Stylish move: data fabric gives enterprises the edge over siloed alternatives
Any organization on the fast track to digitalization knows how mission-critical relevant data can be to growing a future-ready business. Capturing good, reliable data from disparate parts of the business and constructively harnessing the details requires dependable and highly capable data architecture. To meet these information needs, sectors with a variety of trusted, distributed data channels are harnessing the benefits of data fabric.
The need for interoperable, cross-platform data that can be gathered in real-time is becoming more urgent for information-reliant organizations, such as healthcare providers and financial services operators. The availability of real-time and historical data is integral in those sectors. Having that information to hand at any point, with full visibility and accessibility, can make all-important contributions to delivering excellent customer service and fulfilling the needs of the business.
A cohesive data architecture that is interwoven like fabric is a must-have for organizations that need to make use of high-performance application ecosystems. Systems must be compatible with next-generation technologies to drive operational efficiencies, while at the same time reliably service heavy data use and established analytics to support better outcomes for all stakeholders.
And, as a greater number of organisations accelerate their adoption of digital tools – ranging from cloud migration to automation to predictive tools powered by artificial intelligence – the need for a highly interconnected information system with functionality much like data fabric will only grow in importance.
Why data fabric? As the name implies, just like how cloth can be stitched together, data fabric architecture can connect and align capabilities across a variety of data services throughout a host of multi-cloud environments. Despite differing integration methods, endpoints, and interoperability requirements associated with multiple applications, a system like the InterSystems IRIS data platform can bring together and standardise information management across cloud, on-premises, and edge environments, as well as at the device level.
The data fabric key definition
“The term ‘data fabric’ is often abused. Every data platform nowadays wants to claim they are providing data fabric, but it is actually about interconnected data, how can we wrangle the data,” says Kenneth Kuek, Business Development Director of leading data management solutions provider InterSystems, which specialises in unlocking complex data challenges in various sectors.
Some systems are woven together more intricately than others, of course. For financial services and healthcare, data security is high on the agenda, given the need for operators to protect sensitive personal information.
“InterSystem actually plays a part at the data security layer. So like with a bank offering omnichannel [payment] solutions sitting on top of IRIS, that will be another layer of security,” he emphasizes. The IRIS in question is InterSystem’s holistic, cloud-first data platform – supplying customers with the ability to implement an array of technologies and data sets, with fewer code headaches, less maintenance, and more efficient use of system resources. Together, this all adds up to delivering a higher and more efficient return on investment (ROI).
IRIS delivers better platform security too. “So we have application layer security and our data platform security. Logging into the application, the data is not visible to the user. But the bank as the owner can go deeper into the data layer, where we have captured the payment information, the product info, the inventory – everything,” Kuek comments.
The platforms and applications leveraging that data must be well protected, but also able to harmonise the information from multiple sources so that they are still usable in terms of delivering actionable insights.
“The financial institution, for example, is always interested to look at their merchants’ activities, their transactions. But the data comes in all forms,” says Kuek. “That’s why we’re working with a partner at managing omnichannel [environments], which is a very good example [of] where we aggregate different sources of data.”
“Take an e-commerce platform like Shopee, Lazada, or Amazon. The banks are always interested to understand how the merchant is doing on different platforms – are they selling their products well, are they facing a lot of challenges?” he continues. “So when a data fabric platform like InterSystems starts to ingest the data, among the information we can ingest [are a number of key details]: the product that they sell, is it a high value or low value item, is the product moving, is there a lot of inventory or just a little to test out? And of course, [there is the topic of] collection.”
“What form of collection does the merchant have? Is it cash on delivery, is it PayPal, do they accept credit cards?” Kuek asks. “So each form of data coming in will be different, and it will be about how we are putting the data together, and making it meaningful for the client.”
The client could be a financial institution or a bank, who can use this data to decide action items, such as extending an additional line of credit if the data supports this. “The banks can easily decide whether to extend the banking facilities because they can see the sales activity all very clearly, very transparently,” asserts Kuek.
Thanks to its agility, data fabric can avoid the shortfalls of a data lake or data warehouse – the prevalent methods for storing and accessing cloud data in the past few years – which are just another siloed approach to information storage. Such methods can make integration with other data sources, including third parties, a veritable hindrance. It’s a real problem with these recent data frameworks because as data volumes spike, data silos grow too, and so do the complexities associated with integrating or making use of that information.
Kuek gives an example of a Singapore customer providing supply chain financing. In such a scenario, it’s important that the bank has sufficient oversight. “There is historical data, there is ongoing [real-time] data, giving the bank confidence that this is really a very low-risk style of loan to the merchant,” Kuek points out.
Data fabric the fashionable solution
The InterSystems IRIS data platform demonstrates why organisations the world over are implementing smart data fabric over outmoded data management systems. IRIS simplifies the provision of platform and data architecture, providing a host of data services including database management and inbuilt data analytics spanning a myriad of innovations, including natural language processing and machine learning. What’s more, solutions can harness a transactional-analytic database engine to deliver the sort of high-performance at scale that can facilitate both real-time and low latency applications and analytical use cases.
InterSystems IRIS data platform not only slashes operational complexity and maintenance, but also drives down the total cost of ownership while speeding up development times and lifting ROI. Find out how to best support your mission-critical, data-intensive applications at scale, try out InterSystems IRIS for free right now.