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ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced Analytics

  1. What Happened of Note in 1H 2020 in Enterprise Advanced Analytics Presented by: William McKnight President, McKnight Consulting Group williammcknight (214) 514-1444 #AdvAnalytics
  2. William McKnight President, McKnight Consulting Group • Frequent keynote speaker and trainer internationally • Consulted to Pfizer, Scotiabank, Fidelity, TD Ameritrade, Teva Pharmaceuticals, Verizon, and many other 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 2
  3. 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
  4. COVID-19 4
  5. Maslow’s Hierarchy of Needs 5
  6. COVID-19 • Impacted worldwide operations • Sudden, accelerated disruption • No discontinuity event like this • Impacts customer operations • Impacts health & wellbeing • Need to step back and plan 6
  7. WFH Pros/Cons • Lost personal touch • Some feel more connected; meeting family, pets • Life has slowed down, less “I don’t have time” – Doing things never had time to do before: upgrades, maintenance, processes, documentation, learning • Working earlier, later • Virtual hiring • Remote conferences 7
  8. COVID-19 impacts Data Protection • Security concerns: people working in houses • High-speed access issues • Zoom…. Was great, then security concern • Sharing confidential info • Reconsidering tooling, balancing familiarity with security 8
  9. Preparing Offices for Return • Different geographical situations • Social distancing parameters • Limited population, desks, stockpile sanitizer, cleaning • BUT distance is working. Surprise! Some % will stay offsite – Or multiple people to 1 seat arrangements – Some projects done all remote 9
  10. Keeping Focus • Keep focus on how do the customers respond? • Remove pressure from salesforces • Help customers survive • Resilient companies will come out ahead 10
  11. Those Who Are Less Impacted • Cloud-First • Microservices-Based • Data is a separate function • Agile Development • Master Data 11
  12. Specific Events 12
  13. Consortiums • The COVID-19 High Performance Computing Consortium – Bringing together the Federal government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research. • Open Community • 30+ Members • 400+ Petaflops • 100k+ Nodes • 50+ Projects
  14. Healthcare • Microsoft + JAX labs for Healthcare AI – Genomic medicine researchers at the laboratory have been using artificial intelligence to help manage the vast amount of research data needed to power its precision oncology initiatives • Virtual visits • Tele-health 14
  15. Cybersecurity • Companies placed big bets on securing applications and unmanaged IoT devices as well as risk and compliance in the first half of 2020 • Amazon Web Services purchased cybersecurity software company Sqrrl – Advanced threat hunting capabilities were expected to align well with Amazon GuardDuty – Sqrrl analyzes big data to hunt cyberthreats, helping companies identify and address them faster – Utilizes linked data, machine learning, user and entity behavior analysis, risk scoring, and big data technologies to uncover malicious patterns and anomalies hidden within security data sets 15
  16. Transportation Technology • Amazon acquires auto vendor Zoox • Experts predict that Amazon will focus more on integrating the technology into its distribution network than building a fleet of cars. 16
  17. In the Data Enterprise 17
  18. Trends to Continue 2H20 • Graph Solutions • Data Visualization • Stream Processing • Artificial Intelligence 18
  19. Hot Projects • Fraud Detection • Supply Chain Optimization • Preventive Maintenance • Customer Churn 19
  20. AI is disruptive Data is the Foundation Data’s New Highest Use is Training AI Algorithms
  21. 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 • Retaining structure in structured data • Data quality additions 21
  22. Realization that full BOB has a price • Piecemeal architecture with a variety of tools from a number of vendors • If an organization desires to build their modern data ecosystem using “best-of-breed” solutions, the overwhelming challenges will be interoperability, cost, and complexity—not to mention time-to-value – It is not all bad, as there are some interoperability beacons of hope • Understanding, predicting and managing costs is difficult • Complexity of the architecture 22
  23. New Technology Stacks 23
  24. Modern Platform Examples Single Platform example Single Cloud – Azure Single Cloud – AWS Multi-vendor example Data Engineering CDP Data Hub Azure HDInsight Amazon Elastic Map Reduce (EMR) Qubole Data Analytics CDP Data Warehouse Azure Synapse Amazon Redshift Snowflake Data Science Cloudera Machine Learning Azure Machine Learning Amazon SageMaker Databricks Data Catalog CDP Data Catalog Azure Data Catalog AWS Glue Data Catalog Alation Overarching Workload Management CDP Workload Manager None None2 Not applicable Data Movement CDP Data Replication Azure Data Factory (ADF) AWS Glue or Data Pipeline Talend Overarching Deployment CDP Management Console Azure Portal AWS Portal Not applicable1 Overarching Security CDP/SDX Azure Active Directory Identity Access Management (IAM) Not applicable1 24 2 A multi-vendor approach lacks a single overarching workload management, deployment, and security mechanisms. Each individual vendor product will likely have its own means to deliver these features. 1 Not overarching—only individual applications have their own workload management features
  25. 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 25
  26. Data Team Dynamics • Business departments have clearly staked a claim in building their architectures – Still need dedicated technology professionals to do the work – The notion of an "IT professional" is alive and well – The reporting structure is more complicated than ever • Acknowledgement of the need for data deployments to be near the business unit in organization charts • Strategists and implementors are seeing a reduction in the challenges posed by internal grist and resistance to change – Dependence on certain individuals is lessened with the cloud, and many are declaring their organization unshackled from resistance to progress – Acceleration of acceptance and some challenging personnel moments inside the data apparatus in organizations 26
  27. Second Thursday of Every Month, at 2:00 ET Presented by: William McKnight President, McKnight Consulting Group (214) 514-1444 #AdvAnalytics