even weeks or months, and ultimately inhibits companies to drive their digital transformation initiatives.
Here are some reasons why the point-to-point integration approach isn’t producing the desired results:
It’s time-consuming to implement: IT integrators relying on point-to-point can take weeks or months of calendar time to create data connections. In the process, they fail to focus on more high-value tasks, which hurts innovation and growth.
Customers, all this while, experience a lot of delays that fills them with a deep sense of frustration and agony. This acts as a roadblock for a company that wants to delight its customers and grow revenue.
It’s not scalable: As organizations grow, companies find it difficult to integrate their ever-increasing data streams with speed and ease. It can increase the risk of data silos, which ultimately hinders insights delivery and decision-making.
It isn’t future-proof: As organizations grow, they have to handle data that are more complex than ever. With the point-to-point integration method, businesses fail to consolidate all this data and then use it to make decisions. Truth is, point-to-point integrations simply aren’t designed to keep up with the changes, making them less than ideal over time.
Companies that can reimagine their approach to data integration will become “digital transformers.” On the other hand, organizations that rely on legacy solutions will fall short.
Modernize Your Approach to Drive Digital Transformation
New-age data integration solutions offer self-serve capabilities that enable even non-technical business users to create data connections in minutes and, consequently, drive their digital transformation initiatives. Companies can use these solutions to onboard complex, voluminous, bi-directional data gathered from their partners by pointing and clicking through easy screens and dashboards.
At the same time, IT is freed to focus on governance and other innovation-driven tasks. That not only improves companies’ ease of doing business but also revenue generation capacity.
Further, AI and ML technologies can enable non-technical business users to understand and define data semantics, and then make data connections following those data rules. Further, a single click is required to gather, break down, and manage multi-dimensional data and stream it in real-time to execute modern-day business transactions.
In short, companies must modernize their data integration approach to enable all business users to create data connections and integrate customers in order to deliver the promised value and ride the digital transformation wave.