Democratized Data & Analytics for the Cloud
Download as PPTX, PDF0 likes74 views
This document discusses how companies are moving workloads to the cloud and democratizing data and analytics. It notes that 85% of organizations will embrace cloud-first principles by 2025 according to Gartner. Implementing modern data integration is challenging due to issues like real-time data changes, skills shortages, data accessibility, budgets, and data quality. Data silos negatively impact companies through increased costs, missed goals, and poor customer experiences. The document promotes Precisely's data integration and catalog solutions for building adaptable cloud environments and breaking down data silos.
Democratized Data & Analytics for the Cloud
- 1. Democratized Data & Analytics for the Cloud John de Saint Phalle | Senior Product Manager
- 2. Business trends influencing how companies are moving to the cloud Financial responsibility for IT expenditures Data-centric cloud computing Democratized data
- 3. “85% of organizations will embrace a cloud- first principle by 2025” - Gartner “55% of leaders site data modernization as the reason for their shift to cloud” - Deloitte “Approximately $100 billion of wasted migration spend is expected over the next three years” - McKinsey & Company
- 4. Implementing a modern data integration framework isn’t easy Confidential: Prepared for Precisely Customers and Prospects 0 10 20 30 40 50 60 Real-time CDC Skills Governance Data Accesibility Budget Data Quality Legacy % of People Who Consider this a Top Challenge (Rated 1 or 2) 1. Real-time CDC: Keeping data up to date, accessing and processing data in real-time 2. Skills/Staff: Shortage of skills and staff who have an understanding across cloud and legacy technologies 3. Data Accessibility: Making data accessible to users across the business 4. Budget: Spending on maintenance and not innovation 5. Data Quality: Poor data quality and lack of trust in data 6. Legacy systems: Difficult to leverage data from legacy systems (mainframes, EDWs, IBM i etc.) with modern cloud platforms 7. Scalability: Ability to scale with and process massive data volumes Q. What are your top challenges when it comes to implementing a modern data architecture?
- 5. Data silos impact to companies Expense Reduction • $800K (annual) expense reduction • Today, 4-hour peak is ~1000 MIPS which costs the enterprise $1.0M. • An 80% cost reduction ($800K annually) is expected once CDC is fully implemented by reducing the 4-hour rolling avg. Leveraging inaccurate and incomplete data to make decisions Unable to meet company goals Diminished performance Organizational Current architecture is expensive to maintain and scale Missed SLAs Technology Increased costs Value of cloud investment decreased Value of mainframe and IBM i investment reduced Financial Deliver a poor customer experience Slower bringing new revenue driving applications to market Customers
- 6. Democratizing customer data for cloud analytics IT need standards for infrastructure while it remains seamless to employees Cloud applications and platforms need to be fed data that can be trusted Users demand flexibility
- 7. Building an environment that can adapt 1 Data sharing models 2 Business process changes 3 Cloud environment interoperability 4 Deep IT management integration
- 8. Precisely’s single solution for your cloud integration needs
- 9. Cloud / VPC / On-Premises Data Integration Data Observability Data Quality Geo Addressing Spatial Analytics Data Governance Data Enrichment APIs and SDKs Enterprise Business Systems • Enterprise apps • Analytics tools • Precisely industry apps • BI dashboards • AI/ML Enterprise Data Sources • Business Intelligence • CRM • Workforce mgmt. • Data warehouse • ERP • Billing Data Integrity services Data Integrity Foundation Data catalog Intelligence Agents
- 10. Precisely leads with a data integrity vision You’ve struggled with traditional solutions. We believe there’s a better way. Data access owned by IT Collaboration between IT and business data users Massive, loosely integrated solutions Just the scalable, interoperable capabilities you need Data must be brought to the solution Workflows designed for the cloud that run alongside data Slow, batch ETL processes Streaming data pipelines to the cloud Separate business and IT metadata Scalable, shared catalog of business & technical metadata Rules-based data management AI-driven quality rules, alerts, and data enrichment
- 11. Data Integration Break down data silos by quickly building modern data pipelines that drive innovation
- 12. Data Integration differentiators Real-time data streaming gives you fast access to fresh data when and where you need it Business-friendly user interface allows first-time users to create data pipelines without coding Build once, deploy anywhere principle allows you to build data pipelines in the Precisely Cloud and deploy them wherever your data lives 50+ years of domain expertise in mainframe and IBM i systems is built into the Data Integration module to handle your complex data sources Integration with the Data Integrity Suite Foundational enables Data Integration to share metadata with other modules - exponentially building value and spurring innovation
- 13. Data Catalog features & benefits Enables users to maximize value and innovation by connecting all the suite services Easily adapts to customer processes and languages without code because of a flexible, pre-configured metamodel Faster and more complete asset discovery with built-in intelligence that identifies and semantically tags assets A single searchable inventory of data assets built and maintained by automated metadata harvesting that crawl, profile, and manage data Users gain a geographic frame of reference with a robust collection of pre- loaded, analysis-ready attributes that enrich their data
- 14. Thank you
Editor's Notes
- #3: Slides 3 Why are we moving to the cloud
- #4: Slide 4 How prevalent is this move? Who's doing it? Research shows that: 85% of organizations will embrace a cloud-first principle by 2025 55% of leaders site data modernization as the reason for their shift to cloud Approximately $100 billion of wasted migration spend is expected over the next three years So despite the excitement around getting to the cloud, organizations need to be careful…
- #5: It is clear that trying to bridge this integration gap is not easy. We surveyed the marketplace and these were are all identified as top challenges when it comes to implementing a modern data architecture Real-time CDC: Keeping data up to date, accessing and processing data in real-time Skills/Staff: Shortage of skills and staff who have an understanding across cloud and legacy technologies – good developers are hard to find Data Accessibility: Making data accessible to users across the business Budget: Spending on maintenance and not innovation Data Quality: Poor data quality and lack of trust in data
- #6: Without a way to easily integrate all of their enterprise data together, organizations are left with massive data silos. And, these data silos can have a significant impact. We can bucket these impacts into 4 groups. The first is organizational where we see companies are leveraging inaccurate and incomplete data to make decisions, unable to meet company goals, and struggling with diminished performance. From a technology perspective they find that their current architecture is expensive to maintain and scale and they are dealing with missed SLAs. In the market they suffer from delivering a poor customer experience and are slower bringing new revenue driving applications to market. Finally, there is a financial impact as well. There can be increased costs, a decrease in the value of cloud investment (of which we have seen is substantial), and the value of mainframe and IBM i investment reduced.
- #10: The modular, interoperable Precisely Data Integrity Suite contains everything you need to deliver accurate, consistent, contextual data to your business - wherever and whenever it’s needed. Data Integration: Break down data silos by quickly building modern data pipelines that drive innovation Data Observability: Proactively uncover data anomalies and act before they become costly downstream issues Data Governance: Manage data policy and processes with greater insight into your data’s meaning, lineage, and impact Data Quality: Deliver data that’s accurate, consistent, and fit for purpose across operational and analytical systems Geo Addressing: Verify, standardize, cleanse, and geocode addresses to unlock valuable context for more informed decision making Spatial Analytics: Derive and visualize spatial relationships hidden in your data to reveal critical context for better decisions Data Enrichment: Enrich your business data with expertly curated datasets containing thousands of attributes for faster, confident decisions
- #11: You’ve likely been working on this for a long time, but legacy solutions aren’t serving you today. We have a vision for delivering data with integrity to your business.
- #12: The first module is Data Integration. Typically, in any major data initiative, you first need to connect to sources, and sometimes move or replicate data to another environment. With Data Integration, you can easily create streaming data pipelines that integrate data from core environments such as relational, and of course, mainframe and IBM I, with modern cloud-based data platforms like Snowflake to drive analytics and innovation and extend the value of your mission-critical systems. We understand that pipelines must scale for your needs today and extend for tomorrow.
- #13: Mainframe was announced in 1964, Precisely traces its lineage back to 1968