The average number of SaaS applications organizations use has increased rapidly in recent years. It is estimated that large organizations with over 1000 employees use an average of 177 SaaS applications. It’s becoming increasingly difficult to integrate data from multiple data sources and then use it for analytics, customer-facing applications, or internal workflows. Pulling these insights from large volumes of data requires seamless data integration, either manually or supported by automation tools.
Critical Capabilities of an Effective Data Integration Tool
Data Engineering
Building, managing, and operationalizing data pipelines to support various data and analytics demands requires defined architectural patterns, tools, and methodologies. These may include extract, transform, load (ETL), extract, load, transform (ELT) or change data capture (CDC).
Cloud Data Integration
It involves migrating and modernizing data workloads in the public cloud with an architecture that spans on-premises and one or more cloud ecosystems (hybrid/multi-cloud), enabling optimal use of cloud resources.
Operational Data Integration
It supports transactional data integration use cases such as master data management (MDM), data acquisition and sharing, B2B data sharing, data synchronization, and data governance. This also includes integrating, consolidating, and synchronizing data related to critical business processes.
Data Fabric
Data integration capabilities are delivered in support of use cases related to the emerging data fabric design. This includes enabling faster access to trusted data across different environments by utilizing active metadata, semantics, and machine learning (ML) capabilities.
Different Approaches to Data Integration
Data integration strategies and approaches have evolved from manual to automated solutions. Data integration tools today have advanced features, making it easy to consolidate and integrate data using any of these approaches depending on your business needs.
Data Warehousing
A data warehouse is a centralized solution to store and organize your data, enabling quick and easy access. Data warehouses are excellent for storing large volumes of data as they are scalable and affordable. You can connect and load data seamlessly, speeding up queries and maintaining control of raw data even during the synchronization process. You can also create an analytics solution in-house by combining a data warehouse, a data pipe, and a data visualization solution on the top.
But, building an integrated analytics solution in-house requires tremendous effort and would still need automated real-time analytics and granular reporting capabilities. It would also require more work for all your employees to be able to seamlessly access and analyze their data in an intuitive interface. While data warehouses are great for storing large volumes of information, you’ll need additional layers to leverage this information.
API Connectors
Processes like extract, transform, and load (ETL) can help you access information within data warehouses by running queries, transforming them into the right formats, and loading them into other analytics tools. You can make this process more seamless by leveraging APIs to connect the data between your applications and equipment.
APIs allow you to send information to and from applications or devices using customizable triggers and webhooks. They are used to solve minor connectivity challenges between specific applications. But, they take a band-aid approach to data integration and are designed to solve small pieces of the data integration puzzle.
Analytics Platforms
While these solutions may promise to answer all your data integration problems, they still need a crucial element. But, to truly combine the different data types, integrate your data, and see how one source impacts another, you need an analytics solution. Choosing the right analytics solution can be a challenging task. Most solutions share several common features and offer some form of data and application integration. But, a truly integrated and unified analytics solution should have the following capabilities:
- Real-time data consolidation allows you to combine fragmented data and visualize every customer interaction.
- Collaborative reporting allows you to share and access data seamlessly, enabling all your employees to make data-driven decisions.
- Actionable insights can send real-time alerts and trigger automatic workflows based on customer behavior.
End-to-end analytics solutions are designed to consolidate all siloed data sources within a single solution. You can empower every employee with integrated, real-time data to visualize how their actions impact the customer experience and make data-driven decisions in real time. It helps you take informed steps to optimize and personalize your customer’s journey, creating a seamless and cohesive customer experience.
Conclusion
Whatever data integration tool or approach you choose, you must first consider the above-mentioned factors, understand your unique business needs, existing technology landscape, and use case and make data integration a priority. An effective data integration service provider can host, manage, and administer your integration platform for smooth business operations.
