{"id":18241,"date":"2022-10-27T14:40:32","date_gmt":"2022-10-27T09:10:32","guid":{"rendered":"https:\/\/cigniti.com\/blog\/?p=18241"},"modified":"2022-11-23T15:13:26","modified_gmt":"2022-11-23T09:43:26","slug":"simplify-data-integration-artificial-intelligence-data-insights","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/simplify-data-integration-artificial-intelligence-data-insights\/","title":{"rendered":"Simplify Data Integration with Artificial Intelligence"},"content":{"rendered":"

Data-driven decision-making is fundamental for any business that wants to thrive in today\u2019s cut-throat environment. In fact, there is enough evidence today that proves that data-driven decision-making powered by\u202fartificial intelligence (AI) platforms\u202fcan help businesses expedite their operations, thus saving valuable time and money.<\/p>\n

Such decisions involve leveraging past information to predict the challenges and opportunities that await an enterprise in the future.<\/p>\n

Data integration is the combination of technical and business processes that create useful datasets for business intelligence and analytics from diverse traditional and non-traditional sources of data.<\/p>\n

Data collected from various on-premise and cloud sources is integrated as part of a holistic data integration solution to enable DataOps’ effective, enterprise-ready data pipeline. Data flow from these source systems and the procedure to collect, cleanse, normalize, and store the data for processing are defined by data architecture.<\/p>\n

Hindrances to Effective Data Integration<\/strong><\/p>\n

Integrating data from relational databases, streaming data services, and several other real-time sources have grown increasingly challenging for businesses. In order to extract hidden business insight, well-designed data integration processes guarantee that the data is controlled, governed, and trusted. Attempts at effective data integration may be inhibited by:<\/p>\n