Data Virtualization Accelerating Your Data Strategy
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Presentation from CIO Data Innovation on Data Innovation, June 21st, 2018, by Olivier Tijou, Country Director FR EMEA.
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Data Virtualization Accelerating Your Data Strategy
- 1. Data Virtualization Accelerating your data strategy Olivier Tijou Country Director FR EMEA CIO Data Innovation June 21st, 2018
- 3. 3 TOO MUCH DATA…. TOO LITTLE INSIGHT”
- 4. - Source: https://software.intel.com/sites/default/files/article/402274 /etl-big-data-with-hadoop.pdf By most accounts, 80 percent of the development effort in a big data project goes into data integration and only 20 percent goes toward data analysis.”
- 5. 5 Apps & Machine Datamarts Warehouse Staging Database Apps MarketingSales ExecutiveSupport Evolution of the Data production & consumption Governance It is difficult to maintain consistent data access and governance policies across data siloes. Integration is delegated to end user tools and applications Integration Traditional data integration is extremely resource intensive. Agility & Productivity Cloud JSON JSON Big Data AI/ Machine learning Stream Social Video Predictive
- 6. 7 E T L IT Architecture is Unmanageable & Brittle because: IT – Business Dilemma IT Focuses on Data Collection & Storage Business Focuses on Data Visualization & Analysis No One Focused on Data Delivery – So create 100’s to 1K’s of brittle direct connections and replicate large volumes of data Inventory System (MS SQL Server) Product Catalog (Web Service -SOAP) BI / Reporting JDBC, ODBC, ADO .NET Web / Mobile WS – REST JSON, XML, HTML, RSS MS Excel Denodo Excel Add-in Log files (.txt/.log files) CRM (MySQL) Billing System (Web Service - Rest) Cloud DWH Product Data (CSV) E T L Portals SOA, Middleware, Enterprise Apps WS – SOAP Java API Customer Voice (Internet, Unstruc) Data Lake DWH
- 7. 8 IT and Business Going in Different Directions BI Benchmark Report High Cost - IT spends ~1% of Revenue on ETL & Storage 75% of data stored is not used – large £ wasted 90% of all queries are for Current data not available from traditional EDW or data lakes Long Time – Months to Build ETL Process & new data reservoirs 2+ Months to add new data source to an EDW 1 – 2 Months to build complex dashboard or report Big Data is not the silver bullet IT Slowing Down By2020 500% growth in Data & Device Avalanche Due to lack of data accessibility today < 0.5% of all data is ever analyzed and used Source: Business Speeding Up To remain competitive, by 2020, Business Decision Speed & Analysis Sophistication Requires 300% Increase Source:
- 8. 9 Solution to IT/Business divergence: IT Slowing Down By2020 500% growth in Data & Device Avalanche Due to lack of data accessibility today < 0.5% of all data is ever analyzed and used Source: Business Speeding Up To remain competitive, by 2020, Business Decision Speed & Analysis Sophistication Requires 300% Increase Source: Data Virtualization: The universal data access IT and Business to move at different speeds so IT can store data in the most efficient way w/o affecting the business & Business can use the best tool to make decisions without affecting IT Add new data sources and consumers without limitations IT takes back control on data: governance & security FedEx for Data
- 9. 10 An Agile Information Architecture IT: Flexible Source Architecture Business: Flexible Data Usage IT can now move at slower speed w/o affecting business Business can now make faster & more sophisticated decisions as all data accessible by any tool of choice
- 10. 11 Five Essential Capabilities of Data Virtualization 4. Self-service data services 5. Centralized metadata, security & governance 1. Data abstraction 2. Zero replication, zero relocation 3. Real-time information
- 11. Denodo Platform Architecture How it works Development Lifecycle Monitoring & Audit Governance Security Development Tools / SDK Scheduler Cache Optimiser JDBC/ODBC/ADO.Net SOAP / REST WS U LoB View Mart View J Application Layer Business Layer Unified View Unified ViewUnified ViewUnified View A J J Derived View Derived View J JS Transformation & Cleansing Data Source Layer Base View Base View Base View Base View Base View Base View Base View Abstraction
- 12. 14 ROI and TCO of Data Virtualization Customer-reported projected savings by percentage Data Integration Cost reduction 60-80% savings Traditional Call Centres, Portals 30-70% savings BI and Reporting 40-60% savings ETL and Data Warehousing Project timelines of 6-12 months reduced to 3-6 months Up to 85% reduction in time Cost
- 13. 15 Example: Time-to-Market, Development and Test Cost Savings Improvement of Value Drivers: A leading financial services company uses data virtualization to create a data services layer for all of their development teams. They saw cost savings of thousands of hours of development time as the developers are not having to “hunt down and access the data” themselves, but had the data delivered by readily available data services. This equated to a saving of nearly $360,000 per year. ROI and TCO of Data Virtualization Value Driver Metric Goal Actua l Time to Develop Time to develop web service in days 50% 90% Time to Deploy Time to Deploy web service in days 50% 90% TTM Overall time it takes to make web service available for use 60% 90% Time to Engage Time it takes for business to engage with IT 75% 75% Performance Performance of web services 50% 60% Impact Analysis How fast can we perform impact analysis 50% 90% Enterprise Architectural Alignment Ease at which data from disparate sources can be integrated Security, data classification High
- 14. 16 Gartner Gives DV its Highest Maturity Rating 16 “Data Virtualization can be deployed with low risk and effort to achieve maximum value.”
- 15. 17 Patterns de Virtualisation des Données
- 16. 18 Denodo Leader in Big Data Fabric Denodo Technologies continues to extend its big data fabric offering Denodo offers a credible big data platform that helps users build an enterprise wide big data fabric quickly. Denodo’s key strength lies in its unified data fabric platform that integrates all of the key data management components needed to support real-time and dynamic use cases, such as real-time analytics, fraud detection, portfolio management, healthcare analytics, and IoT analytics. Customers like its broad and easy-to-use data integration, end-to-end lineage, integrated governance, simplified data modeling capabilities, search, optimized query, and analytical engine. Denodo’s AI and machine learning capabilities are expanding rapidly to focus on delivering a higher degree of automation at every layer of the big data stack
- 17. 19 Denodo The Leader in Data Virtualization DENODO OFFICES, CUSTOMERS, PARTNERS Palo Alto, CA. Global presence throughout North America, EMEA, APAC, and Latin America. Paris Hub for France, Switzerland, BELUX LEADERSHIP Longest continuous focus on data virtualization – since 1999 Leader in 2017 Forrester Wave – Enterprise Data Virtualization Winner of numerous awards CUSTOMERS ~500 customers, including many F500, G2000 & start-ups companies across every major industry have gained significant business agility and ROI. FINANCIALS Backed by $4B+ private equity firm (HGGC). 50+% annual growth; Profitable.
- 18. 20 Example of Agile Architecture - Autodesk
Notes de l'éditeur
- Denodo is the leader in data virtualization. Rather than moving the data, data virtualization provides real-time, integrated views of the data across all your sources.
- With the data deluge that every enterprise is facing, there is way too much data that gets thrown away because enterprises do not yet know how to generate actionable insights from those sets of data That means lost opportunity, lost business, lost revenue, and wastage of otherwise valuable data
- Because of ETL tools, companies are spending a tremendous amount of their big data development efforts on data integration vs. data analysis. Intel took a look at this and found that it was 80/20; companies are putting 80% of the effort on data integration alone, and only 20% into the core function – analyzing the data.
- It is difficult to integrate numerous on-premises and cloud data sources. Traditional tools cannot integrate streaming data and data-at-rest in real time. It is difficult to maintain consistent data access and governance policies across data siloes. Traditional data integration is extremely resource intensive. Integration is delegated to end user tools and applications
- Big data landscape, already a complex landscape with hundreds and thousands of players is becoming more and more complex by the day There are new providers of infrastructure, analytics solutions, enterprise applications, open source applications and data sources who are trying to solve big data problems one way or the other But while helping solve specific big data related problems these numerous players are also creating one big issue for enterprises - creating a lot of data silos which don’t talk to each other very well Some players are trying to solve cross-infrastructure / cross-platform issues but the scope is very limited for them
- Let’s look at this dilemma from another angle. Essentially, IT covers data collection and storage, business focuses on using the data, but what about this part in the middle? No one is focused on data delivery. So what do we do? We wing it, and this is not ideal, over the long run. We create hundreds or sometimes even thousands of one-off, ad-hoc connections, and we replicate data in a haphazard fashion, and this quickly leads us down the road of inefficiency. But access to the data in the underlying systems is not easy. They are buried across multiple different systems and most often siloed. They are in different formats and require different types of access methods. But business has to go on, and business users resort to manual laborious tasks of directly accessing the data from these systems. If IT respond to their requests, they most often create point-to-point batch integrations, which are again time consuming and error prone. As a result, it takes way too long to get the answers to business users. Forrester says, “Data bottlenecks create business bottlenecks.”
- Business and IT are actually moving in opposite directions. Business needs to speed up, but IT is actually slowing down. IT is slowing down because data volumes are exploding. Forbes found that IT is only able to process less than 5 percent of the available data, a percentage that will shrink as data volumes grow. This is because ETL processes are time-consuming to set up and maintain, and they hinder flexibility, scalability, and agility.
- The solution is data virtualization. Because data virtualization provides real-time data without replication, business can be relatively independent from IT, and move at its own pace. Storage is not an issue, because data virtualization abstracts users from the complexities of storage. Data virtualization also enables business users to use their tools of choice, so they can select the best ones for their needs. And new sources, consumers, or attributes can be added relatively quickly, without extensive re-coding or re-testing.
- Data virtualization has many capabilities, but now we’re going to focus on these 5 essential capabilities.
- Let’s take a closer look at the ROI and the total costs. If you look at data integration, call centers and portals, or BI and reporting initiatives, data virtualization amounts to substantial savings. For ETL and data warehousing initiatives, data virtualization can cut the time in half, or more.
- Data virtualization customers are seeing some fairly dramatic ROI. As you can see with these examples, these customers are either meeting or practically doubling their expected returns. It all comes down to being able to deliver the data in a “business-ready” format, exactly when its needed.
- Here we see that data virtualization is now on the very mature side of the Gartner Hype Cycle, compared with other information infrastructure solutions. Gartner says that now “Data Virtualization can be deployed with low risk and effort to achieve maximum value.” --------------------------- Plateau of Productivity The real-world benefits of the technology are demonstrated and accepted. Tools and methodologies are increasingly stable as they enter their second and third generations. Growing numbers of organizations feel comfortable with the reduced level of risk; the rapid growth phase of adoption begins. Approximately 20% of the technology's target audience has adopted or is adopting the technology as it enters this phase. Year to mainstream adoption: less than 2 years