Some key challenges faced by CXOs & Enterprise leaders are –
- Predict and prevent service outages
- Predict and prevent security breaches
- Offer self-service Analytics and automate appropriate parts of IT
A pivotal inhibitor to mitigate these challenges is the Data Value Gap.
Data automation and Data Fabric are emerging as key technologies to overcome these challenges. Learn from industry experts about these key technologies and how they create a lasting impact in enterprise IT.
A Globally Trusted Panel
Sean McDermott, CEO of Windward Consulting Group
Sean McDermott is a serial entrepreneur with 35+ years of experience in IT. A recognized voice in IT Operations Management, IT Service Management and AIOps, Sean is a member of the Forbes Tech Council and can be seen on Entrepreneur, InformationWeek, TechTarget and DevPro Journal.
Joe McKendrick, Principal, The Field CTO
Joe McKendrick is an author, an independent researcher and speaker exploring innovation, information technology trends and markets. Joe is a contributor and analyst for Forbes, CBS Interactive, Information Today, Inc. and RTInsights, among many others and was listed as one of 10 “key opinion leaders” in “Who’s Who in Digital Experience,” Onalytica, February 2021.
Jen Stirrup, CEO & Founder, Data Relish
Jennifer Stirrup is a #1 best-selling Amazon author and a recognized leading authority in AI and Business Intelligence Leadership, a Fortune 100 global speaker and has been named as one of the Top 50 Global Data Visionaries and one of the Top Data Scientists to follow on Twitter, and one of the most influential Top 50 Women in Technology worldwide.
The value proposition of AIOps post-COVID for enterprises:
- Digital Transformation
- Predictability
- Cost Efficiencies
- Decision-making
Data is to AIOps what Location is to Real Estate. How do you harness the Data?
Top Data Management Challenges that keep companies stuck:
- Source of Data
- Access to Data
- Data Accuracy
- Talent Gap
Your Preferred Approach to the IT Data Problem
- Understand the tangible and intangible use case that drives your AIOps strategy
- Develop a vision to garner organizational support and buy-in
- Explore Data Pipeline Automation for quicker deployment
- Leverage No/Low Code solutions to address the skills gap
Have we Finally Seen the Arrival of the Autonomous Enterprise?
How AI can be supercharged to bring the vision of the Autonomous Enterprise to reality?
Intelligent Data Supply Chain – How it can make your job easier and more rewarding?
Every enterprise wants to become digital and is looking to IT to deliver on that.
Why IT is under pressure
- Service outages
- Security breaches
- Capacity issues
IT is expected to provide
- Support for IoT and Operational Technology (OT)
- Self-service options
- Automation, org-wide
- Superior customer experience
- Superior user experience
The Problem with Data that Goes into AIOps
- Silos and more silos
- Data from disparate sources
- IT Ops Data is messy
- Many Data formats
- Diverse Data platforms
- Low-quality Data
- Lack of Data management skills
The Data Value Gap that Goes With AIOps
- Time spent loading and scrubbing Data
- Scarce Data-quality efforts
- Manual tasks compromise competitiveness
- Low trust in own Data
An Intelligent Data Supply Chain is Critical
Only 16% of enterprises have them. “Data supply chains are a better way to source high-quality data. They build on the process and supplier management techniques.” – Tom Davenport.
Robotic Data Automation Fabric- The Solution
- Automate Data Integration and preparation activities involved in dealing with Machine Data for Analytics and AI applications.
- Faster time-to-market
- Quick time-to-ROI and time-to-insights
- Reduce costs
- Scale easily as an enterprise grows
- Boost DevOps and ML pipeline observability
- Use easy-to-build bots
Essential Components of the Intelligent Data Supply Chain
- Data Intelligence
- Data Automation
- Data Integration
Unraveling the Buzzwords: Build a Good Foundation for Analytical Success
Learn clearly about the famous buzzwords in the realm of AIOps and technology. Move away from a hyped-up version of these technologies and learn about their real significance to organizations and enterprises today.
- Data Fabric
- Data Mesh
- Data Lake and Data Puddles
- RDA and Last-mile problem
- The First-mile problem of AI
- AIOps is a DataOps problem
