Data is the fuel that drives every organization. It’s also smothering organizations with mountains of information they can’t easily access or use.
Many agree that the most common data challenges can be boiled down to the 3 Vs. First, the volume of data is increasing at an extraordinary rate. Humans produce 2.5 quintillion bytes of data every day. Second, the velocity of data, i.e., the speed at which that data is generated, distributed, and collected. And finally, the variety of data, meaning that data flows into organizations from many different sources and in different formats, i.e., structured, semi-structured, and unstructured.
Consequently, organizations face several challenges from a data architecture perspective, including a need for real-time access to data, a lack of strong governance controls, and issues automating processes using robotic process automation (RPA). Data architecture is a set of principles that guide how an organization collects, integrates, improves, stores, and delivers data to users so they can do their jobs efficiently and effectively.
Why data fabric is needed and how it accelerates digital transformation was the main focus of a recent webinar featuring Forrester’s Noel Yuhanna.
During the webinar, the discussion looked at these challenges and the importance of weaving data fabric into your digital business.
Lack of Real-Time Insights
Democratization of data, which is about distributing or sharing data across organizations, is extremely hard with legacy data architectures.
“One of the challenges people have with legacy platforms is the real-time-ness of data,” said Noel Yuhanna, vice president and principal analyst at Forrester Research Inc., on the webinar. “The architectures we’ve had traditionally aren’t keeping up to the new demands of data, such as real-time.”
That’s because legacy data architectures use the batch processing model, he said. Under this model, data is collected over time, typically once a day, then that stored data is sent to an analytics tool for processing. With real-time data processing, on the other hand, data is processed as soon as it flows into the business.
Companies need a real-time data architecture that enables them to unlock the full potential of their data, no matter where it lives. That’s where data fabric comes in.
Data fabric is a platform that delivers one unified view and connects data across heterogeneous systems, regardless of where the data resides – on-premises, hybrid cloud, or multi-cloud platforms. A company can then use this single source of truth to identify trends and patterns they might not otherwise uncover, helping them make better business decisions. A data fabric does away with data integration issues and improves the quality of data in addition to simplifying data sharing. A data fabric also automates critical data governance functions by applying corporate policies to data and delivering trusted data.
Data fabrics help organizations boost the value of their data and accelerate their digital transformation journeys. Data fabric works as one unified architecture — as well as the services and technologies that run on that architecture — that gives any employee real-time access to data. Providing real-time, secure, and connected data across the business supports modern applications, insights, and analytics.
A data fabric architecture enables organizations to leave their current data sources in place and let the data fabric stitch them together. Data fabric removes the friction between the producer of the data and the consumer of the data and makes the data more accessible to enable the enterprise to get [real-time] insights.
Lack of Strong Data Governance Controls
Data governance establishes data policies about how companies gather, store, process, and dispose of data. It also ensures the data’s consistency and reliability and that it is correctly used. This is increasingly important as companies confront new data privacy regulations and depend on data analytics to operate more efficiently and make better business decisions.
“Data fabric is the semantic layer that sits above your data sources, and this semantic layer drives consistent trusted data,” said Yuhanna on the webinar. “And this is a very important fundamental principle of data fabric — driving real-time consistent, trusted data.”
However, many organizations using legacy data architectures struggle with data distribution and controlling and managing data, whether it’s for the European Union’s General Data Protection Regulation or the California Consumer Privacy Act, according to Yuhanna’s presentation.
When companies don’t govern their data appropriately, they introduce security and data governance risks and failures, resulting in irrelevant, poor-quality, or inaccurate data that isn’t helpful to the business. The data fabric architecture helps automate data discovery, data consumption, and data governance to provide organizations with data that’s ready for analytics.
By implementing security rules and automated data governance, enterprises remain compliant and reduce the risk of exposing their data.
“Data fabric enables stronger governance controls,” Yuhanna said during his presentation. “And I think every organization, whether it’s financial services, retailers, healthcare, are actually requiring a better control of data from a security perspective and governance perspective as well. And some of these organizations, especially in financial services and healthcare, have been using data fabric to protect customers’ personally identifiable information.”
RPA Not Delivering on Its Promise
There’s a lot of talk about robotic process automation (RPA) today; however, automation using RPA has yet to really fulfill the return on investment that was promised.
There were two reasons for that. One is that people were automating processes that were inefficient to start with, missing any opportunity to improve their data architecture. Second, we noticed enterprises were only automating a few processes, not the full spectrum of manual tasks.
For RPA to succeed, organizations must implement intelligent tools to capture every step in the process. Data fabric enables that AI use case. It transforms RPA by augmenting the processes and making the processes smarter.
Conclusion
Data fabric architecture is an innovative technology that vastly improves data management and integration to maximize the value of the growing amounts of data flooding enterprises. An abstract layer that breaks down data silos, data fabric enables organizations to get the right data to the right users in real-time no matter where it resides. Data fabric facilitates data democratization across the organization, offering more robust data governance controls and helping enterprises make more informed decisions based on real-time data insights.
About the Author
Pradeep Menon is EVP, Digital Transformation Services Delivery, at Orion Innovation, a leading digital transformation and product development services firm. For over two decades, Pradeep has played an integral role in the development of Orion’s Global Application Development practice and Digital Transformation services delivery. He specializes in the Finance and Auditing domains with an exceptional concentration in program governance and on-time delivery. Pradeep is recognized for his leadership abilities and for cultivating a culture of extraordinary achievements. He is passionate about leading large-scale transformations, encouraging innovation, and delivering at scale.
In his current role, Pradeep is responsible for driving technology innovation centers – Centers of Innovation (COI), while incubating the capabilities using emerging technologies. Pradeep is also responsible for Orion’s Global delivery methodology & strategy, which delivers the highest level of complex applications across multiple technology platforms. He’s also instrumental in creating Orion’s Portfolio Governance platform Conscious™ and Orion’s own agile process called O’gile.
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