Data downtime refers to periods of time when data is missing, erroneous, or otherwise inaccurate, and data teams spend upwards of 40 percent of their time tackling it instead of working on revenue-driving projects.Īs organizations increasingly rely on data to drive decision-making and power digital products, the need for this data being ingested, stored, processed, analyzed, and transformed to be trustworthy and reliable has never been higher. We call this phenomenon data downtime, and it’s becoming increasingly common for even the most robust and well-staffed data teams. If this situation sounds familiar to you, know you’re not alone. As all of this is going on, you get texted by John in finance about an errant table in his spreadsheet, and Eleanor in Operations about a query that pulled “interesting” results. Other team members across the organization are repeating efforts, and your CMO is left in the dark while no updates are being sent out to the rest of the organization. You wake up to messages from your CMO saying, “The numbers in this report don’t seem right…again.” You drop what you’re doing and begin to troubleshoot the issue at hand. It’s Wednesday morning, and your phone won’t stop buzzing. In this submission, Monte Carlo Data CTO and Co-Founder Lior Gavish provide expert advice for evaluating data observability software. This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories.
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