An Expert Guide: Selecting the Right Data Integration Tool for Your Business
Technological innovations have altered the integration landscape as well and getting a consistent access to data has become intensely challenging. As a result, there is a sudden surge in the demand of 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 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, a right integration tool should scale to covers the following technology integration needs:
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Business Intelligence and Data Warehousing: Analytics is not an afterthought anymore. An Advanced integration tool should provide functionalities to 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 modelling 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 criteria for selecting an integration technology is 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 interenterprise 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: Data lake is one of the new emerging needs. Organizations are solving innumerable business problems with a managed data lake. Modern data integration should pack controls for pulling, cleansing, and monitoring raw data. More importantly, there shall be controls for orchestrating and scheduling workflows.
Two relevant functionalities for Data Lake Management are metadata and data modelling. A new age tool for data integration should promise metadata acquisition from different data sources. It should also allow users in establishing 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 advantage 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 quintessential in this regard.
Interoperability: Besides bulk processing of data, the 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 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 and 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 & 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. The Data mapping solution 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 mean to measure the worth of data integration tools. It is quintessential for data integration tools to offer simple yet focussed 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.
Also Know the most common cost overheads associated with data integration
Many organizations face several old problems even after squandering a lot of money on technology integration. Costs expand when the integration approach lacks scalability to accommodate new technology changes. Frequent disruptions cause loss of valuable business opportunities. Know some related cost overheads associated with data integration.
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 checkout the response time taken by service team for troubleshooting technicalities.
How fast can you bring the data stored in disparate systems of clients and partners? This is the question which baffles entities confronting innumerable data connectivity issues because of complex IT infrastructure. That's why, the demand for data integration tools has been intensified in the recent times. 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 in harnessing data generating over hybrid ecosystems, including multi-tenant, multi channel and multi-tier environment. Organizations should ensure that they are selecting a modern data integration tool that also carries B2B accelerators to align the technologies & processes and moving the organizational data towards stream of business.
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