{"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 Businesses must start thinking about data as a corporate asset rather than a by-product of the business to start avoiding some of these hindrances. Beyond design considerations, using cutting-edge technologies like AI has been successful in reducing the number of large data integration challenges.<\/p>\n How AI Plays a Key Role in Data Integration<\/strong><\/p>\n According to the Harvard Business Review, over the next ten years, AI will contribute $13 trillion to the global economy, and businesses that succeed at deploying AI across the enterprise would find themselves at a considerable advantage.<\/p>\n AI and machine learning (ML) technologies have been demonstrated to increase overall data integration project results in addition to significantly reduce the heavy lifting frequently involved with data integration initiatives. AI in the data integration platform help change the way businesses make decisions in the following ways:<\/p>\n Closing Lines<\/strong><\/p>\n AI-enhanced data integration is progressively automating the development of data pipelines and organization-wide application flow. Data integration tools now have access to large volumes of varied data thanks to big data storage (HDFS\/ Hive\/ Cloud storage). The big data storage enables its embedded recommendation engine to intuitively deduce the data structure components from this and use the same for automating the repetitive and redundant data integration tasks.<\/p>\n To meet the rising demand for data integration pipelines, the AI engine is gradually enhancing its inferred and tagging analytical logic, metadata discovery framework, and learned knowledge base.<\/p>\n Cigniti helps global enterprises to manage various forms of data from their applications, systems, databases, data warehouses, digital data, or even offline data. We provide a single process for integrating all the data into a single hub to prevent data silos and assure that the appropriate information is made available to the appropriate user. With the help of our data integration services, you can maximize the value of your data, improving both operational effectiveness and customer satisfaction.<\/p>\n\n
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