Harnessing the Power of the Cloud to Solve Analytics Challenges – Data Summit 2023

Harnessing the Power of the Cloud to Solve Analytics Challenges – Data Summit 2023 | Presented by Datavail

In today’s data-driven world, organizations are rapidly shifting their analytics workloads to the cloud to unlock scalability, agility, and innovation. In this insightful session, Tom Hoblitzell, Vice President of Data Management at Datavail, explores how cloud-native analytics platforms are transforming business intelligence, data science, and decision-making.

This presentation provides a strategic roadmap for modernizing analytics environments, overcoming migration challenges, and achieving measurable business outcomes through cloud adoption.

🔗 View the full presentation here: https://www.datavail.com/resources/data-summit-harnessing-power-cloud-solve-analytics-challenges/

🔍 Key Highlights
What is Cloud Analytics? Learn how cloud technologies enable faster, more cost-effective analytics delivery.
Analytics Maturity & BI Strategy: Understand the stages of analytics maturity and how cloud accelerates innovation.
Cloud Migration Trends: Discover Gartner-backed insights on the rapid shift to cloud-based analytics and AI infrastructure.
Business Outcomes: Explore how cloud analytics drives growth, efficiency, agility, and competitive advantage.
Case Studies: Real-world examples from major media, broadcasting, and retail companies showcasing successful cloud analytics transformations.
Modern Architectures: See how AWS and Azure services like S3, Glue, Athena, Data Lake, and Power BI are used to build scalable, secure analytics platforms.
Challenges & Solutions: Address common barriers such as data governance, integration, and skill gaps with best practices and strategic planning.
🎯 Why Watch This Session?
This session is ideal for:

CIOs, CTOs, and CDOs leading digital transformation
Data architects and analytics leaders
IT teams planning cloud migrations
Business leaders seeking faster insights and innovation
Anyone looking to modernize their analytics stack

1. Harnessing the Power of the Cloud To Solve Analytics Challenges Tom Hoblitzell Vice President, Data Management

2. Passion for solving complex global business challenges through advanced technology and leading-edge digital business intelligence A forward-thinking strategist to drive outstanding business results Built and successfully grew analytics practices at major systems integration firms leading growth from start-up to mature practice with over $60 MM in revenue Sold Practices as part of strategic acquisitions (Fujitsu, Capgemini) Acquired and integrated IT acquisitions into existing practices to increase growth and round out capabilities and competencies Acts as a strategic advisor to key clients enabling growth of analytics and digital transformation initiatives to leverage data as a strategic asset T Tom Hoblitzell VP, Data Management, Datavail Over 30 years of experience

3. About Datavail Our business is focused on helping customers leverage data to drive business results 14 Years building and operating mission critical systems $20M Invested in IP that improves service experience and drives efficiency 1,000 Technical employees staffed 24x7

4. A Cloud Analytics What Is It? “Analytics is the process of gathering, cleansing, transforming, and modeling data with the goal of discovering useful information to support decision making.” Source: Quantzig The goal of Analytics is to make data accessible , useful , and actionable , which leads to digital transformation. Cloud Analytics uses modern cloud technologies and approaches to achieve the goal with lower costs, faster scalability, and agile implementation .

5. Analytics, BI and Data Science Solutions Analytics Foundations Business Analytics Applied Analytics Data Science Emerging Analytics Analytics, BI and Data Science Act Investigate Understand Decide Source: Gartner

6. Organizations will move more than two-thirds of advanced analytics for both development and production to the cloud by 2023. Conversely, less than one-third of them are in the cloud today, leading to concerns in maximizing the value in massive migration efforts. Organizations investing in ABI and DSML in phases or separately often bring complexity due to lack of advanced planning, cohesion and cross-manageability. This causes a slower delivery of advanced analytics capabilities, which are an urgent need during fast-paced changes. Analytics Moving to the Cloud Even Faster Than Gartner Originally Predicted By 2023, cloud architects will become key stakeholders when purchasing analytics and BI tools, as scalability and cohesive cloud ecosystems move into the top three key buying considerations. By 2024, 70% of enterprises will use cloud and cloud-based AI infrastructure to operationalize AI, thereby significantly alleviating concerns about integration and upscaling. Source: Gartner

7. 75% Move to the Cloud for Analytics Close to Three-Fourth Currently Use or Plan to Use Cloud for Analytics, BI and Data Science Migration to Cloud (Multiple Responses) Other Responses "Print Management Solutions" "Telecommunications" "Developer Environments" "Office Suite; Collaboration Tools" n=85 All respondents; Excluding ‘Not Sure’ Source: Gartner

8. Advanced Analytics in the Cloud Also Growing Rapidly Mean Source: Gartner, 2023

9. Recommendation: Move Analytics to the Cloud with Modular Expansion Data Preparation Dashboards Reporting Self-Service Analytics Prescriptive Analytics Predictive Modeling Deep Learning Simulation Monitor and Explore Stage 1 Stage 2 Stage 3 On-Premises Cloud Analytics and BI Data Science and Machine Learning Investigate and Learn Cloud-Enabled Marketplace for Composition Source: Gartner

10. Most Important Features Source: Gartner

11. Use Cloud to Compose for Faster Delivery of Advanced Analytics Lower Operational Effort Scalability | Elasticity Evolving Production Get more advanced analytics models Democratize advanced analytics in dashboard DSML ABI Source: Gartner

12. Cloud: Desired Business Outcomes Growth Innovation – new business models, markets, products New customer engagement models Competitive Advantage Mergers and Acquisitions Business Continuity Agility, Time to Market Rapid Business Insights Business Scalability Efficiency Improved Collaboration Cost Efficiency Flexible Economic Models Rapid deployments Reliability and Stability High Availability / Disaster Recovery Compliance and Security

13. CEO’s Top Focus for Each C-Level Role Digitization New Technology in General Efficiency Data & Analytics Cybersecurity ERP Digitization Strategy & Innovation Customer Experience Efficiency & Automation AI, Data & Analytics E-Commerce Data Security & Risk Data Science, Analytics & BI Customer Data & Digital Services Data Exploitation & Access MDM & Data Integration Data Pooling, Convergence, Lake Cash Flow / Preservation Cost Management Profits & Profitability Investment Management Management Information / Forecasting M&A Efficiency, Productivity & Automation Cost Management Digital / Technology Change Operational Excellence Profitability Rescaling / Scalability CIO/CTO CRO/CMO CDO CFO COO

14. Top Outcomes Achieved by Adopting Cloud n = 848 Total Respondents, Excluding "Don't Know/Not Sure" Question: Please rank the top three outcomes your organization has achieved so far by adopting cloud. Source: Gartner Cloud End-User Buying Behavior Survey

15. Why Cloud for Analytics? Analytics Innovation Brainstorming Ideation Visibility Constant Refinement Design Thinking Incubation Focus The Cloud as a Driver of Analytics Innovation Source: Gartner

16. Analytics: New Capabilities in the Cloud vs On-Prem – Help Generate and Manage Innovation Source: Gartner On-premises Cloud Attracting users with emerging capabilities More prototypes with greater visibility Metadata powered collaboration Focus on high-quality analytics Desktop or web-based training Prototype with limited audience Discussion Tired of repetitive basic analytics Onboarding Prototype Pilot Production Ideation Design Incubation Focus Sandbox Constant refinement Elasticity/automation

17. What Do You Believe Will Be the Benefits to Moving Your Analytics to the Cloud? (top 3) Source: Datavail

18. But Moving to the Cloud Can Be Challenging… Top Internal Challenges Adopting Data & Analytics in the Cloud Challenges with technology infrastructure and/or architecture Solving risk and governance issues (security, ethics, privacy, data quality) Adding more agility and flexibility to our data and analytics initiatives Integrating multiple data sources Obtaining skills and capabilities needed Making data and analytics more usable for business consumers and front-line workers 33% 32% 29% 29% 27% 26% n= 270 , total respondents, excluding “don’t know” Source: Gartner

19. M BI Maturity Stages Maturity is now critical to company competitiveness and success. Creating Market Agility and Differentiation Fostering Innovation and People Productivity Integrating Performance Management & the Business Measuring and Monitoring the Business Running the Business 1 2 3 4 5

20. Data Management / Analytic Modernizations Data Lake Non-relational databases Machine Learning Data Warehousing Log Analytics Big Data Processing Relational Databases Data Silos Business Intelligence Business Intelligence DW Silo 1 DW Silo 2 OLTP ERP CRM LOB Devices Web Sensors Social

21. Benefits of Cloud Native Analytics Scalable data lakes Purpose-built data services Seamless data movement Unified governance Performant and cost-effective Data Lake Non-relational databases Machine Learning Data Warehousing Log Analytics Big Data Processing Relational Databases

22. The Analytics Cloud Journey Optimization / Managed Services Modernizations Migrations Assessments / POCs Strategy / Planning Customer Profile / Plans: Do you have a documented cloud strategy and cloud adoption implementation plan? Which Analytic workloads are currently running in the cloud? Which cloud providers do you currently use or plan to utilize (AWS, Azure, Google, Oracle, Other) Preferred technologies for Analytics Top Challenges Workload priorities for 2023: Applications: Repurchase (SaaS), Rehost, Replatform , Refactor? Analytic: Migrations, Modernization? Databases: Migrations, Modernizations?

23. Center of Excellence COE Cloud Engineers / SRE Cloud Solution Architect Cloud Database Administrator Partnerships Training Certifications Create a Center of Excellence for strategic cloud focused initiatives Perform Builds using the Well Architected Approach Execute Migration with a wholistic approach Optimization for performance, ROI, or cost Assessment of environment Prescriptive guidance in place for DevOps / SRE / Cloud Architect teams

24. Best Practices in Moving to the Cloud Get a clear view of your cloud strategy – and align Expected Benefits of moving to the cloud Cloud data strategy XaaS strategy Constraints Roadmap Assess your current state Use the cloud for experimentation Set the right migration approach based on your priorities Put analytics wherever the data is Utilize the power of the cloud to scale Use multiple clouds depending on your purpose Enable self-service analytics

25. Roadmap Strategy Process

26. Summary: Why Move Analytics to the Cloud? “As a Service” of cloud – pay as you go instead of capital outlay Increased scalability. Think about your on-site IT infrastructure Faster insights Easier maintenance and disaster recovery Stronger decision making Can start with a Small Project! Cost-Savings Agility Scalability Solves new analytics requirements (Use-Cases)

27. C Case Studies From small business to large enterprises, see how we’ve helped our clients gain value from their organizational data. National Broadcasting Company Major Media Company Retail Company

28. C Case Studies From small business to large enterprises, see how we’ve helped our clients gain value from their organizational data. National Broadcasting Company Major Media Company Retail Company

29. Case Study: Major Media Company Challenge Client’s IT Staff was dedicated to providing custom reports based on client requirements that required two to three dedicated resources. Cost of Database Software license was becoming prohibitive Basic problem with the on-prem existing analytics solution: Didn’t scale Costly (licenses and VM Servers) IT Bottleneck (required for each dataset developed) Dependence on Affinity ERP email capability (performance and file-size limitations) Dependency on internal staff for report customization Solution Proposed solution was to take advantage of the AWS Cloud Analytics services. Serverless solution reduced cost (pay as you go) Scaled easily Provide Self Service data visualization and data set delivery Automation of data movement and processing

30. Media Company – New Architecture SQLServer DB OLTP OLTP 1. Existing Data Source Bucket with incremental data Stage - S3 2. Stage Data 3. Data Marts RDS RDBMS Amazon RDS Lambda function Amazon CloudWatch AWS Data Pipeline AWS Glue Data Integration Services 7. REST API for Data Integration 4. Self Service Analysis Analysis Amazon QuickSight Business Intelligence Internal User External User Bucket with data sets Data Set Delivery -S3 5. Build and Deliver Data Sets AWS Glue 6. Deliver Reports – signed URL in email Email

31. C Case Studies From small business to large enterprises, see how we’ve helped our clients gain value from their organizational data. National Broadcasting Company Retail Company Major Media Company

32. Case Study: International Retail Company Challenge Existing vendor solution was not providing the reporting and analytics environment required to manage the business. Technology was obsolete Support was minimal “keep the lights on” Needed to expand from B2B to include B2C Sales and Operational Data Expand to include additional data sources Solution Determined that an AWS “Data Lake” solution to bring both structured and unstructured data into the Data Lake for processing to drive analytics for the business. Utilized AWS Data Lab and POC to prove solution addressed business needs A support model was established so that Datavail was in a Build/Run opportunity to provide support for the new solution – from data loads, to reporting, to governance and managing the environment

33. Existing Business Environment Account Support (~24hr response) Wholesalers ~15K+ Stores Chains ~7K+ Stores 3 rd Party 3 Onshore 3 Offshore RBH ~20 Users Comm. Data Comm. Intelligence Bus. Dev. & Planning Report Consumers ~300+ Processing ~100K records daily ~1.5M records weekly Daily/Weekly CWD Data RIS Distributor Data Weekly Chain Scan Data Mix daily/weekly grain Daily Master Data Invoice Sales Order Data Agreements Daily/Weekly/Monthly Cube refresh Master Data Invoice Sales CWD Sales History Chain Scan Data RIS Distributor Sales Industry Exchange Data Support Support Support Requests Agility for Today’s and Tomorrow’s Business Needs – Cloud Flexibility and Speed - Time to Deliver Updates and Data Availability Proactive Control of Data Quality

34. The Solution: Automated Data Profiling/Reporting Analysts On-Prem S3 Bucket AWS Athena Data Catalog Glue Crawler Glue Crawler Profiler Metrics Repository Data Profiler on EMR CSV or Other Files

35. C Case Studies From small business to large enterprises, see how we’ve helped our clients gain value from their organizational data. Major Media Company Retail Company National Broadcasting Company

36. Broadcasting Company has an existing data warehouse that is not meeting the user’s needs and they want to re-engineer this warehouse to meet the functional and analytical requirements of the user The existing DW has obscure field names which forces all reporting requests to go through a Data Scientist vs. enabling the user to create their own reports External data is not integrated into the warehouse for trend analysis or for other types of market analysis National Broadcasting Company Challenges Improving the frequency of digital advertising data will improve and enhance fund raising campaigns and pledge drives The existing DW environment: SQL Server Tableau and Microsoft BI for reporting Alteryx as the ETL tool

37. Solution Considerations Improve the flexibility, scalability and overall capabilities of the warehouse to support business reporting and analytics while providing data to the data science team to focus on analysis that is external to Broadcasting Company Improve and reduce the support structure to make the solution easily supportable by the existing support team including technical training, knowledge transfer, etc. Protect PCI and PII data in a secure manner Leverage the cloud to take advantage of potentially lower costs assuming security can be maintained Provide an approach to start with Broadcasting Company’s Digital business while extending the solution to other lines of business

38. A Modern Data Lake Architecture INGEST MODEL ANALYZE REPORTING Azure Data Factory STAGE & STORE Azure Data Lake Power BI Service Snowflake DB SaaS DATA SOURCES Other Data Sources Ad-hoc Reporting and Analysis Standard Reporting Streaming Data Prayer Data Web Site Data

39. Thank You Tom Hoblitzell VP, Data Management [email protected] www.datavail.com

40. Appendix

41. Datavail Technical Capabilities - Agnostic ETL MDM Cloud Providers Database Reporting/ Analytics Cloud & Big Data Amazon Redshift AWS Data Pipeline AWS Glue AWS Lambda Amazon Elastic Compute Cloud (Amazon EC2) Amazon Relational Database Service (Amazon RDS) Amazon QuickSight