{"id":24965,"date":"2020-09-08T06:00:00","date_gmt":"2020-09-08T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24965"},"modified":"2020-09-09T09:24:11","modified_gmt":"2020-09-09T16:24:11","slug":"the-state-of-data-management-why-data-warehouse-projects-fail","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/09\/08\/the-state-of-data-management-why-data-warehouse-projects-fail\/","title":{"rendered":"The State of Data Management – Why Data Warehouse Projects Fail"},"content":{"rendered":"\n
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Data is the fuel of our modern world, and its increased proliferation within organizations means that proper data management has never been more critical to success.<\/p>\n\n\n\n

More than ever, organizations are investing in data warehouses and data lakes to help them  make the most of their valuable data assets and deliver on the promise of agile analytics and  actionable business insights. However, the process of identifying and moving data into a data  warehouse or data lake is not always straightforward, all too often inhibiting progress and success.<\/p>\n\n\n\n

Based on new research, “The State of Data Management – Why Data Warehouse Projects Fail<\/a>” commissioned by SnapLogic and conducted by Vanson Bourne, who  surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and  UK, this whitepaper explores the data management challenges organizations are facing, the  vital role data warehouses play, and the road to success.<\/p>\n\n\n\n

Organizations are increasingly investing in cloud data warehouses to help them make the most of their valuable data assets. However, according to this new research, 83% of organizations we surveyed are not fully satisfied with their data warehouse performance and output. Respondents cited app and data silos, outdated legacy tech, complex data types\/formats, and slow data movement\/access issues as reasons for their dissatisfaction.<\/p>\n\n\n\n

Other notable stats:<\/p>\n\n\n\n