Data Integration Architecture – What is it and why does it matter? By Traci Curran September 8, 2021 Data is everywhere. In any organization, you will find data in multiple applications, databases, data warehouses, and in many cases, public and private clouds. Usually, data does not belong to a specific group within an organization and is often shared across teams and applications. Similar to a professional sports team, each function in the organization should have a specific role, sharing data and information in real-time to support better outcomes. In order to efficiently share data, businesses need to focus on integrating their data in an automated and timely manner. This can be a challenge when functional units use multiple applications and store data in multiple locations. Organizations need a data integration architecture to connect secondary to primary data sources, normalize the information, and automate the flow of information without manual interventions. What is Data Integration Architecture? Data integration architecture defines the flow of data between IT assets and organizational processes to enable interoperability between systems. Today, data is everywhere, often stored in multiple formats in a complex non-integrated fashion. This means users spend far more time searching for data and information instead of using it to make better business decisions. When data is shared manually, obtaining knowledge for decision support becomes cumbersome and impacts customers and business performance. Creating a data integration architecture allows integration of disparate data and provides normalization to enable faster decision support. The underpinning data and information used by functional units must be systemized and architected to enable collaborative decisions and faster innovation. Creating a data integration architecture does not mean combining all data sources into one data source, such as a giant database or a data warehouse. It does mean understanding data relationships enabling interoperability between the systems and tools used across the organization. Data integration architecture helps define the flow of data between internal and external people and technologies. This helps remove data silos and enable accurate data usage across the organization. Data integration design consists of mapping primary systems and secondary systems. Secondary systems feed data and information to the primary systems. The primary system can vary across functional units, but the data needs to remain consistent across the organization. Each functional unit in an organization can have a specific primary perspective based on their job function and the decisions they have to make. Some secondary systems will always be secondary systems. The overall architecture has to consider the users of the systems and the data sources that need to be accessed. In other words, enterprises need a single source of truth. The need for Data Integration Architecture Data integration needs architecture to map, reconcile, and deliver data across multiple sources, often with different expressions. The architecture should understand the source of the data and help reconcile and normalize the data for use. This helps enable better overall communication between functional units in the organization and improves service performance. Integration architecture management can be done from multiple perspectives. Service-oriented data integration architectures (SOA). Operational data integrations looking at key performance indicators (KPI) from multiple related operational processes. All types of data can be segmented into a specific area with its architecture, data model, scope, and details. Organizations should understand the value of data integrations for decision support and knowledge management. Examples of Data Integration Architecture There are many starting points for the creation of a data integration architecture. Organizations can begin with a single functional unit or set of applications. Investigating what data sources are used to make decisions helps map data sources into primary and secondary use cases. Examples of Data integration architectures are: Configuration Management Database (CMDB) feeding Configuration Management System (CMS) feeding Service Knowledge Management System (SKMS) Marketing Systems feeding into a Customer Relationship Management System (CRM) or Enterprise Resource Planning (ERP) application Moving SharePoint data into a Knowledge Management System (KMS) Multiple data sources feeding an application Using a data integration architecture can also help with technology consolidation, saving money, time and improving the performance of functional units within the organization. Many times, duplicate sources of information may be discovered that cause inconsistent decisions and degrade the performance of the business. The organization should apply Lean principles when performing data integration architecture projects. Actian and Data Integration Architecture Actian is a leader in data management, including data integration architecture. Our data solutions enable organization performance and reduce the risk of manual processes. Actian helps ensure that business-critical enterprise information is effectively harnessed for real-time service delivery success no matter where it resides. Specific Enterprise Data Integration solutions are: DataConnect – Highly scalable hybrid integration solution that enables you to quickly and easily design, deploy and manage integrations on-premises and in the cloud DataFlow – Provides a parallel execution platform for real-time processing of data-in-motion. Accelerate the analysis, extraction, transformation, and loading of data across the business Business Xchange – Fully managed B2B integration service that enables electronic data interchange (EDI) to exchange procurement and supply documents Contact us today to discuss how we can help your organization become higher-performing with your data and information. About Traci Curran Traci Curran serves as Director of Product Marketing at Actian focused on the Avalanche Data Platform. With more than 20 years of experience in technology marketing, Traci has previously held senior marketing roles at CloudBolt Software, Racemi (acquired by DXC Corporation), as well as some of the world’s most innovative startups. Traci is passionate about helping customers understand how they can accelerate innovation and gain competitive advantage by leveraging digital transformation and cloud technologies.