Future.ready().watson dataplatform 01
•Télécharger en tant que PPTX, PDF•
1 j'aime•144 vues
Intervento Brunello Bonanni IBM Government Agencies 13 settembre 2017 Open days "Data &analytics framework"
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Future.ready().watson dataplatform 01
Similaire à Future.ready().watson dataplatform 01 (20)
Plus de Redazione InnovaPuglia
Plus de Redazione InnovaPuglia (20)
Dernier
Dernier (20)
Future.ready().watson dataplatform 01
- 1. Watson Brunello Bonanni Client Technical Advisor IBM Government Agencies Future.ready() Watson Data Platform
- 2. Watson Data Platform Architecture - Overview Common Processes Common Data Business Analyst BI Developer API Data Scientist DS Tools Data & Analytics Processing Protected Data Access (Governance) Data Sources StreamsPublicExternalAppsCloudOn Prem Data Engineer DE Data Flows, Models, Machine Learning Security, Governance, Auditing, etc. Productive use experiences geared to specific personas Broad set of connectivity
- 3. We are unleashing the power of data with Watson Data Platform Data Engineering Data Science Business Analysis App Development Data Sources • On-premises / cloud • Structured / unstructured [and content repositories] • In-motion / at-rest • Internal / external Hadoop NoSQL / SQL Object store Discovery / Exploration Machine learning Model development Reports / Dashboards Applications APIs Integration Matching / Quality Streaming Persist Analyze Ingest Deploy Iterate Govern Data Assessment Metadata / Policies Find Share Collaborate Data Fabric common data, pipelines and projects Composable data & analytics cloud services 1 1 2 Tailored user experiences for data professionals 3 Foundational elements that provide a common catalog, projects, and community capabilities across the platform 3 3 2
- 4. We have the breadth of capabilities and offerings required Fit for purpose User Experiences Ingest Analyze Persist Data Connect for the Data Engineer Data Science Experience for the Data Scientist Watson Analytics for the Business Analyst Bluemix for the Developer Access and prepare data with Data Connect Migrate data with Bluemix Lift Capture data-in-motion with Streaming Analytics Rapid, in-memory processing with Apache Spark Continuous intelligence with Watson Machine Learning Real-time analysis on data-in-motion with Streaming Analytics Easily store JSON data with Cloudant Optimize for analytic workloads and warehouse data with dashDB for Analytics Optimize for online transactional processing workloads with dashDB for Transactions Answer questions about complex networks of inter-related data with Graph Choose from the best open-source databases with Compose
- 5. Watson Data Platform Fabric A console to manage activities and monitor usage A catalog to store and unify metadata across multiple sources A single orchestration pipeline that brings together data from distinct sources and flows that data to multiple runtime engines Common tools including shapers and data visualization capabilities We are unifying Watson Data Platform through a new “Fabric” Console Catalog Community Projects Connectors Tools Orchestration … Enable team collaboration through projects and a community Enable simple access to disparate data sources through connectors
- 6. 6 Tailored Experiences for Users Collaborating Together Architects how data is organized & ensures operability Gets deep into the data to draw hidden insights for the business Works with data to apply insights to the business strategy Plugs into data and models & writes code to build apps Inges t data Transfor m: clean Create and build model Evaluat e Deliver and deploy model Communicate results Understand problem and domain Explore and understand data Transfor m: shape OUTPUT ANALYSIS INPUT Data Engineer Data Scientist Business Analyst App Developer IBM Bluemix Data Connect Data Science Experience Watson Analytics Bluemix
- 7. Data Science Experience 7 Built-in learning to get started or go the distance with advanced tutorials Learn The best of open source and IBM value-add to create state-of-the-art data products Create Community and social features that provide meaningful collaboration Collaborate URL: http://datascience.ibm.com
- 8. Watson Machine Learning • ML models are first-class entities in the WDP asset Catalog • Model Builder assistant simplifies data preparation and offers an “Automatic Path” to help with model selection
- 9. HOW TO CAPTURE VALUE IN THE NEXT FIVE YEARS 1 Shift to a platform mindset
- 10. HOW TO CAPTURE VALUE IN THE NEXT FIVE YEARS 1 Shift to a platform mindset 2 Meaning Making Owner
- 11. HOW TO CAPTURE VALUE IN THE NEXT FIVE YEARS 1 Shift to a platform mindset 2 Meaning Making Owner 3 Embrace the Vision of change
Notes de l'éditeur
- WDP is made up of layers Integrated experiences designed for the core personas A common processing layer of data flows, models, machine learning and analytics – how we get to the insights A foundation of common data which enables self-service access The goal of the platform is to bring these pieces together. By itself DSx is a great tool for data science. Watson Analytics is a great tool for BI. But they don’t enable transformation until the work together across broader use cases.
- The “Experiences” are product offerings that consist of a set of services and an interface tailored for each role. In addition to the tailored interface, collaboration really ties the team and the organization together by allowing them to share projects, code and ideas. Imagine a Data Engineer builds out a new data source and shares that asset with the Data Scientist and the Business Analyst. The Business Analyst immediately builds the reports and dashboards they need. The Data Scientist experiments with the data and ultimately builds a model that passes all the tests and is worth of promoting to new applications. They can immediately share that model with the Application Developer who deploys the new application using the model. Along this journey the team members are keeping each other updated on their status, asking questions, maybe sharing ideas or requirements. This is where Data and Analytics Development becomes a team sport. No longer does this need to be done in silos. Additionally, because these assets can now to published – other departments can re-use these assets, making the entire organization more agile. When you look at refining data into a valuable business results, it will involve a number of roles, each with a specific purpose. Data Engineer: Drive data integration, connections, and quality Data Scientist: Find new trends and convert into models Business Analyst: Perform general analysis and prepares visualizations App (Application) Developer: Uses many data services and integrates models The Business Analyst may get assistance from the emerging Data Scientist role who can do a more sophisticated analysis, find a root cause to a problem, and develop a solution based on an insight that he or she discovers. The Data Scientist role is a pretty broad spectrum of individuals, ranging from the traditional user of SPSS, SAS, etc. to the “new” Data Scientist that is manipulating large amounts of data using open sources tool and program languages (versus a GUI). The Data Engineer is another critical role. They focus enable data integrations, connections (plumbing) and data quality. They do the underlying enablement that a data scientist and business analyst depend on. The App Developer himself needs data services to store and manage the applications he is building. For example, the data scientists may develop a model or algorithm that then gets instantiated in a reactive application. They all have similar requirements: They want self-service; they often take the do-it-yourself approach which makes it challenging to collaborate and move the result to production. It also may result in them not having access to all data. They often need and want access to many different tools and capabilities, many of which are open-source based. They want and need to collaborate with each other. Getting all of these groups working together more easily can speed time to insight and results.