From On-Demand to Real-Time: The Data-Processing Punch behind Payments
Real-time has become the de facto experience in business. An online-only service must act in the moment or risk losing the customer, who expects access to services around the clock
The urgency to do so accelerated during the pandemic, as the underlying shift to online business saw organizations invest billions of dollars in IT projects to become “digital first.”
Post-pandemic, analyst IDC expects “aggressive” levels of investment in digital projects as organizations ramp up their strategies. But the operational tides have changed.
Simply being online is no longer enough to succeed. why? For a start there’s greater competition: 89 percent of organizations are pursuing a digital-first strategy, according to IDC.
Next, businesses have more innovative tools for capturing sales dollars. These include URL- and QR-code-based payment options that can be embedded in social networks, digital wallets from Apple and Google and infrastructure projects such as the European Payments Initiative, and Buy-Now-Pay-Later credit options (BNPL.)
Finally, shoppers are a lot less loyal than they were in the pre-pandemic world, according to a consumer report from McKinsey.
As the consultant says in its separate Global Payments Report here, “sticking to them [customers] is no longer sufficient” with the consequence that businesses must begin to develop what it calls robust “commerce facilitation” rather than a “discrete payment experience.”
“Initial real-time payment growth has been primarily in peer-to-peer settings and online transactions,” McKinsey notes. “The next tests will be the consumer-to-business point-of-sale and billing spaces… and their more straightforward paths to monetization.”
A fresh wave of real-time commerce is on the horizon – riding it successfully will require smarter and more responsive engagement with customers.
Banking in the moment
Of course we get that and yes, some organizations are already moving in this direction. BNP Paribas has, for example, built applications that are capable of making bespoke loan offers to customers at its ATMs – leading to a huge jump in the number of customer conversions.
But you can only achieve this level of real-time engagement if you have a comprehensive and always current understanding of the customer on which you can act. Building this 360-degree view means harnessing two sets of customer data: their clicks and other streaming data generated or harvested in real time, and their history. All of this information must be blended and analyzed using analytics tools at sub-millisecond speeds to deliver the actionable, context-based insight that allows businesses such as BNP to engage with customers in the moment and make offers that close transactions.
The good news – seven in ten organizations believe that, armed with critical customer information, they can make special offers and close deals at the time of engagement. the bath? Four out of five struggle to unify real-time and historical data to engage with prospects, losing revenue opportunities as a result.
why is this?
A major issue is the decentralized and dispersed nature of data. Cloud, social media and IoT means data is generated across the IT estate, making capturing and processing this data in real time a challenge. Meanwhile historical data is stowed away in customer or inventory databases, or in shipping and payment systems on disk-based CRM and ERP systems that are slow and difficult to access.
Then there’s computation. Streaming and historical data must be integrated and processed at sub-millisecond performance levels. But integration points between systems are prone to create bottlenecks that impede analytics and application performance. Added to this are the existing computational and security challenges of processing data in highly distributed networks, right out to the edge where the customer lives but processor resources are scarce.
It takes a platform-level approach to overcome these challenges. That means creating a common and pre-integrated data processing, analytics and computation environment that lets you break through the data and system silos to ingest and enrich streaming and historic data consistently while delivering reliable and consistent performance on that data’s journey.
What does a real-time platform look like?
It has two core attributes. The first is a unified data storage and execution engine for streaming and historic data. This provides a basis for applications to act on data as it is created or captured rather than – as often happens – for data to be processed offline. Your engine should allow data streams and threads to execute concurrently and seamlessly distribute work for performance, scalability, and responsiveness. Integration at this infrastructure layer frees IT teams from having to build and maintain complex integrations with their inherent performance bottlenecks.
The second core feature is in-memory computing architecture. An in-memory data-stores allow accessing and processing data in the fastest way possible – in RAM, meaning you don’t have to wait for data to be retrieved from slower media. This is key for real-time analytics, applications, and payments. An advanced in-memory data store can cluster nodes and pools of memory to provide local computational power and high levels of application performance as well as a caching layer for microservices. In-memory can therefore provide performance required by real-time analytics, applications, and payments.
In-memory has a further advantage – enhanced data security. Because data is stored in memory rather than to disk, payment processors do not have to store sensitive data, such as payment card information, on persistent storage – an avenue of potential attack for hackers. This also facilitates easier compliance with privacy regulations.
With expanded digitalization comes a new wave of real-time commercial opportunities. Fathom your customers using a unified platform of data analytics and computation and you can deliver the business intelligence needed – at the speed required.