Data integration is a process of combining disparate data from disparate sources into a unified format. It’s about properly managing data and using it for driving value. In essence, data integration generates a single, unified view of data to enhance collaboration, drive data exchange, and improve data-driven decision-making. So, it’s your data but smarter.
Simply put, data integration is indispensable for driving organizational objectives. Here are seven reasons why.
Every data format is designed for a particular reason. Each of the data formats illustrate information in a way that no other format can, with one of a kind attributes, structure, schema, metadata, and much more. Integrating data from myriad formats helps users capitalize on the value of each with ease and speed.
As the number of applications increases, the number of interfaces increases. Navigating through this web will only get user tangled. With the aid of a data integration platform, organizations can enter that web with a machete. Data integration solutions manage complexity, streamline connections, and simplify data management to help organizations gain a competitive edge.
When disparate data sets are brought together, the value of information is naturally increased. The value can be increased by:
4. Data collaboration becomes easy
With centralization and accessibility come easier collaboration. Whether collaboration involves data sharing or exchange among internal teams and applications or across business ecosystems, data integration allows users eliminate data silos, manage big data better, and drive decision-making.
By integrating data, users can manage their business processes and workflows faster. This helps companies drive value and become easier to do business with. Giving people the flexibility to leverage data, wherever and whenever they want, helps them gain a better understanding of data to extract actionable insights, comprehend buyers’ personas, and improve responsiveness to their changing demands.
Data integration increases accuracy, completeness, and consistency of data by automating data transformation processes using digital transformative technologies such as AI. As the data is onboarded, cleansed, mapped, transformed faster (keeping authenticity intact), it becomes free from errors, duplicities, inconsistencies, etc. This increases the integrity and quality of data, which can be used to drive value.
Data integration also has a direct impact on the quality and speed of data onboarding. Users can onboard complex customer data feeds in minutes instead of months using self-service integration technology. When data is onboarded faster, it can be used to analyze customer personas and meet their changing demands without a hitch. Powerful experiences can be delivered which, in turn, fuels loyalty and ultimately companies’ sales and revenue.
In short, employing a robust data solution allows users maximize the value of data garnered from different data sources for improved business intelligence and decision-making.