12/01/2023 | Press release | Distributed by Public on 12/01/2023 07:40
By:Parth Parkhani, Data Specialist - Azure
A data-first organization emphasizes using and managing data wisely in all operations. It fosters a culture where data is crucial to guiding decisions, sparking innovation, and streamlining processes. It's not just about data storage; accessibility matters. Everyone should be able to access data easily, analyze insights, and automate actions.
Despite being a key goal, achieving a data-powered organization is challenging. Enterprises struggle to embed data-driven practices, spending excessive time on real-time reports and system integration.
Despite significant advancements in data ecosystems and integration technologies, companies still struggle to derive actionable insights due to persistent data silos. Additionally, the complexity of modern data landscapes, including the variety of data types and formats, creates data accessibility challenges.
Moreover, the rapid pace of technological change introduces a skills gap between data consumers and data processors. Many companies find it challenging to keep up with the demand for data professionals who can navigate and derive insights from complex data environments and also understand the business context for getting the data. Regulatory concerns further limit data usage, and navigating governance and compliance adds complexity to data initiatives.
In the contemporary business landscape, the seamless management, transformation, and utilization of extensive datasets have become imperative for success. However, organizations grapple with a significant predicament-the high costs associated with the maintenance, transformation, and consumption of large volumes of data. This financial burden is intensified by the inefficient utilization of resources within the data ecosystem. Inefficient processes and underutilized capacities contribute to the escalating costs, creating a pressing business problem that necessitates strategic interventions to optimize resource allocation, enhance efficiency, and ultimately mitigate the financial strain associated with data management.
Becoming truly data-driven requires more than just adopting advanced technologies; it entails addressing organizational, cultural, and regulatory barriers. Companies must develop a comprehensive strategy that aligns technology, people, and processes to fully unlock the potential of their data assets.
According to a 2023 by Gartner, although data is a component of the CIOs' roadmap, only a small fraction of data analytics leaders view their teams as effective and possessing a sufficient level of data maturity to provide valuable information to the business. This observation becomes even more intriguing when juxtaposed with business investments in the data domains. How can we ensure that data teams in organizations shift their focus from provisioning systems, governing data, and creating static reports to activating the data?
Although there's no one-size-fits-all solution to addressing intricate challenges, Microsoft's latest data offering, Microsoft Fabric, promises to address these challenges. The underlying principle of the Fabric platform is to provide a unified solution that combines data extraction and transformation, visualization, and machine learning products, all while offering centralized governance and identity management through Microsoft Purview.
Fabric tries to address the current challenges at organizations as follows:
Transformative Pillars:Redefining your Data Estate
A robust governance framework is vital for secure data sharing and collaboration. Integrating governance into the organizational culture is challenging. Microsoft Fabric aids governance by aligning it with storage, incorporating Purview features like information protection, data cataloging, lineage views, and sensitivity labeling into storage. It streamlines access management for cloud resources and data, enhancing governance. Fabric offers a single lakehouse storage for organizational data, logically segregated into workspaces. It operates with no-copy data storage, enabling users to access trusted and transformed data directly from without duplication. This eliminates risks like data errors, outdated information, and high latency that often accompany data handoffs.
Data estates' success relies on integrated systems managed by the data teams. This demands significant effort in creating, provisioning and maintaining these complex tools. Optimizing disparate capacities across data engineering workloads is crucial for cost control.
Fabric simplifies this by offering a unified platform, freeing teams from integration concerns. Automatic environment provisioning aligns with minimizing user workload, enabling focus on data enhancement rather than intricate integrations.
It handles compute provisioning, requiring organizations to provision a single capacity, simplifying systems, and optimizing compute resources. The shared compute model enhances resource utilization without individual capacity provisioning. The Artificial Intelligence (AI) layer adds capabilities like code writing, model building, and contextual suggestions.
Based on our experience with clients, many companies with varying levels of data maturity aspire to implement self-service Business Intelligence (BI) for the end users. This goal of self-service consumption essentially aims to alleviate Information Technology (IT) workload to allow them to focus on other tasks and enables business users to swiftly explore, access, and generate the analytics crucial for their daily operations. Despite robust data models, crafting appropriate reports remains challenging as business users depend on dedicated analytics teams.
Addressing these challenges, Microsoft Fabric simplifies self-service analytics by integrating Microsoft into Power BI, facilitating conversational analytics and thus allowing end users to generate analytics using simple, intuitive language. This empowers users to start using language to explore data and thus empowers them to make faster data-driven decisions. Another advancement in Fabric, contributing to the wider adoption of self-service analytics, is its integration with Microsoft 365. This essentially means that end users can collaborate on data that they have discovered on Power BI, share the metrics over Teams with their team members, and quickly populate the same into Excel and PowerPoint to share with the larger team. An AI layer of Copilot puts power back into the hands of the end users to collaborate faster.
You should evaluate how Fabric can help you overcome your data estate challenges and fit your data strategy. Our clients have tried Microsoft Fabric with our guidance, and they have achieved amazing results and value. They have improved their self-service BI and team collaboration with unified UIs, enhanced their integration of different cloud resources, and strengthened their data governance with Purview integrated into the data transformation and storage resources.
Data Specialist - Azure
Parth is a Data & AI enthusiast with expertise in driving customer experience & value in Data, Analytics & AI for fortune 500 companies. He is passionate about the transformative potential of AI and Software in solving business challenges of growth, profitability, and resilience.
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