How to modernize your data platform to accelerate analysis and queries

Learn how augmented analytics leverages machine learning (ML) and augmented intelligence (AI) to provide business users with faster answers to the “why”. Agenda ○ Automates insights and enables natural language query ○ Provides augmented intelligence through machine learning and predictive analytics ○ Translates data from an abstract algorithm to a consumable visualization for the business ○ Can be obtained in AWS Marketplace with solutions such as Qlik, Sisense, and ThoughtSpot Chief Ambassador, DevOps Institute Helen Beal Partner Solutions Architect, AWS Chris Chapman

How to Evolve Your Business Intelligence with Augmented Analytics Helen Beal

PLEASE LEAVE THIS SPACE FOR YOUR GRAPHIC ARTIST RECORDING YOUR LOGO HERE Human Chief Ambassador: DevOps Institute DevOps Editor: InfoQ Ambassador: CD Foundation Analyst: Accelerated Strategies WoW coach, speaker, learning facilitator, writer Strategic advisor Geek, wordsmith, Bananagrammer Volunteer warden at Kingley Vale Once saw a flamingo lay an egg Can dig an Olive Ridley turtle nest Mission: Bringing joy to work

Evolution of DevOps 2009 2019 2021 DevOps: Born in the Cloud and Coming to the Enterprise Augment DevOps with NoOps ARA is a Key to DevOps Value Stream Management Wave

Daily Data Production (2020) Icons by Freepik and Eucalyp from Flaticon 1.7 MB per person per second 90% of the world’s data created in last 2 years 95 million photos and videos are shared every day 306.4 billion emails sent every day Digital universe = 44 zettabytes end of 2020

How Big is Data? Abbreviation Unit Value Size (in bytes) b bit 0 or 1 ⅛ of a byte B byte 8 bits 1 byte KB kilobyte 1,000 bytes 1000 bytes MB megabyte 1,0002 bytes 1,000,000 bytes GB gigabyte 1,0003 bytes 1,000,000,000 bytes TB terabyte 1,0004 bytes 1,000,000,000,000 bytes PB petabyte 1,0005 bytes 1,000,000,000,000,000 bytes EB exabyte 1,0006 bytes 1,000,000,000,000,000,000 bytes ZB zettabyte 1,0007 bytes 1,000,000,000,000,000,000,000 bytes YB yottabyte 1,0008 bytes 1,000,000,000,000,000,000,000,000 bytes

The Increasing Importance of Data Icons by Freepik from Flaticon Outcomes Data Insights

From DevOps… to BizDevTestSecDataOps Icons by Freepik, Catkuro, Smashicons, Becris from Flaticon Development Operations DevOps Team DBA/DevOps Evangelist “The Business” Data Scientist

Definitions of DataOps 1. It’s about databases in the DevOps toolchain. We need to ensure that the database is part of the CICD pipeline and that any updates are correctly applied, rollbacks and redeploys take state changes into account and data is fully tested before deployment. 2. It’s about using DevOps principles to extract data insights. We need to ensure that the people involved in getting data analytics to the people that need it can do it fast, at high quality and securely - so no risk to our governance and compliance requirements. Database DevOps DataOps

“DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting and recognizes the interconnected nature of the data analytics team and information technology operations.”

Cross-Skilling

Leveraging DevOps Practices The Good Practice Applied in the World of Data Incremental , continuous change Data needs to be mined and business intelligence analyzed at speed and with adaptability too. Systems from backlog to deployment must handle data needs. CICD & DevOps Toolchains Teams working with data need to leverage the power of automation to maximise throughput and stability and provide CICD capabilities and limited blast radius. The Three Ways We want to accelerate flow, amplify feedback and use our data to drive experiments too. Monitoring and observability are key with AI for feedback. A high-trust, collaborative culture In order to build trust in a DevOps culture we have data-driven, not opinion driven conversations. Data must be available real-time, on demand and via self-service. Value stream centric working Truly understanding flow, means all people in the value stream have a profound understanding of the end-to-end system and this is driven by data insights. “We build it, we own it.” Teams must be multifunctional, cross-skilling must be standard practice, it must be quick and easy to get results from tools - choose those designed with usability. Focus on value outcomes Insights lead to decisions lead to measurement experience improvements for the customer: AI accelerates mean time to outcome (MTTO).

Icons by Smashicons, Freepic, Dimitry Miroliubov, Eucalyp from Flaticon Visual Analytics Self-Service MashUps and APIs Natural Exploration BI in the Cloud Create Interactive Dashboards SAAS/PAAS AI

Augmented Analytics: User Experience A DevOps team quickly builds a high-quality, device-friendly app on a cloud-based platform designed with developer and consumer usability in mind and makes it available via self-service. Icons by Smashicons, Freepic, Dimitry Miroliubov, Eucalyp from Flaticon Deep exploration Personalized Insights Real-time Queries Transparency The business doesn’t need to be data engineers or scientists to search using natural language for the answers they need and gain the insights that will enable them to make intelligent, data-driven business decisions. Context Anomaly Detection Causal Relationships Trend Isolation Noise Reduction Segmentation

Toil Reduction Icons by Surang and Smashicons from Flaticon Drowning in data Swimming in innovation ? AI provides answers to the questions you didn’t know to ask.

Thank you and stay human. Helen Beal Chief Ambassador helen@devopsinstitute.com @BealHelen helenjbeal