Executive Briefing: Introducing data downtime—From firefighting to winning
Who is this presentation for?Data scientists or analysts
Have you ever shown a report to your manager or CEO and tell you the numbers look way off or had a customer call out incorrect data in one of your product dashboards? Barr Moses defines data downtime—periods of time when your data is partial, erroneous, missing, or otherwise inaccurate.
Data downtime is highly costly for data-driven organizations and affects almost every data team, yet is often addressed ad hoc in a reactive manner. You’ll explore why data downtime matters to the data industry, signs you may have a data downtime problem, and how to address it proactively in your company.
Specifically, you’ll explain the pain, data downtime, and the data reliability maturity curve. Unreliable data is more prevalent than we admit; every data professional has a data horror story. Current solutions take heroic efforts from both engineering and data teams. Barr’s learnings draw on research spanning more than 80 data teams and studies of how they manage unreliable data.
Barr leads a deep dive into specific signs to identify if you or your organization is experiencing data downtime. You’ll see how data downtime is analogous to application downtime—and it will only increase in significance as data becomes more important for organizations—and therefore, it demands the same level of diligence.
There are four key milestones in the journey that data teams go through to manage data reliability: reactive, proactive, automated, and scalable. For each step in the journey, you’ll examine specific tactical examples for what teams do and what actions they take to progress along the curve.
Despite great efforts of many teams, data downtime persists in many organizations, and there are various unsolved problems in this space. If you do, the reward is meaningful. Barr outlines the quantifiable value organizations gain as they proactively address data downtime.
- Experience with data-driven products or decision making
What you'll learn
- Learn about data downtime and what it means for your teams
- Understand specific best practices, homegrown solutions, and open source tools that you can use to manage data downtime
- Identify where your organization is in the data reliability maturity curve and tactics to move up in the journey
Barr Moses is CEO and Co-Founder of Monte Carlo. Previously, she was VP at Gainsight (an enterprise customer data platform) where she helped scale the company 10x in revenue and worked with hundreds of clients on delivering reliable data, a management consultant at Bain & Company, and a research assistant at the Statistics Department at Stanford University. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a BSc in mathematical and computational science.
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