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Data management agility has become an essential priority for companies in an emerging distributed, diverse, and complex environment. To decrease human errors and overall costs, data & analytics (D&A) managers need to think beyond legacy data management strategies and move towards modern technologies such as AI-enabled data integration.
The increasing design concept called “data fabric” is a powerful practice to overcome data management challenges like frequent maintenance of earlier consolidation, low-value and high-cost data integration cycles, the rising need for real-time and event-based data sharing and more.
A data fabric is an upcoming data management design for acquiring reusable, flexible, and augmented data integration services, pipelines, and semantics. They support analytics and operational use cases across several deployment and orchestration processes and platforms. Data fabrics assist in integrating different data integration styles and use active metadata, semantics, ML, and knowledge graphs to augment data integration delivery and design.
If you’re in search of a modern and effective method for handling your data, using a data fabric could be the ideal solution. This approach enables you to amalgamate data from various sources, offering you a clear and comprehensive real-time perspective of your data landscape. Not only does it streamline the data management process, but it also expedites data processing, empowering you to make quicker and more well-informed decisions.
Data fabrics possess the ability to seamlessly expand to accommodate the ever-growing volume and diversity of data that businesses generate today. This aids in boosting productivity, facilitating better decision-making, and maintaining a competitive edge. Moreover, employing a fabric ensures users have secure and compliant access to high-quality data required for performing their data-related tasks.
Recently, there has been a remarkable surge in the number of businesses, entrepreneurs, and companies entering the networked environment. Ever since the widespread accessibility of the internet, all websites have transformed into valuable sources of data. Consequently, enhancing the value of this data has become of utmost significance. Nevertheless, certain obstacles are impeding the endeavour to increase the value of data, and these challenges can be outlined as follows:
These obstacles prove that data is growing at an exponential rate. These challenges make accessing data and extracting insights from it extremely tough. Any company or organization specializing in ML, big data solutions, and AI must gather, organize, and process their data. Many businesses typically address this issue by managing data across warehouse departments, employing diverse methods. However, while this approach works well for multiple teams, a significant amount of data is often overlooked and needs to be tapped for enterprise-wide data access.
The challenges related to data usage and accessibility result in reduced productivity and a shortage of reliable data for deriving insights and making future predictions. The solution to address these issues lies in data fabric. By using data fabric, businesses can effectively gather credible data from their entire network and comprehensively analyze it to extract valuable insights.
The term “Fabric” is defined as the integrated layer of data and interlinking processes across all data units such as multi-cloud and hybrid platforms.
By bringing together AI models, automated technologies, continuous analytics, and machine learning across complex data units, companies can enhance data trust, make effective decisions, and drive digital transformation. Some of the common ways include:
Data fabric starts with online transaction processing, where data linked with all transactions is updated, stored, and integrated into a warehouse over a database. This data is then cleansed, structured, and processed for advanced applications. Anyone with access to the data can use it to derive insights to help the business adapt and grow. The implementation of data fabric requires the following:
Data fabric is particularly well-suited for businesses that are geographically dispersed, have multiple data sources, and encounter complex data-related challenges. Nonetheless, it’s crucial to remember that data fabric differs from the need for data processing and integration. To accomplish those tasks, data virtualization becomes essential.
Companies with vast volumes of data can always turn to data fabrics as it offers a real-time flow of data and accessibility, adapts per the requirements, function across all systems, and requires minimum training without interference.