An Expert Guide: Selecting the Right Data Integration Tool for Your Business
Before pulling the trigger and getting that shiny new data integration tool, check out this list of features to look for in such a tool.
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Technological innovations have altered the integration landscape and getting consistent access to data has become challenging. As a result, there is a sudden surge in the demand for data integration tools for extracting business intelligence and streamlining computer-to-computer data exchange. However, while selecting a data integration tool, organizations must ensure that it focuses on aligning transformational and legacy technologies and addresses end-to-end integration needs. Experts believe that selecting the right integration tool can be a long-standing decision and organizations must exercise caution while making a strategic investment.
Selecting the right data integration tool is always a challenging job for any business owner. The tricky part is to find the right solution provider in a market where several new entrants and technological behemoths are operating. To select the right solution, organizations should closely evaluate their use case and integration needs.
Experts recommend that organizations should select a solution which addresses existing and new integration needs. Ideally, the right integration tool should scale to cover the following technology integration needs.
Business Intelligence and Data Warehousing: Analytics is not an afterthought anymore. An advanced integration tool should provide functionalities for extracting data from emergent environments and non-relational clusters like Hadoop, Hortonworks, Cloudera, etc. It should enable business users to combine digital ecosystems with information infrastructures, and predictive modeling technologies. Moreover, it should support a wide variety of data formats like CSV, RCFile, Parquet, etc. Solutions lacking metadata and modeling capabilities shall not be preferred.
Master Data Management (MDM): Another emerging criterion for selecting an integration technology is a strong vision and focus for Master Data Management. The solution should provide granular data access to support for data governance and metadata management. An advanced solution should ensure continuous support for data capture, and bulk data processing.
Database-Level Consistency: A relevant functionality which a data integration tool should ensure is data consistency across multi-tenant, bidirectional, and unidirectional applications and data models. This ensures flexibility across the organizational data initiatives. Organizations operating across a broad spectrum of partners should have this ability to aggregate data in an IT environment full of inconsistent databases.
Interenterprise Data Access and Sharing: The data integration framework must provide B2B integration capabilities for enabling organizations to seamlessly exchange data with partners, customers, authorities, and stakeholders. Advanced solutions are addressing inter-enterprise data sharing with self-service and citizen integration. These features help organizations in exchanging data in a complex IT environment of partners and external service providers without any disruption.
Data Lake Management: Organizations are solving innumerable business problems with managed data lakes. Modern data integration should pack controls for pulling, cleansing, and monitoring raw data. More importantly, there should be controls for orchestrating and scheduling workflows. Two relevant functionalities for Data Lake Management are metadata and data modeling. A new age tool for data integration should promise metadata acquisition from different data sources. It should also allow users to establish relationships between different data models. It should help in conducting data model rationalization, sharing metadata, and synchronizing metadata.
Data Migration: Bringing the data from heterogeneous systems gives competitive advantages to companies. A data integration solution should allow users to pivot data between disparate cloud and on-premise applications in simple steps. Metadata and simple reporting features will be essential in this regard.
Interoperability: Besides the bulk processing of data, data integration technology should deliver a combination of Extract, Transform, and Load (ETL) and B2B integration capabilities for Pervasive Integration Needs. The interoperability to create Business Process Management and Enterprise Service Bus (ESB) solves many IT and business challenges for organizations. A modern data integration solution needs to support all data delivery modes in a hybrid IT environment. The solution should ensure reusability features to prevent the workflows from becoming obsolete.
Unified Interface: A single 360-degree view for partner data exchange reduces integration worries. A single source of truth is well positioned to take care of information governance needs. Moreover, the interface should allow users to promote interactions within the user community. The solution should enable users to execute data integration from a centralized platform in a hyperconnected infrastructure. The interface should be built specifically for representing repository objects, developing process flow, defining versions, identifying redundancies, and conducting tests. Organizations should lookout for runtime platform capabilities while evaluating an interface.
Data Connectors and Templates: Customized templates and connectors provide the self-service ability to create flows for integrating different data structures, i.e., databases, XML, packaged applications, SaaS applications, Hadoop Distributed File System (HDFS), etc. Connectors allow systems to be connected in a non-technical way and makes the Electronic Data Interchange (EDI) smooth and fast.
Data Mapping: An advanced integration solution should enable basic to simple data transformations instantly. The tool should support custom transformations for various file formats including legacy and nonrelational databases. Data mapping solutions should solve a slew of data communication challenges including data transformation, data mediation, database consolidation, etc.
Bi-Model and Citizen Integration: Citizen integration with bi-model integration has become the new means with which to measure the worth of data integration tools. It is essential for data integration tools to offer simple yet focused integration capabilities as data is primarily meant for non-technical users. Simple integration controls help normal business users in building integrations so that specialized IT teams can focus on other relevant issues.
Service Support: Another considerable factor for selecting a data integration tool is customer service. The integration solution provider should provide responsive technical support for products. Organizations should check out the response time taken by service team for troubleshooting technicalities.
How fast can you bring in the data stored in disparate systems? This is a question which baffles organizations confronting innumerable data connectivity issues because of complex IT infrastructure. That's why the demand for data integration tools has been intensified. However, before making an investment, organizations must remember that the role of a data integration tool is not limited to data aggregation only. It should enable users to harness data generation over hybrid ecosystems, including multi-tenant, multi-channel, and multi-tier environments. Organizations should ensure that they are selecting a modern data integration tool that also carries B2B accelerators to align the technologies and processes and moving the organizational data towards the stream of business.
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