Digital transformation is now vital to business survival, and an increasingly key element of digital transformation is real-time data. Digital integration hubs (DIHs), which are a type of Smart Data Hub, are increasingly being used as high-performance data access layers that provide real-time access to data from across the enterprise to power real-time business processes. For example, financial services firms are using DIHs to enable multiple line-of-business systems to simultaneously access siloed data from throughout the enterprise to drive upsell and cross-sell opportunities across all their customer touchpoints.
What is a digital integration hub?
A DIH, or Smart Data Hub, aggregates a subset of data from multiple on-premises and cloud-based systems into an in-memory cache. This high-performance data access layer can be simultaneously accessed by multiple business applications at in-memory speeds. The source data systems can include on-premises or cloud-based data warehouses and data lakes, operational datastores – including those residing on mainframes – SaaS applications, and streaming data sources. The aggregated data can then be accessed in real-time by any number of business applications using a variety of APIs such as SQL, from backend operational systems to frontend customer management applications. A synchronization layer, or change data capture layer, ensures that the data in the in-memory cache is constantly updated as changes are made to the underlying datastores.
The most popular and cost-effective solution for building a real-time Smart Data Hub, or DIH, is with an in-memory data grid (IMDG). Powered by an in-memory computing platform, the IMDG is deployed on a cluster of commodity servers that pools the available CPUs and RAM and then distributes data and compute across the cluster. The IMDG cluster can be deployed on-premises, in a public or private cloud, or in a hybrid environment.
The IMDG aggregates data in memory, and the in-memory computing platform uses massively parallel processing (MPP) across the distributed cluster. The combination of in-memory data caching and MPP provides real-time performance, which is up to 1,000x faster than a solution built on disk-based data storage.
The IMDG typically supports a range of APIs, including key-value and SQL support. ACID transaction support may also be available to ensure no loss of data when transactions are processed on the in-memory data. The distributed computing architecture of the IMC platform also makes it easy to increase the compute power and RAM of the cluster by adding new nodes. The platform automatically detects additional nodes and redistributes data to ensure optimal use of the cluster CPU and RAM.
With the relevant data cached in the DIH and compute distributed across the in-memory computing cluster, business applications, including everything from consumer-facing websites to back-office systems to mobile applications, can access real-time, 360-degree customer views that would be impossible to achieve without the DIH.
Digital integration hubs in action
A couple of use cases make it easy to see the power of a DIH. Most financial institutions offer several services, from core banking to credit cards to mortgages to wealth management. However, the data for each of these services is typically stored in multiple, siloed systems, including operational datastores, data lakes, data warehouses and SaaS applications. This has made it extremely difficult and often cost-prohibitive for firms to create a real-time 360-degree view of a customer.
A DIH, or Smart Data Hub, solves this problem. The DIH can span all the source systems, aggregating each customer’s current and historical information to create a single, comprehensive view of the customer. This view can then be used to present upsell and cross-sell opportunities across the firm’s entire product line and present these opportunities to the customer via any touchpoint: mobile app, website, bank teller, ATM, etc.
Retailers can also benefit from a DIH. A retailer that wants to hone its online recommendation engine to instantly deliver the most relevant recommendations needs access to data from multiple sources, including the customer’s account information, current reward program offerings that might apply to the customer, the customer’s past purchases (and potentially returns), the past behavior of similar customers, current product inventories, pricing and shipping information, etc.
Again, the DIH can span all these data sources and create real-time views to provide customers with the most relevant recommendations. At the same time, the retailer could also use the DIH to empower in-store personnel with an app that provides customer account data, existing stock quantities and location, stock delivery schedules for out-of-stock items, and recommendations for alternative products – all to ensure a more personalized and satisfying in-store customer experience.