Modern Data Challenges require Modern Graph Technology
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The document emphasizes the importance of modern graph technology for digital transformation, enabling businesses to manage complex data across various sources effectively. Traditional data architectures struggle to meet new requirements, leading organizations to prioritize data quality and integration in the next 12 months. Real-world use cases from finance, retail, and manufacturing illustrate how graph technology can enhance operational efficiency and customer insights.
Modern Data Challenges require Modern Graph Technology
- 1. Modern Data Challenges Require Modern Graph Technology Noel Yuhanna Vice President, Principal Analyst San Francisco, CA Graph Summit April 2023
- 2. Digital transformation continues to be a top priority for global enterprises . . .
- 3. Digital transformations must be powered by new generation solutions to remain competitive … Graph AI Cloud
- 4. 4 Data has become the most critical asset for any business to succeed • Improve customer experience • Enable innovation • Expand markets • Increase revenue • Retain customers • Deliver new products and services • And more ... 51% Commercializing data!
- 5. 5 Data spread across multiple repositories and hybrid cloud is creating new data management challenges ... Multiple clouds Edge On-premises Facebook LinkedIn Opensocial Simply Hired Google+ Twitter Social media Data lakes/DW SaaS SugarCRM Oracle Salesforce Abiquo Eloqua SAP AppDynamics Cloud9 DaaS providers Hoovers OneSource Reuters Windows Marketplace D&B Azure Google AWS 5G Smart devices Edge computing Driverless cars Robots IoT sensors
- 6. 6 IT processes, data and analytics are top focus when it comes to digital transformation 24% 26% 27% 28% 29% 34% 46% Inventory management and distribution Employee experience Product design and development/engineering Security Customer service/experience Data and analytics IT processes “Which is/will be the focus of your organization’s digital business transformation?” Note: Only the top seven responses are reported in this chart Base: 2,252 services decision-makers who are involved in their organization’s digital transformation efforts; Source: Forrester Analytics Business Technographics® Business And Technology Services Survey, 2021
- 7. 7 7 © 2023 FORRESTER. REPRODUCTION PROHIBITED. Data management has become critical for all…. 17% 18% 18% 19% 19% 20% 20% 20% 21% 24% 24% 0% 5% 10% 15% 20% 25% 30% Lack of technology skills Understanding the data Lack of foundational investments Lack of executive support to develop big data… Lack of collaboration between teams Accessibility, availability, and/or readiness of data… Lack of business competency to deal with data that… Organizational business issues with data… Inability to process big data and act on it at the… Maturity of technology around data management Maturity of technology around security What are/were the biggest challenges in executing your vision for data, data management, data science, and analytics? Base: 3627 Data and analytics decision-makers Source: Forrester's Data And Analytics Survey, 2022
- 8. 8 Traditional data architectures are unable to support new data requirements Lack of support for real-time data: Traditional architecture mostly supports batch or micro batched Lack of consistent, trusted data: Data is not consistent across apps, insights, analytics Lack of modern data governance strategy: Most organizations are unable to secure and govern critical business data Lack of integrating all data: Many are unable to integrate all data — across silos Lack of self-service data capabilities: Most traditional architectures do not have self-service Lack of automation to simplify deployments: Most traditional systems lack automation to simplify data management function
- 9. 9 9 © 2023 FORRESTER. REPRODUCTION PROHIBITED. Data quality, data integration, cloud are the top priorities for organizations over the next 12 months 21% 21% 23% 24% 24% 25% 29% Data governance and auditing Specialized (IoT) analytics platforms and solutions to monitor products, customer… Security analytics for threat detection/hunting Master data management Public cloud big data services (AWS, Azure) Data Integration Data quality “Which of the following are the most important components of your organization’s plans for data, data management, data science, and analytics in the next 12 months?” Note: Only the top seven responses are reported in this chart Base: 1,616 business and technology professionals (500+ employees); Source: Forrester’s Future Fit Survey, 2022 © 2023 Forrester. Reproduction Prohibited.
- 10. 10 10 What businesses need is a modern approach to connecting data to support new apps, and analytics. © 2023 Forrester. Reproduction Prohibited. Apps, insights, and analytics need contextualized data Connected data Trusted data Real-time data Self-service data Governed data
- 11. Graph delivers contextualization to support new digital transformation initiatives… Customer transaction data Sensor data Click-stream data Inventory data Point-of-sale data ERP data Social data Supply chain data Product data Sales data Customer data System of record(master data) © 2023 Forrester. Reproduction Prohibited.
- 12. 12 • Make connections quickly and more accurately: For new and emerging business use cases, faster time to value • Data analysis performance: Takes query, insights and predictive analytics to the next level • Uncover hidden connections: In data science, and advanced analytics • Improve staff productivity: With minimal coding and more analysis • Address new business needs: Integrates with AI/ML to deliver new business use cases. Why use Graph Database?
- 13. 13 • Improve customer experience. • Increase automation of internal processes. • Improve operational efficiency and effectiveness. • Increase employee productivity. • Improve existing products and services. The top benefits of Graph are aligned with the top business requirements of digital transformations. Note: Top five responses are shown. “Don’t know,” “other,” “none of these, and “we are not using artificial intelligence (AI) technologies” responses were excluded. Base: 3,139 data and analytics decision makers whose firm is interested in using/planning to use/currently using AI; Source: Forrester Analytics Global Business Technographics® Data And Analytics Survey
- 14. 14 14 © 2017 FORRESTER. REPRODUCTION PROHIBITED. Native graph vs. Non-native/multi-model databases › Key advantages of native graph: • High performance – low latency access • Improved scalability • Optimized query processing – nodes/edges • Improved administration – simplicity • Better data consistency/integrity • Key focus – driving innovation • Comprehensive APIs for graph • Broad tooling • Improved support
- 15. 15 15 © 2017 FORRESTER. REPRODUCTION PROHIBITED. Graph – Use Cases Are Many – Endless Possibilities › 360-degree view of customer › Social network Apps – Facebook, twitter, LinkedIn. › Data Integration/ MDM › Fraud and risk analysis › Analysis of communication/network management › Recommendation engines › Master data › Access control › Hybrid Operational-Analytical Apps and Translytical › And others
- 16. 16 16 © 2017 FORRESTER. REPRODUCTION PROHIBITED. Leading industries – expanded to others .. › Financial services • Fraud detection, portfolio mgt, Upsell/cross sell, stock analysis, risk analysis › Healthcare and life sciences • Clinical trials, patient management, drug research, disease tracking, prescription management, insurance analysis, › Retail • Customer churn analysis, recommendation engine, customer experience, customer intelligence and product revenue analysis › Others – Oil And Gas, Government, Telco, ….…
- 17. 17 17 © 2017 FORRESTER. REPRODUCTION PROHIBITED. Case study: Financial services company uses graph technology to support fraud analysis › Background • With billions of events everyday, this large financial services company was facing a major challenge to detect, alert, and process fraudulent activities. • Data was spread across Oracle, SQL, Hadoop, Hive, files, streams . . . • Integrating data across these sources was a challenge, and with new sources being added, such as clickstream, web logs, and social media feeds, it had to look at a new approach. › Solution • Used connected graph data platform to store, process and leverage unstructured data, including logs and streams, and built models that integrated all relevant data sets in real time to accurately assess if any given activity was a fraud. • Unlike other banks and financial services companies that quite often had false positives, this financial services company was quite accurate in its analysis © 2023 Forrester. Reproduction Prohibited.
- 18. 18 18 © 2017 FORRESTER. REPRODUCTION PROHIBITED. Case study: Retailer leverages graph technology to deliver customer analytics › Background • Big data spread across clickstream, social media, blog, several databases, logs, and data repositories • Wanted integrated view across billing, revenue, and other customer data to better understand its customers and their usage patterns • Retailer also wanted real-time insights, immediate access to billed and unbilled revenue, and ability to upsell and cross-sell new products. › Solution • Retailer used a combination of Hadoop, streams, replication, Hive, NoSQL, as connected data within a lake to deliver actionable insights • Some integration took place in Hadoop, others in-memory and Spark. • Plans to add more data sources — geolocation, customer preferences . . . © 2023 Forrester. Reproduction Prohibited.
- 19. 19 19 © 2017 FORRESTER. REPRODUCTION PROHIBITED. Case study: Manufacturing organization uses graph technology for IoT analytics › Background • A large manufacturing company with hundreds and thousands of machinery and components and more than a dozen plants wanted a solution that could minimize machinery failures. • Some of the machine equipment was getting old, but the company wanted to ensure that replacements were being done for the right machines, parts, etc. › Solution • They installed sensors and additional devices to collect data that fed into the connected data fabric along with other data sets. It streamed data to Hadoop in its data center, processed the data with historical data to determine machines likely to fail, wear out, and have parts issue. • Overall, the manufacturer claims to have eliminated many hours of machine outages every month and, thus, have related to savings of millions over the year. © 2023 Forrester. Reproduction Prohibited.
- 20. Data is a huge prerequisite to AI success!
- 21. . . . however, messy data without Context can dramatically slow the AI process
- 22. 22 © 2021 Forrester. Reproduction Prohibited. Separate data engineering tasks from ML model building tasks to make model building faster, more focused on business use cases Data Connection Data acquisition Data source Data source Data source Data source N Feature engineering Data engineering Model building Data + Graph Modeling © 2023 Forrester. Reproduction Prohibited.
- 23. Machine learning algorithms analyze data to create predictive models.
- 24. Graph and AI can help determine which shipments to prioritize and where to reroute to. ML can help predict supply chain issues while there is still time to remediate.
- 25. ML can help predict who will launch what cyberattack before it happens. Graph and AI can help determine what systems are more vulnerable and need attention.
- 26. Graph and AI can determine the best way to retain customers and improve customer experience. ML can help predict customers likely to churn.
- 27. Graph and AI can help determine when to shut the production line down to minimize cost and deliver best business performance. ML can help predict machine faults before they shut down the production line.
- 28. Graph technology takes AI/ML to the next level … invest in it and make it part of your digital transformation strategy to gain competitive edge
- 29. Graph database adoption continues to accelerate across all industries 14% 23% 35% 47% 65% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2010 2015 2020 2025 2030 © 2023 Forrester. Reproduction Prohibited.
- 30. 30 © 2021 Forrester. Reproduction Prohibited. Forrester Graph Data Platforms Wave Report © 2023 Forrester. Reproduction Prohibited.
- 31. Graph technology continues to evolve expanding across various platforms and architectures Graph Database Applications Graph databases started out with developers building custom Apps © 2023 Forrester. Reproduction Prohibited.
- 32. SaaS/Commercial Applications Graph technology continues to evolve expanding across various platforms and architectures Embedded Graph Database Embedded graph databases expanded to cover building modern SaaS/Commercial Apps © 2023 Forrester. Reproduction Prohibited.
- 33. AI/ML/Data Science Platforms MultiModel Data Platforms Graph technology continues to evolve expanding across various platforms and architectures Graph technology is now seen expanding into new platforms and architectures Graph Technology Data Fabric Data Mesh EDW / Data Lakes/ Lakehouse Edge Platforms/Apps 22% 18% 15% 7% 10% 18% © 2023 Forrester. Reproduction Prohibited.
- 34. Future of Cloud Infrastructure Clouds SaaS Clouds Industry Clouds Domain Clouds Use Case Clouds Cloud Evolution © 2023 Forrester. Reproduction Prohibited.
- 35. 35 Thank You. Noel Yuhanna Vice President, Principal Analyst © 2023 Forrester. Reproduction Prohibited.