{"id":26979,"date":"2021-08-28T06:00:00","date_gmt":"2021-08-28T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26979"},"modified":"2021-08-25T10:21:21","modified_gmt":"2021-08-25T17:21:21","slug":"data-warehouse-costs-soar-roi-still-not-realized","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/08\/28\/data-warehouse-costs-soar-roi-still-not-realized\/","title":{"rendered":"Data Warehouse Costs Soar, ROI Still Not Realized"},"content":{"rendered":"\n

Enterprises are pouring money into data management software \u2013 to the tune of $73 billion in 2020<\/a> \u2013 but are seeing very little return on their data investments.\u00a0 According to a new study out from Dremio<\/a>, the SQL Lakehouse company, and produced by Wakefield Research, only 22% of the data leaders surveyed have fully realized ROI in the past two years, with most data leaders (56%) having no consistent way of measuring it.\u00a0<\/p>\n\n\n\n

In addition to the survey findings, the report also introduces the Data Value Scorecard,  a measure of companies\u2019 efforts to unlock the value or ROI of data. Through a series of metrics that assesses their data policies, the Scorecard provides a quick, aggregated view of how efficiently companies and their employees use and manage data.<\/p>\n\n\n\n

“Data leaders are frequently concerned about the out-of-control costs of data warehouses, particularly as their workloads grow \u2013\u00a0 a big surprise to them since the data warehouse cost of entry can be low to start. Moreover, the lack of predictability around future costs leads to very poor financial governance for data leaders and CFOs alike,” said Billy Bosworth, CEO, Dremio. “While companies understand they have an expensive sunk cost for data warehouses, they are keen on ways to drive newer analytics architectures directly from open data lakes for better financial governance and highly performant queries against vast amounts of data.”\u00a0<\/p><\/blockquote>\n\n\n\n

The report found that in order to run analytics, enterprises are making multiple copies of their data \u2013 12 copies on average. A staggering 60% report that their company has over 10 copies of their data floating around.\u00a0<\/p>\n\n\n\n

To add insult to injury, 82% say that their end users have used inconsistent versions of the same dataset at the same time due to their extract transform and load (ETL) processes, undermining the data integrity & trust, and slowing down the decision-making process. Notably:<\/p>\n\n\n\n