Data integration challenges will hinder the enterprise’s dream to become a data-driven business. Overcoming these challenges is crucial for organizations to ensure a scalable business.
Data integration is one of the most crucial aspects that modern enterprises need to manage efficiently. But many enterprises find it a difficult task to have a successful data integration strategy. Enterprises need to have an efficient data integration process in place to increase work efficiency.
A recent report released in 2022 by Celigo commissioned by Forrester titled “Simplify SaaS With iPaaS” suggests that nearly 74% of the respondents state that modernizing data warehouses is one of their top priorities for the next 12 months. The reports also suggest that approximately 67% of the respondents that do not have iPaaS implemented lack data visibility in their existing SaaS landscapes.
An effective data integration strategy focuses on gathering and processing data from various sources and streamlining data flow between the team. Here are a few ways to overcome data integration challenges
Ingesting data from multiple sources
One of the most significant challenges that businesses face is centralizing the data which is present in all formats, sources, environments, and locations. These multiple sources might have very few to zero integrations between them. Moreover, enterprises also have to look after cleaning data and removing the duplicates because it is gathered from multiple sources.
CIOs should consider evaluating how the analyst utilizes a data set and how often they will require to access it to have a seamless data flow. It is also crucial to understand how the end-user operate is a key to developing a robust data infrastructure and integration strategy. Implementing an Integration Platform as a Service (iPaaS) is an effective way to streamline data integration.
Manual processes to integrate data
A few organizations still integrate data manually, wherein the data scientists spend most of their time searching and preparing data. CIOs might want to consider evaluating tools that will assist them in ingesting and integrating data automatically. Monitoring data lineage will assist enterprises in ingesting raw data and executing minimum transformations. For instance, mapping source data through a centralized schema will help to trace the issues in the common data sets back to their roots in the raw source data.
Design and implement processes for data management
Data integration is an intricate and continuous process that needs a thorough evaluation before implementing standards throughout the enterprise. A few organizations find it challenging to design a strategic plan to integrate data from various third-party sources.
CIO should consider assigning data ownership before implementing an organization-wide standard for data entry and maintenance. Appointing one team or resource with a responsibility to maintain the quality of data and manage it will ensure a seamless workflow. They will be responsible for checking that whatever enters the data warehouse is according to the company’s compliance and strategy. If this strategy does not work for the enterprise, it is crucial to train the entire workforce on how to input and update data will enhance the data integration process. Designing and implementing organization-wide protocols for data entry and management will help enterprises to minimize low-quality, outdated, and duplicated data.