The Risks and Rewards of AI

Editor's Notes

• #5: Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage. 
• #6: Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage. 
• #7: Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage. 
• #9: Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage. 
• #10: Alex and Siyka to challenge this assumption live to keep it entertaining and establish them as experts
• #11: Bring the Citizen Data Scientist concept from Gartner here, challenging this slide
• #26: By applying machine learning to our business problems we can augment and amplify the strengths that we have as humans, allowing us to: -detect what is not visible to the human eye: AI that provides automated, real-time detection can uncover important insights in any data set, versus manual, time-intensive processes in which you could still miss the aberrations or outliers that are subtle, but could still be consequential. -predict future outcomes: a major and ongoing challenge for business is dealing with unexpected circumstances. while humans are able to do forecasting and make educated guesses, computer can compute and analyze data, identifying patterns and predicting outcomes faster than any human. -reduce noise: With all the data that’s generated, comes a lot of noise which makes it difficult for humans to know what is important for them to focus on. With AI it’s possible to classify data points into specific groups to get insights.
• #31: Key Takeaways -We take a different approach and allow you to turn data into doing -We allow you to bring data from the connected world and drive business outcomes faster than ever before Talk Track At Splunk our approach is different. We allow you to ingest data from all kinds of different sources: Be it systems, devices or interactions, and turn that data into meaningful business outcomes across your organization. That’s the power of Splunk. Let’s let’s take a quick look at how… ​ 
• #35: 34
• #36: Before Splunk Failure detection — Customer often informs IT Incident Triage — All hands on deck, taking up 30 to 40 minutes Incident Troubleshooting — Lengthy log analysis done manual Service Restoration — Fix is implemented Root Cause Analysis — Up to 30% unknown root causes, causing incidents to recur With Splunk Better detection customer is notified by IT Faster triage often conducted by 1st level staff without all hands on deck Faster investigation (MTTI) through rapid log search and correlation conducted in conjunction by different teams (everyone looks at the same data) Faster and more comprehensive root cause analysis reduces incident recurrence
• #38: Today, approximately 20 management systems, from Microsoft System Center Configuration Manager (SCCM) to SolarWinds network management tools, more than 4,500 configuration items (CIs) across 120 IT services and 240 locations worldwide, feed into Splunk ITSI at Leidos, helping the company boil 3,500 to 5,000 daily alerts down to roughly 50 tickets for network and datacenter operations to act on. Passing CMDB information into Splunk ITSI allows different alert displays for different staff.
• #39: Siyka to present TransUnion is a big data and Informations solution company founded in 1968 4.8 Billion data updates each month 30+ countries served 90 000 data sources 50+ PB of information
• #41: 40