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ADV Slides: 2021 Trends in Enterprise Analytics

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It is a fascinating, explosive time for enterprise analytics.

It is from the position of analytics leadership that the mission will be executed, and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.

The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the third year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

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ADV Slides: 2021 Trends in Enterprise Analytics

  1. 1. 2021 Trends in Enterprise Advanced Analytics Presented by: William McKnight “#1 Global Influencer in Data Warehousing” Onalytica President, McKnight Consulting Group A 2 time Inc. 5000 Company @williammcknight www.mcknightcg.com (214) 514-1444 Second Thursday of Every Month, at 2:00 ET #AdvAnalytics
  2. 2. Dataversity Webcast Vertica and Pure Storage address your variable workloads on-premises with a cloud-optimized architecture Jeff Healey Sr. Director of Vertica Marketing E: jeff.a.healey@vertica.com Miroslav Klivansky Field Solution Evangelist mklivansky@purestorage.com
  3. 3. What is Vertica? SQL Database Load and store data in a data warehouse designed for blazingly fast analytics Query Engine Ask complex analytical questions and get fast answers regardless of where the data resides Vertica is the leading unified analytics warehouse built for the scale and complexity of today’s data- driven world. It combines the power of a high-performance, MPP query engine with advanced analytics and Machine Learning. Analytics & ML Create, train, and deploy advanced analytics and machine learning models at massive scale
  4. 4. Remove scale, performance, and capacity constraints 3 Get data quickly enough to act upon it, explore your data interactively, and enable everyone to make their own data-driven decisions Fear of more users or growing data volumes is a thing of the past Scale Data Volumes Scale Users SQL Database + Vertica Analytics Platform + Get data quickly enough to act upon it, explore your data interactively, and enable everyone to make their own data-driven decisions Analytics & ML Query Engine
  5. 5. Benefits of Vertica in Eon Mode Deliver Vertica with the Cloud Economics Promise Consuming only what you need when you need it Through separation of compute from storage. Scale Infrastructure Linearly. Elastically scale your analytics for workload changes, seasonality, or peak load times. Improved Database Operations. Faster node recovery, superior workload balancing, and more rapid compute provisioning. Isolate Analytic Workloads. Designate specific nodes as a subcluster to isolate workloads and support multi-tenancy. Hibernate. Stop and start analytics more efficiently by hibernating compute nodes when they’re not needed..
  6. 6. Vertica powers the applications and services that enable our data-driven world. A Day in the Life with Vertica
  7. 7. Learn More - Vertica in Eon Mode for Pure Storage Visit www.vertica.com/pure today
  8. 8. BIG FAST SIMPLE
  9. 9. Vertica Eon Mode Requirements Data Safety Performance at Scale + Capacity for Data Growth + Linear Scalability + Tuned for Everything + Easy to Manage + The image part with relationship ID rId9 was not found in the file. The image part with relationship ID rId11 was not found in the file. Separation of Compute from Storage =
  10. 10. FLASHBLADEPURPOSE-BUILT FOR MODERN ANALYTICS BLADE PURITY SCALE-OUT FABRIC Powerful, Elastic Data Processing & Storage Unit Massively Distributed Software for Limitless Scale Software-defined fabric that scales linearly with more data & clients 3
  11. 11. BORN FOR UNSTRUCTURED DATA FLASHBLADE WAS BUILT TO ADDRESS MODERN DATA CHALLENGES 1980 20202005 20152000 20101990 GPS/GIS 1983 NFS 1985 WWW 1989 LDAP, Wikis, Java and IPv6 1995 MP3 1996 Machine Learning recognizes cats 2012 iPhone 2007 Edge computing widely adopted 2018 1st SSD ships 1991 Era of Analytics 2005 Hadoop 2005 S3 2006 bitcoin 2009 Nest Thermostat 2011 Dropbox 2007 AWS 2002 Self Driving Cars 2018 Amazon Echo 2015 Kubernetes 2015 IoT 1999 LinkedIn 2003 First human genome sequence completed 2003
  12. 12. FLASHBLADE CHASSIS: FRONT 4 RACK UNITS, UP TO 15 BLADES © 2016 PURE STORAGE INC. 11
  13. 13. FLASHBLADE CHASSIS: REAR 2 FABRIC MODULES, 4 1600W POWER SUPPLIES © 2016 PURE STORAGE INC. 12
  14. 14. INTEGRATED NETWORKING SOFTWARE-DEFINED NETWORKING 2x BROADCOM TRIDENT-II ETHERNET SWITCH ASICS Collapses three networks – frontend, backend, and control – into one high-performance fabric 8x 40Gb/s QSFP Connections into customer top-of-rack switches 13 FlashBlade Chassis Up to 15 Blades 4RU Height N+2 Redundant, Heals in Place Blades Capacity & Performance DirectFlash NAND Embedded NVRAM
  15. 15. FLASHBLADE BLADE INTEL XEON SYSTEM-ON-A-CHIP Compute + Networking + Chipset Low-Power, Low-Cost Design 8x Full XEON Cores DRAM MEMORY PROGRAMMABLE PROCESSORS FPGA NAND FLASH 17TB or 52TB (per Blade) INTEGRATED NV-RAM Supercapacitor-backed write buffer PCIE CONNECTIVITY CPUs & Flash communicate via custom protocol over PCIe
  16. 16. SOUL OF FLASHBLADE IS PARALLEL POWERING 75 BLADE-SCALE IN SINGLE IP WITH PURITY FOR FLASHBLADE KEY-VALUE DATABASE STORE FOR DISTRIBUTED PARTITIONS KEY VALUE BILLIONS& BILLIONS OF OBJECTS NATIVE OBJECT NATIVE NFS/SMB
  17. 17. MODERNIZE YOUR DATA EXPERIENCE Expedite & automate troubleshooting VM Analytics Plan with ease AI driven workload planner Take the guesswork out of management Cloud based management Global information at your fingertips Pure1 mobile app
  18. 18. Learn More - Vertica in Eon Mode for Pure Storage Visit www.vertica.com/pure today
  19. 19. 2021 Trends in Enterprise Advanced Analytics Presented by: William McKnight “#1 Global Influencer in Data Warehousing” Onalytica President, McKnight Consulting Group A 2 time Inc. 5000 Company @williammcknight www.mcknightcg.com (214) 514-1444 Second Thursday of Every Month, at 2:00 ET #AdvAnalytics
  20. 20. William McKnight President, McKnight Consulting Group • Frequent keynote speaker and trainer internationally • Consulted to many Global 1000 companies • Hundreds of articles, blogs and white papers in publication • Focused on delivering business value and solving business problems utilizing proven, streamlined approaches to information management • Former Database Engineer, Fortune 50 Information Technology executive and Ernst&Young Entrepreneur of Year Finalist • Owner/consultant: 2018 and 2017 Inc. 5000 strategy & implementation consulting firm • 30 years of information management and DBMS experience
  21. 21. McKnight Consulting Group Offerings Strategy Training Strategy  Trusted Advisor  Action Plans  Roadmaps  Tool Selections  Program Management Training  Classes  Workshops Implementation  Data/Data Warehousing/Business Intelligence/Analytics  Master Data Management  Governance/Quality  Big Data Implementation 3
  22. 22. Why Are Trends Important? • It is imperative to see trends that affect your business to know how to respond • Plan for and deal with change • Better to be at the beginning of the trend rather than the end • Wants, needs, and tastes of your customer changes • Make you a leader, not a follower • Grow your business ideas • Give you ideas what to improve in your business
  23. 23. Information Management Leaders • Information Management leaders of tomorrow can advance maturity while also solving business issues – There’s no budget for “staying on trends” • Information Management leaders must pick their winning (i.e., multi-year sustainable) approaches and get on board
  24. 24. The Money Tree Doesn’t Exist Hitch your Trend Pursuit Efforts to a Budget Delivering ROI 6
  25. 25. Those Who Were Less Impacted by 2020 • Cloud-First • Microservices-Based • Data is a separate function • Agile Development • Master Data 7
  26. 26. 2020 Reactions 8
  27. 27. Last Year’s Trends • Data Takes Steps to the Balance Sheet • Explosion in Sensor-Based Time-Series Data • Business Intelligence Interfaces Upheaval • ETL will be Nearly Automated • Cloud Object Storage • More Edge AI • Data’s New Highest Use Will Be Training AI Algorithms • Explainable AI • Kubernetes and Containers • Hybrid Databases 9
  28. 28. Factors to Watch in 2021 • Pandemic Footprint • Vaccine Rollout • Resiliency of Corporations • Prioritization of Forward Factors • Continued Preparedness Awareness 10
  29. 29. Top Trends in Enterprise Analytics for 2021 and Beyond
  30. 30. Remote Work Continues • Some projects done all remote • Or multiple people to 1 seat arrangements • Remote Conferences • Some Offices Prepare for Return
  31. 31. Led by Cloud Capabilities, Strong Tech Spending Rebound in 2021 • CXOs ready to release floodgates • Storage strong growth – AWS Storage Revenue Approaching $10B • Artificial Intelligence, Kubernetes Approaches and Automation are driving corporate tech budgets
  32. 32. Leading Organizations are increasing a focus on AI/ML • Budgets for AI/ML increasing significantly • Beyond Initial Use Cases • Model Expansion in Production 14
  33. 33. Leading Organizations are increasing a focus on AI/ML • Collaborative AI • Human/AI Hybrid Solutions 15
  34. 34. Model Deployment Takes Center Stage • Model Deployment Will Rise to the Top Activity of Data Professionals • Data Scientists Will Continue to Wrangle Data Since Most Data Environments are Mid/Low Maturity • Models Getting More Sophisticated – Data Wrangling Increasing – Continued Challenges to Data Maturity • Organizations will struggle without MLOps 16
  35. 35. • Embedded Databases at the edge • AI baked into the chips • Decision making at the edge More Edge AI
  36. 36. • High-Performance Edge AI • Real-Time Data Wrangling More Edge AI
  37. 37. • Combatting Bias • Responsible AI • Regulations • This trend will evaporate in time Explainable AI
  38. 38. Data Lakes • The Rise of the LakeHouse • Explosion in Sensor-Based Time-Series Data and Edge AI • Leveraging Cloud Storage for Data Lakes • Data Integration Automation 20
  39. 39. New Technology Stacks: Shift from only data warehouses, lakes, and ETL to data fabrics, AI, and pipelines 21
  40. 40. DEVOps • Continuous Delivery • Security in the Pipelines • Visibility into the processes
  41. 41. MLOps • MLOps applies DevOps principles to ML delivery • The ML process primarily revolves around creating, training and deploying models • Once trained and validated, models are deployed into an architecture that can deal with large quantities of (often streamed) data, to enable insights to be derived • Development of such models can benefit from an iterative approach, so the domain can be better understood, and the models improved • It also then needs a highly automated pipeline of tools, repositories to store and keep track of models, code, data lineage and a target environment which can be deployed into at speed • The result is an ML-enabled application: MLOps requires data scientists to work alongside developers, and can therefore be seen as an extension of DevOps to encompass the data and models used for ML 23
  42. 42. • Automated Data Discovery • Auto-generated pipelines based on global experiences • Joins by data • Key variables updated with each new data point • That, in turn, automatically execute the proper next best action • Next best action determined by AI • Enterprises will automate data cataloguing and profiling Automation
  43. 43. • Lack of and expensive data engineers • More vendors rearchitecting to open source • Vendors to compete on customer satisfaction and execution Open Source
  44. 44. • Data analytics stack goes Kubernetes for both open source and commercial • Winners go from thought to POC quickly • Serverlessness Kubernetes
  45. 45. We are at the start of General AI • GPT-3 has opened a new chapter in machine learning. – Its most striking feature is its generality. – Only a few years ago, neural networks were built with functions tuned to a specific task, such as translation or question answering. Datasets were curated to reflect that task. – GPT-3 has no task-specific functions, and it needs no special dataset. It simply utilizes as much text as possible and plays forward its output. • Somehow, in the calculation of the conditional probability distribution across all those gigabytes of text, a function emerges that can produce answers that are competitive on any number of tasks. • It is a breathtaking triumph of simplicity that probably has many years of achievement ahead of it. 27
  46. 46.  There’s more maturity in moving imperfectly than in merely perfectly defining the shortcomings  Build credibility  Don’t be afraid to fail  Don’t talk yourself out of having a new beginning Have an open mind No plateaus are comfortable for long That resistance is not about making progress, it’s the journey
  47. 47. Winning Approaches in 2021 • Cloud Computing • Artificial Intelligence • Data Lakes • Data Warehousing • Master Data Management • Agile Development • Kubernetes • Automation • Data Quality • Graph Data • Organizational Change Management • DevOps and MLOps • Data Catalogs • Data Governance
  48. 48. 2021 Trends in Enterprise Advanced Analytics Presented by: William McKnight “#1 Global Influencer in Data Warehousing” Onalytica President, McKnight Consulting Group A 2 Time Inc. 5000 Company @williammcknight www.mcknightcg.com (214) 514-1444 Second Thursday of Every Month, at 2:00 ET

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