A data fabric is a new way of managing data across different sources and environments. It uses smart and automated systems to connect, transform, and share data. A data hub is part of a data fabric that helps you collect, process, and analyze data from various sources. According to Gartner, by 2026, 80% of organizations will have deployed multiple data hubs within their data fabric to drive mission-critical data and analytics sharing and governance.^[1] Organizations can use different data hubs for different needs, domains, or use cases. For example, they can use a data hub to find and explore the data in their organization, combine and transform the data from different sources into one format, apply rules and policies to make sure the data is good and safe, or deliver the data to the users and applications that need it. You can also use a data hub, for example, for customer data, product data, or supply chain data.

An organization may struggle to access and integrate data from different sources and formats, which can result in data quality issues and inconsistent results. It may also find it hard to handle the diversity and dynamism of data, which can lead to increased costs and reduced performance of data solutions. Moreover, it may face regulatory risks and reputational damages if it fails to secure and protect its data according to the applicable laws and policies. These challenges can limit the organization’s ability to leverage its data for operational and analytical purposes and hinder its growth and innovation potential.

Financial Fabric is an innovative New York-based fintech company founded in 2014. The company provides specialized data analytics solutions to the buy side, with a mission to enable investment managers to make investment risk analytics and fund operation decisions in near real-time. Financial Fabric’s proprietary DataHub runs on Microsoft Azure and empowers fund managers across hedge funds, family offices, and corporate treasuries with asset, trade, cash, and other financial information. Financial Fabric was dealing with large datasets and needed a solution that could rapidly ingest data, efficiently run complex queries, and provide near real-time analytics services to its financial service users. Financial Fabric learned about the capabilities of Azure Data Explorer (ADX) from Microsoft and decided to try it out. The company moved one workload to ADX as a trial and found that they were able to ingest data extremely rapidly with ADX. Query speed was also drastically improved, with response time going from several minutes or hours to just a few seconds. The data was also subject to extremely high compression, making for exceptionally efficient storage. Recognizing the potential benefit of ADX for all its clients, Financial Fabric began to incorporate ADX into its core DataHub product. The platform has helped Financial Fabric’s hedge fund and investment management clients deal more nimbly with specific data requests and has given data scientists a lot of freedom and flexibility. Financial Fabric continues to collaborate with the Microsoft ADX team on a weekly basis. ^[2]

Imagine that your data is like a fabric that covers your entire business. Each piece of data is a thread that connects different parts of your business, such as customers, products, sales, etc. Now, imagine that you have multiple data hubs. These hubs act like needles that stitch together different threads of data. These data hubs allow you to see and use your data in a unified and consistent way, no matter where it comes from or where it is stored. Without moving or copying your data from one place to another, you can also access and analyze it in real-time. You can also easily change or add new data hubs as your business needs and goals change. You can also ensure that your data is of high quality, secure, and compliant with the rules and regulations that apply to your business by applying common policies and rules across the data fabric. And you can also encourage more collaboration and innovation among your employees and partners by allowing them to easily consume and share data. This is how deploying multiple data hubs within a data fabric can help you use your data to achieve business success.

An organization that has deployed multiple data hubs within its data fabric can use its data better and smarter. It can use its data to make better decisions, actions, and outcomes. It can also create a data-driven culture that supports collaboration and innovation among its data users and makers. Some of the benefits that such an organization can enjoy are better customer experience, better operational efficiency, better business agility, and better competitive advantage. By using multiple data hubs to collect, process, and analyze customer data from different places and ways, the organization can understand its customers better and what they want and do. It can also deliver more personalized and relevant offers, suggestions, and interactions to its customers across different points of contact, making them happier, loyal, and stay longer. By using multiple data hubs to connect, change, and deliver operational data from different systems and platforms, the organization can improve its business processes and workflows. It can also automate and simplify data tasks and activities, reducing mistakes, delays, and costs. It can also check and measure its operational performance and find areas for improvement. By using multiple data hubs to access and analyze real-time data from different sources and environments, the organization can react faster and more effectively to changing market situations and customer needs. It can also change to new data needs and requirements by adding or removing data hubs as needed. It can also try new data sources and methods to find new insights and opportunities. By using multiple data hubs to improve and share data across the enterprise, the organization can use its data for strategic purposes. It can also create new products and services based on data insights and feedback. It can also stand out from its competitors by offering unique value propositions based on data.