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2022-02-08

Rethink Your Strategy to Overcome Major Data Integration Challenges

Rethink Your Strategy to Overcome Major Data Integration Challenges

Every business has developed the habit of collecting complex data streams that are generated from their business, say, warehouse status, transactional, social media, etc. These collected data streams may range from multiple formats and structures but the end-objective of businesses is to integrate these data streams for delivering insights and ultimately driving value. This unified view enables business users to comprehend customer needs and make decisions accordingly. Data integration has a major role to play here. 

Data integration enables business users to create data connections and integrate new customers – at speed and scale. And by doing so, users can garner insights and use the insightful information to make informed decisions in business. 

As much as businesses understand the significance of data integration, many are unable to implement it. Here are three major challenges:

Data is in multiple formats and sources: The data collected from customers can have different formats, different kinds. This dissimilarity in data formats is due to the emergence of schema-less data management. This schema-less approach has created uncertainty of data when it comes to management. 

Along with uncertainty, there is another problem when it comes to data integration. The data generated by companies are extracted from multiple departments or data handling systems. The point is that these systems might not be handling data in the same format, but they could be different from one another. Every database garners data in multiple formats, say structured, semi-structured, and unstructured. Integrating these data streams is a complex process when companies do not have a robust ETL solution in place. 

Data is voluminous: Data integration is a time-consuming process, especially when the process involves data from various formats. However, it is not the only obstacle, the volume of the data plays a big role in the time invested for integration. Companies that rely on legacy solutions will take a lot of time and effort to integrate large volumes of data. IT integrators must write long hours of code to integrate voluminous data that is time-intensive and error-prone. 

The quality of data: Invalid or incompatible data is a big problem faced by companies today. Businesses might not be aware of it, but the analytics obtained from data would mislead businesses as analytics are evaluated to make business decisions. Replication of data is another major problem as it interferes with the insights delivery process and ultimately decision-making.

Companies need to reimagine their data integration approach to overcome the challenges and create data connections as well as add new customers – at speed and scale. With features such as pre-built application connectors, shared templates, dashboards and intuitive screens, AI-data mapping, etc., even non-technical business users can integrate complex, bi-directional data streams with ease and speed. Users can create onboarding connections by pointing and clicking through easy screens. With advances in AI and ML, users can map voluminous data in a precise manner. The mapped and transformed data can be quickly integrated for multiple business purposes.