Cloud and business agility
•Descargar como PPT, PDF•
0 recomendaciones•622 vistas
How to Effectively Use the Cloud To Gain Better Business Agility
Recomendados
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Destacado
Destacado (11)
Similar a Cloud and business agility
Similar a Cloud and business agility (20)
Último
Último (20)
Cloud and business agility
- 1. Driving Business Agility Through Cloud-based Analytic Innovation Michael O’Rourke IBM Cloud Data Services © 2015 IBM Corporation
- 2. 2 "Without big data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway." - Geoffrey Moore
- 3. 3 Business Agility Through Data Requirements Based Top-Down Design Integration and Reuse Competence Centers Better Decisions Enterprise Focus Opportunity-Oriented Experimentation Throwaway Hackathons Business Innovation Functional Focus Traditional Big Data
- 4. GlobalDataVolumeinExabytes Sensors (InternetofThings) Multiple sources: IDC,Cisco 100 90 80 70 60 50 40 30 20 10 AggregateUncertainty% VoIP 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 2005 2012 2017 By 2017 the number of networked devices will be more than double the entire global population. Social Media (video, audio and text) The total number of social media accounts exceeds the entire global population. Enterprise Data The uncertainty is growing alongside data complexity 4
- 5. To manage risk and create agility, embrace all data ….uncertainty of new information is growing alongside its complexity Volume Variety Velocity Data at Scale Terabytes to petabytes of data Data in Many Forms Structured, unstructured, text, multimedia Data in Motion Analysis of streaming data to enable decisions within fractions of a second Veracity Data Uncertainty Managing the reliability and predictability of inherently imprecise data types
- 6. 010101010101010101010101010101011101000101010101010101110101010101101010101110101 0101010101010101010101010101011101000101010101010101110101010101101010101110101 Organizations that embrace data in all its forms can unlock greater insight Analyze Data (Integrate & Interconnect) Apply Insight (Intelligent Outcomes) Capture Data (Instrument) Identify Patterns (Unlock Insight)
- 7. But, Most of the data you might need… you do not own 60% of determinants of health Volume, Variety, Velocity, Veracity 30% of determinants of health Volume 10% of determinants of health Variety Clinical data Genomics data Exogenous data (Behavior, Socio-economic, Environmental, ...) 1100 Terabytes Generated per lifetime 6 TB Per lifetime 0.4 TB Per lifetime Source: "The Relative Contribution of Multiple Determinants to Health Outcomes", Lauren McGover et al., Health Affairs, 33, no.2 (2014)
- 8. Big Data Who are my brand advocates, fence sitters and adversaries? Are my employees effective at engaging with customers? Which customers are likely to defect to my competitor? How are my customers and prospects engaging with my products and services? What is the customer sentiment regarding my brand? What new products and features does my customer desire? How do my customers feel about my competitors products? Fuels Insights That Enable For the Enterprise Higher Sales Conversion Rates Sales Marketing Customer Service Product Development Workforce Optimization Improved Customer Service Higher Loyalty Enhanced Online Accuracy New Product Innovation New Demand Generation Risk Mitigation The Business Agility Process … Align big data to business outcomes
- 9. But How?But How? Is Information Accurate? Takes too Long Can’t find the right information Data Quality Problems Ease of Use Integration of Different Systems Traditional Barriers Big Data Barriers
- 10. 10 The 3 R’s of Success with Big Data Analytics • Revolution • Responsiveness • Resiliency
- 11. “If you are not moving at the speed of the marketplace you’re already dead – you just haven’t stopped breathing yet” Jack Welch
- 12. Revolution – Managing Disruption 12 Data Products Need to BeData Products Need to Be Built DifferentlyBuilt Differently Give Data Back in PowerfulGive Data Back in Powerful WaysWays We Don’t Have Time to Do It Right,We Don’t Have Time to Do It Right, But We Have Time to Do It OverBut We Have Time to Do It Over Decide on Where to StartDecide on Where to Start Building Your ApplicationBuilding Your Application Create and Die By Your ProductCreate and Die By Your Product Pre-Flight ChecklistPre-Flight Checklist - DJ Patel, US Chief Data Scientist - Ruslan Belkin, VP Engineering Salesforce
- 13. Responsiveness – Data Sensitivity 13 In-House Data Typically Structured External Data Unstructured, but converted to structured. Unfamiliar External Data Leveraged As-Is Homemade Data Solution Augmentation
- 14. Big Data - Examples In-House Data External Data . Unfamiliar External Data Homemade Data Leads Most Likely to Generate New Sales Analysis of Customer Transactions Over Time Understanding Customer Loyalty Patterns Market Basket Analysis on Short & Long Term Behavior Targeted Advertising Use Browsing History Targeted Discounts via Phone Recognition of Possible Attrition Social Media Sentiment / Buzz on Your Reputation Pharmaceutical Drug Analytics Through Refill Patterns “Personalized” Credit Offers per Customer Hospital and Physician Quality Ratings Experimentation for Customized Landing Pages Patient Claim Analysis Based on Proximity to “poor” Locales
- 15. 15 Elastic Provisioning Pay-as-You-Go Manage High Volume External Data Sources Self-Service Through a Browser SQL / NOSQL – Unstructured Data Access Data Anywhere, Anytime Leverage Current Cloud Apps Resiliency – Leveraging Cloud Elasticity
- 16. Big Data Analytics – Reference Architecture Sensors Internet Social Media Services Customer Conversations Public and Internal Sources BackOffice Applications Old and New Sources Information Ingestion 16 Data Connection / Movement 16 Data Shaping / Cleansing Real-Time Data Streaming Distributed Messaging System DataWorks Apache Kafka Streams
- 17. Information Ingestion Analytic Sources 17 Data Connection / Movement 17 Data Shaping / Cleansing 17 Logical Data Warehouse 17 Interactive Queries and Iterative Data Processing 17 Batch Processing Framework Real-Time Data Streaming Distributed Messaging System Sensors Internet Social Media Services Customer Conversations Public and Internal Sources BackOffice Applications Old and New Sources DashDB Cloudant Postgres DB2 MongoDB ReThinkDB Redis Big Data Analytics – Reference Architecture
- 18. 18 Information Ingestion Analytic Sources Interactive Analytics Alerting, Reporting and Planning Visualization & Collaboration Real-Time Decision Mgmt. Systems of Engagement Accelerators Metadata Catalog Insight Hub Activity Hub Content Hub Master & Reference Data Hubs Information Interaction 18 Logical Data Warehouse 18 Interactive Queries and Iterative Data Processing 18 Batch Processing Framework 18 Data Connection / Movement 18 Data Shaping / Cleansing Real-Time Data Streaming Distributed Messaging System Sensors Internet Social Media Services Customer Conversations Public and Internal Sources BackOffice Applications Old and New Sources Predictive Analytics D3 Embeddable Reporting Apache Zeppelin DashDB Cloudant Postgres DB2 MongoDB ReThinkDB Redis DataWorks Apache Kafka Streams Big Data Analytics – Reference Architecture
- 19. Example – Ford: Integrated Health Management Platform 19 Information Ingestion Analytic Sources Metadata Catalog Insight Hub Activity Hub Content Hub Master & Reference Data Hubs Information Interaction Vehicle Device Sensors Dongle Information from Parking Spots Vehicle & User Information Maintenance History Old and New Sources Streams Cloudant Predictive Analytics
- 20. Example – Integrated Health Management Platform 20 Information Ingestion Analytic Sources Metadata Catalog Insight Hub Activity Hub Content Hub Master & Reference Data Hubs Information Interaction Clinical and Wearable Device Sensors Fitbit, Jawbone Device Data Lab Results and Patient Conversations Health Records from RDBMS Old and New Sources DataWorks Streams DashDB Cloudant D3
- 21. Has Been BridgedHas Been Bridged The GapThe Gap
Notas del editor
- Varacity – accruacy, precision, reliability, provenance, fidelity, perrmission
- From the viewpoint of health outcome determinants, almost 60% of data are exogenous, and never captured as part of EMR systems now. Inserting IBM in the dataflow, and enabling the generation/capture of this exogenous data is crucial for any emerging health ecosystems. Two important aspects of this data that plays directly to IBM strengths: traditional “big data” characteristics – volume, velocity, variety all data is generated in uncontrolled environments (that is, no hospital or supply side control) – highly fragmented value chain that needs a neutral entity that can collect, store, manage, curate, analyze data for insights.
- Revolution is about managing disruption when warranted and pushing your organization towards maxmized throughput when it isn't. 1. 400 Variations of the same jobs at IBM – Linkedin. If you are not thinking about how to keep your data clean you are screwed I guarantee it. 2.To the user. If you give them too much information, they will be in paralsis. Linked in- who viewed you. 3. Never try to launch a complicated data product on a fixed schedule. 4. Every single company I've worked at and talked to has the same problem without a single exception so far — poor data quality, especially tracking data,” he says.“Either there's incomplete data, missing tracking data, duplicative tracking data.” 5. A. Product has to Work B. Has to work for the user, makes sense to them. C.Has to Feel Safe, not creepy D. User needs to feel in control
- Responsiveness means being sensitive to which pieces of data are best suited for analysis, and conscious of the level of data quality and speed required to remain agile.
- Resiliency means leveraging the elasticity of the cloud to best augment your internal capabilities, giving your company the ultimate in staying power. Enterprise applications already hosted in the cloud: If, like many organizations -- especially small and midmarket businesses -- you use cloud-based applications from an external service provider, much of your source transactional data is already in a public cloud. If you have deep historical data on that cloud platform, it might already have accumulated in big data magnitudes. To the extent the service provider or one of its partners offers a value-added analytics service -- such as churn analysis, marketing optimization, or off-site backup and archiving of customer data -- it might make sense to leverage that rather than host it all in-house. High-volume external data sources that require considerable preprocessing: If, for example, you're doing customer sentiment monitoring on aggregated feeds of social media data, you probably don't have the server, storage, or bandwidth capacity in-house to do it justice. That's a clear example of an application where you'd want to leverage the social media filtering service provided by a public-cloud-based, big-data-powered service. Tactical applications beyond your on-premises, big data capabilities: If you already have an on-premises big data platform dedicated for one application (such as a dedicated Hadoop cluster for high-volume ETL on unstructured data sources), it might make sense to use a public cloud to address new applications (say, multichannel marketing, social media analytics, geospatial analytics, query-able archiving, elastic data-science sandboxing) for which the current platform is unsuited or for which an as-needed, on-demand service is more robust or cost effective. In fact, a public cloud offering might be the only feasible option if you need petabyte-scale, streaming, multistructured, big data capability ASAP. Elastic provisioning of very large but short-lived analytic sandboxes: If you have a short-turnaround, short-term data science project that requires an exploratory data mart (aka sandbox) that's an order-of-magnitude larger than the norm, the cloud may be your only feasible or affordable option. You can quickly spin up cloud-based storage and processing power for the duration of the project, then just as rapidly deprovision it all when the project is over. I call this the "bubble mart" deployment model, and it's 3. You can access data anywhere With business trips, outsourcing, and branching out to new markets, your company needs big data available at any location. You could spend the time, money, and manpower to set up an elaborate VPN, but the cloud already comes with built-in secure global access. Even if you don't need it now, as your company expands, some of the work may need to be outsourced, and new offices could open on the other side of the globe. The cloud is perfect for these global needs. 4. Pay as you go It's simple: you only pay for the resources you actually use. On quiet days, things will be on the cheaper side, on crazy days with sharp usage spikes, you'll pay the right price. No more, no less. Consider the alternative -- buying super-expensive hardware to handle a sudden rise in demand. Most of the time the hardware will gather dust, its capacity barely used, putting the big bucks spent on it to waste. Add maintenance prices, and you'll soon realize that using big data in the cloud saves money on system resources and directs it where your company actually needs it. Resource pooling: Cloud architectures enable the efficient creation of groups of shared resources that make the cloud economically viable. Self-service: With self-service, the user of a cloud resource is able to use a browser or a portal interface to acquire the resources needed, say, to run a huge predictive model. This is dramatically different than how you might gain resources from a data center, where you would have to request the resources from IT operations. Pay as you go: A typical billing option for a cloud provider is Pay as You Go, which means that you are billed for resources used based on instance pricing. This can be useful if you’re not sure what resources you need for your big data project. Fault tolerance: Cloud service providers should have fault tolerance built into their architecture, providing uninterrupted services despite the failure of one or more of the system’s components. The technical stack an enterprise chooses is dictated by the type of data they need to store, and the type of data is dictated by business requirements. The RDBMS is good for managing structured, highly relational data and will continue to be the software of choice for many requirements. For the growing amount of unstructured data produced by social media, sensor networks, and federated analytics data-and for constantly changing data that needs to be replicated to other operating sites or mobile workers-NoSQL technologies better fit those use-cases. Unstructured data can be terabytes or even petabytes in size.
- The IBM DataWorks™ data refinery transforms raw data into relevant information. It includes IBM DataWorks Forge, an app primarily for knowledge workers, as well as APIs for application developers. IBM DataWorks leverages a highly performant and scalable engine to discover, profile, enrich, mask and deliver data to applications. Forge (Beta) A data-rich app that empowers knowledge workers - including business analysts, data scientists and non technical users - to find data, visualize it, and prepare it for use. By automatically profiling, classifying, and scoring data, Forge guides you through the process of enriching and improving the quality of data using actions such as removing duplicates, filtering and joining. After you prepare and enrich your data, Forge makes it easy for you to deliver data to applications and systems. APIs Flexible, REST-based APIs enable developers to quickly access data and ensure it is fit for purpose. Using the IBM DataWorks APIs, you can quickly create higher-quality applications that load data between data sources (such as SQL Database, Object Storage, dashDB, IBM Analytics for Hadoop, DB2 and Oracle); mask data while loading; securely load on-premises data to cloud environments; cleanse US postal addresses; and classify and profile data. Streaming Analytics is powered by IBM InfoSphere® Streams, an advanced analytic platform that you can use to ingest, analyze, and correlate information as it arrives from data sources in real time. When you create an instance of the Streaming Analytics service, you get your own instance of InfoSphere Streams running in the Bluemix cloud, ready to run your InfoSphere Streams applications. You can use the Streaming Analytics service in two ways: Interactively by using the Streaming Analytics console. Programmatically in the context of a Bluemix application by using the Streaming Analytics service instance REST API. You can also combine these two methods. Your Bluemix application can use the service programmatically, while you use the console to monitor the status of your applications. Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. Fast A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Scalable Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers Durable Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.
- Data storageStored in a relational model, with rows and columns. Rows contain all of the information about one specific entry/entity, and columns are all the separate data points; for example, you might have a row about a specific car, in which the columns are ‘Make’, ‘Model’, ‘Colour’ and so on.The term “NoSQL” encompasses a host of databases, each with different data storage models. The main ones are: document, graph, key-value and columnar. More on the distinctions between them below. Schemas and FlexibilityEach record conforms to fixed schema, meaning the columns must be decided and locked before data entry and each row must contain data for each column. This can be amended, but it involves altering the whole database and going offline.Schemas are dynamic. Information can be added on the fly, and each ‘row’ (or equivalent) doesn’t have to contain data for each ‘column’. ScalabilityScaling is vertical. In essence, more data means a bigger server, which can get very expensive. It is possible to scale an RDBMS across multiple servers, but this is a difficult and time-consuming process.Scaling is horizontal, meaning across servers. These multiple servers can be cheap commodity hardware or cloud instances, making it alot more cost-effective than vertical scaling. Many NoSQL technologies also distribute data across servers automatically. Apache Spark is the shiny new toy on the Big Data playground, but there are still use cases for using Hadoop MapReduce. Spark has excellent performance and is highly cost-effective thanks to in-memory data processing. It’s compatible with all of Hadoop’s data sources and file formats, and thanks to friendly APIs that are available in several languages, it also has a faster learning curve. Spark even includes graph processing and machine-learning capabilities. Hadoop MapReduce is a more mature platform and it was built for batch processing. It can be more cost-effective than Spark for truly Big Data that doesn’t fit in memory and also due to the greater availability of experienced staff. Furthermore, the Hadoop MapReduce ecosystem is currently bigger thanks to many supporting projects, tools and cloud services. But even if Spark looks like the big winner, the chances are that you won’t use it on its own—you still need HDFS to store the data and you may want to use HBase, Hive, Pig, Impala or other Hadoop projects. This means you’ll still need to run Hadoop and MapReduce alongside Spark for a full Big Data package. The notion of running a data warehouse in the cloud was a pretty novel thing when Amazon Web Services launched its Redshift service in November of 2012. Most on-premises data warehouse (DW) platforms are appliance-based, which makes them difficult to expand, and the resulting need to leave room for growth also makes them expensive to acquire. In the cloud though, economics are better, elasticity is realistic and logistics are streamlined. Combine that with the ability to handle "big data" volumes with the familiar SQL/relational model that Redshift uses and it's hardly surprising that the service has been one of Amazon's fastest growing since its launch.
- Company Background Ford Motor Company is an American automaker based in Dearborn, Michigan, a suburb of Detroit, the automaker was founded by Henry Ford, on June 16, 1903. As the fifth-largest automobile company in the world, Ford Motor Company represents a $164 billion multinational business empire. Known primarily as a manufacturer of automobiles, Ford also operates Ford Credit, which generates more than $3 billion in income. In London, Ford is working to make parking easier for drivers and the city. Drivers voluntarily use plug-in devices that create live data on traffic and parking. The City Dash app tells users whether they are legally parked. If not, the app recommends the nearest open spot. It allows drivers to pay for parking meters by mobile phone, and identifies the closest available parking spots to the driver’s final destination. Success Criteria Online/offline sync & replication critical Data needs to reside on the device and in the cloud and be available regardless of connectivity. Advanced Geo Spatial capability Ford needs to be able to help drivers find parking the nearest open relevant parking space. Solution & Results Cloudant’s advanced geospatial capabilities Ford can help drivers find open parking spots in London where parking is at a premium and only show relevant spots instead of simply the nearest which may not be easy to get to. With Cloudant’s Replication and Sync Capabilities Ford can be certain the app will function even if the is a loss of cellular signal. Ford is also installing numerous sensors in their cars to monitor behaviour. They install over 74 sensors in cars including sonar, cameras, radar, accelerometers, temperature sensors and rain sensors. As a result, it Energi line of plug-in hybrid cars generate over 25 gigabytes of data every hour. This data is returned back to the factory for real-time analysis and returned to the driver via a mobile app. The cars in its testing facility even generate up to 250 gigabytes of data per hour from smart cameras and sensors. SHARED OPERAITONAL INFORMATION Vehicle registration Vehicle Management User Registration Usage record Maintenance history Utilizations ANALYTICS Driving behavior Trajectory Origin / Destination Pattern Analytics
- A Boston based holding company, to be capitalized with $25 MM and established for the purpose of investing into a portfolio of North American healthcare companies, to create capabilities necessary for an Integrative Disease Management Platform focused primarily on Metabolic and Chronic Diseases. A patient, Sarah, is being monitored for cardio vascular disease (CVD) and has been diagnosed with hyperlipidemia (high cholesterol) and has authorized her provider to access the data being gathered by her fitbit. Her provider has decided to not prescribe a statin and treat her with increased monitoring, a nutritionist, and to begin increasing her physical activity level via step goals with a Fitbit. The provider has prescribed a lipid panel be performed every 3 months to monitor progress. At the 6th month of increased physical activity and visits with the nutritionist the Application has been collecting data about adherence to attending scheduled visits and the fitbit data has been aggregated. When the lab results for her lipid panel come back normal for the first time, the system alerts the physician that his patient is doing well! Logging into his application he then triggers the app to send a notification with a personal message to his patient to interpret the numbers for her and reinforce that her behavior change is having an impact. Provider Console Patient Console provider console The application offers an integrated experience for the provider to see EHR / Lab / Patient social device data. Example Functionality to be spec’d out:risk notifications, adherence algorithms, provider/patient communication (electronic/direct mailing). Provider is also given historical reporting to follow patient progress (fed by EDW). The application offers ability for patients to authorize the integration of personal devices to providers. Integration of EHR/Labs/device data. Alerts / Communication with provider. Mobile App Experience fitted to the smaller screen.
- • Well, today the gap has been bridged… • You can meet your goals • Capture the opportunity...