Increasing operational efficiency by reducing manual processes.Achieving data continuity and seamless knowledge transfer. Unlocking a layer of connectivity to help you stay competitive.Data integration is the framework or architecture that seeks to enhance visibility and allow users to see the flow of information through their organization.Īs an organization that still relies on a legacy system, your company may not only be at a significant disadvantage compared to competitors, but you can also miss out on many potential benefits, such as: Today, data comes in many different formats, structures and volumes, and it's more distributed than ever. It's no secret that technology has evolved rapidly in the last decade, bringing about a global digital transformation that millions of people rely on every day. Finally, the data is extracted and consolidated into a cohesive data set for the client to view and use. Then, the master server will intake the requested data from internal and external databases and other sources. Though many data integration techniques tend to have common elements, such as a master server and a network of data sources, organizations can use these tools in many different ways to meet their specific needs.įor some, a typical enterprise data integration process involves a user or company requesting data from the master server. That being said, there is no one-size-fits-all approach to data integration. With the help of the right tools and platforms, data integration enables effective analytics solutions to help users receive actionable, accurate data that drives business intelligence reports and learning. This guide outlines the ETL process very much relevant in data management today. Data integration is very much a part of that process as well. Today, as we move toward modern data systems, we can also use the power of the Delta Lake framework, where raw data moves to data transformations and onto useable data in a “medallion” approach of bronze, silver and platinum layers. Data integration also plays a key role in the data pipeline, which encompasses: Data integration is one of the primary components of the overall data management process, helping users share existing information and make better-informed decisions. This allows organizations to meet the information requirements of all applications and business processes.īecause fresh data enters your organization every second, it must always be accessible for analysis and made ready with data wrangling techniquesThis is where data integration steps in and makes your data meaningful and useful. This one view is often called the single source of truth or “SSOT.”īy consolidating this data into a usable dataset, users get more consistent access to various types of critical information. This approach seeks to combine data and integrate it from separate sources to make it easier for users to view data from one unified view. These databases include data warehouses, production databases and third-party solutions which generate and integrate various types of data. What Is Data Integration?ĭata integration is the process of moving data between internal and external databases, targets and sources. In this data integration guide, we'll discuss various data integration steps, examples, tools and what to look for in your modern solutions. New data integration methods, frameworks, and strategies can now ease the burden of growing complexity and volume and help you and your company benefit from tools that can bridge the gap between your manual operations and automation. As a leader or primary decision-maker in your company, you likely juggle many responsibilities, but a legacy system can make managing complex data seem even more time-consuming and disconnected. If your organization still uses a legacy system, you may be one of the many businesses that are being held back from modernization and streamlined processes. No matter your industry, it's critical to use a secure, efficient and scalable system to handle your data.
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