Most organizations aren’t able to obtain any valuable insights from their growing volumes of data. This is according to a new survey from Matillion and IDG, “Optimizing Business Analytics by Transforming Data in the Cloud.”
According to the survey, data volumes are growing at an average of 63% per month, with 12% of organizations reporting over 100% percent growth every month. According to a survey by IDC, in 2018 alone, storage suppliers added more than 700 exabytes of storage capacity to keep up with growing data volumes.
They also found that almost all organizations plan on utilizing the cloud for data management in some capacity in the next 24 months. Ninety percent of respondents have already placed some data in cloud data warehouses (CDWs).
But despite the growing interest in CDWs, their adoption alone doesn’t address all of the needs of data analytics. Over 90% of participants responded that it is a challenge to make data available in a format that is useful for analytics. Obstacles standing in the way of the data analytics projects include a lack of necessary data granularity, manual coding of data pipelines, and difficulty connecting with multiple data sources.
The report also claimed that another area where organizations are struggling is in transforming data. Over one-third of respondents said they manually code their data into the necessary format before they load it into business intelligence and analytics tools. Only 28% actually load the data directly to the cloud and have the cloud handle the transformation process.
“The massive amounts of digital data from thousands of potential sources can give companies critical information and insights into their operations, markets, and other areas when properly analyzed,” said Matthew Scullion, CEO of Matillion. “However, most organizations are not efficiently preparing their data, which makes the process costly and ineffective. Our research underscores both the demand and the urgency to utilize the flexibility and scalability of the cloud, not just to store data, but also to automate and accelerate data transformation.”
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