Data Fabric – Reimagining Data Management with Modern Capabilities

Data Fabric

Organizations are mobilizing resources, tools, and solutions to initiate Digital Transformation to support their business functions – who, by the nature of their modern, agile ways of working, are becoming more demanding of their data. A successful Digital Transformation is almost always marked by the systematic assembly of geographically diverse data spread across devices, applications, and sources. The success of these transformations is linked to a sound data management strategy that must align with business-value deliverables. Data Fabric, an architecture that embraces these challenges, is a crucial enabler in streamlining availability and access to data and improving the discovery of critical insights.   

For many organizations, a Data Warehouse was the answer to their data management challenges. However, the introduction of cloud technologies changed how enterprise data management was perceived since compute and storage were not intrinsically linked anymore but could be physically separated from each other. When Data Fabric evolved as a way to connect data on public, private, or on-premise locations, experts began to see its potential. Organizations were no longer compelled to migrate information to a central repository – they could access data wherever it lived by stitching multiple data sources into a single thread. 

Today, Data Fabric is gaining acceptance by enterprises as a significant step in the maturity curve for their data management capabilities. However, the Data Fabric approach is not complete – it continues to evolve while more data challenges come to light. 

On paper, Data Fabric has the potential for simplifying data management and helping decision-makers find business-critical insights. However, integrating such an architecture can be a convoluted affair that requires various individuals’ support and, more importantly, buy-in. The technology used in a Data Fabric is a facilitator in attaining fast-paced transformation, but the equation is incomplete without the involvement of people who will ultimately absorb Data Fabric piece-by-piece into their process and culture. 

Aligning people and processes is imperative to prepare for possible impacts while integrating Data Fabric into an organization. It calls for planned and coordinated changes in various functional teams and training individuals to meet new technical needs while also understanding the business value potential that can be achieved. One of the most consequential people/process outcomes of Data Fabric can be the automation of tasks, which helps minimize manual efforts and enables decision-makers to utilize resources on more impactful tasks better. 

Organizations differ in data maturity stages; the need to improve capabilities is dynamic, and this varies globally and by industry. Some companies could have a system of maintaining physical records, while others could digitize their physical records, and some could be using only digital methods to keep records. Organizations that have been at the bleeding edge of adopting modern tools could employ AI models to extract quick insights and plug the learnings into strategies. Before the ship sails on the Data Fabric journey, a thorough examination of the current state is necessary to ensure the proper use cases are prioritized by aligning them with realistic, achievable, and measurable business-value aligned outcomes. 

Here are some specific outcomes that Data Fabric can foster in organizations: 

  1. Superior Data Governance – 

Every day, almost every person generates about 1500 digital interactions. By 2025, data is expected to nearly triple to 5500 interactions per person. This exponential data growth stresses the urgency for a comprehensive data solution that implements Data Governance best practices. Data Fabric helps organizations create governance rules extracted by AI & ML tools from regulatory documents and ensures that data is used ethically.  

  1. Magnified Understanding of Data Sources with DataOps – 

Understanding where data comes from is essential for maintaining the quality and integrity of your data. Data generated from disputed territories or dubious sources could disrupt enterprise data uniformity. Data Fabric can flag such questionable sources and alarm the user to take protective measures with intelligent AI-embedded systems. The AI and Machine Learning algorithms learn by analyzing the incoming data, its nature, and how it is utilized for business-relevant purposes. 

  1. Organizational Resetting – 

Data Fabric strengthens an organization’s capacity to extract more from growing data volumes and arrive at actionable intelligence to optimize processes, operations, and systems for improved efficiency across the board. Data can be approached in a standardized way, right from its organisation and assembly to discovery and insight extraction. By connecting dynamic data sets, enterprises can retire cost-cutting resources and put them to better use. 

  1. Automated Data Engineering and Integration – 

Data Fabric helps integrate all tools, processes, and data sources into a single access source and automates the delivery process. Decision-makers can access enormous multidimensional data within a single point of access, locate valuable data quickly, and spend more time applying these insights to drive value for the business. Enterprises can capture data changes in real-time and custom query data with ease.

Experts have rightly said that “Data Fabric is the transformation and not the end state.” Data Fabric is an evolving architecture that will deliver more capabilities in time as data challenges and associated requirements grow more complex in nature. 

Data Fabric can provide organizations with a trusted solution that taps into idle and unused data to discover potentially game-changing insights. With AI at the center of Data Fabric, organizations can reorient themselves to improved processes by identifying flaws early on and making a strategic decision to optimize. 

Navigating the Data Fabric adoption journey requires strong leadership to identify gaps in people, processes, and technology. Leaders within the organization can then direct their teams to the destination with a meaningful roadmap that marks milestones and their purpose. Consider seeking guidance from a team of expert professionals who have a proven record of successful implementations and come with trusted and reliable expertise to transform to your desired future state. 

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Kevin Burnley

Kevin Burnley

Kevin Burnley, Director of Technical Sales, is one of the founders of the EMEA Practice at Mastech InfoTrellis. He comes with over a decade of experience in Data Governance, Data Quality, Master Data and Data Integration projects. Kevin assists clients in realising business value by optimising their data management platforms to drive revenue, reduce costs and meet regulatory compliance. As a specialist in the IBM Data & AI software portfolio, he leads Mastech InfoTrellis' Data Fabric and Cloud Pak for Data competencies in EMEA. Kevin has been the UK & Ireland Senior Technical Sales Consultant for IBM Analytics, specialising in Information Integration and Governance. He brings a wealth of knowledge and experience from implementing IBM solutions.