Oracle big data publix sector 1
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Intervento Sergio Fiora Oracle Italia – Technology Business Unit 13 settembre 2017 Open days "Data &analytics framework"
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Oracle big data publix sector 1
- 1. Converting Big Data into Economic Value for Public Sector Sergio Fiora Business Development Local Gov. & Healthcare Oracle Italia – Technology Business Unit Bari 12 Settembre 2017 Fiera del Levante, Pad. 152 Regione Puglia Sala convegni
- 2. The Rise Of Data Capital 1. Data is now a kind of capital 2. Companies & organizations must execute new strategies to compete 3. Data needs to be secured and invested like the economic capital
- 3. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | UseData ProduceData Things ProcessesThoughts Datification of Everithing raises the Data Capital Converting Data into Value is the real challenge
- 4. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Analytics 2.0 Analytics 3.0 Analytics 1.0 • Basic reporting • Limited range of tabular data • Batch oriented analysis • Analysis bolted onto limited set of business processes • Busines Intelligence • Report self service – Graphycs – Geospatial • Data warehousing • Platform for all data • Deeper analysis on more data • Faster test-do-learn • wider business process coverage • Analysts focus on discovery and driving business value Adapted from Tom Davenport Aligning analytical requirements and architecture Enabling Analytics 3.0 with a pragmatic architecture . Big data firms • Extended analytics to larger and less structured datasets for search engine • New infrastructures/ data scientist
- 5. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | A change of paradigm in Information Consumption 6 From “Business Intelligence” ... • Known and structured access patterns to structured information • Analytics activity in «delayed mode» vs data generation and preparation time ... To “Business Inspiration” • IT central role in guidance and skills • High complexity in data management makes analytical tools a secondary priority • Free-hand, Search oriented information access based on new business models • Real Time analytic activity • New roles and skill are leading: Data Officers, Data Scientists, Data Analysts • Optimized usability for business-wise users is first priority A “bi-modal” approach to Business Analytics
- 6. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 7 Points of emphasis
- 7. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Unlocking the Value of Fast Data Volume, Velocity and Variety Time BusinessValue Engine STOP HOT Warning – maintenance required WARM Piece of trim gone: note for future development COLD
- 8. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | • The global first implementation of a city wide “smart” parking management system and technology to manage parking supply and demand more intelligently. • Sensors at 8,200 of the city’s 27,000 metered parking spaces, to get information from the gate arms at the city’s 14 garages, which among them have about 13,000 spaces Smart City: Parking Management San Francisco Park
- 9. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Sfpark - San Francisco, USA City Operation Business Intelligence demand driven pricing of parkingspaces City Infrastructure Loading sensor data on parking availability, payments, bus arrivals Business Productivity 3rd party service providers enhance real time parking data Illustrations © Sfpark SF Municipal Transport Agency Sustainable City Streets less congested, less violations less smog and safer Citizen Empowerment Less traffic, fewer citations, guides drivers to parking lots and bus
- 10. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 11 Beware Black-boxed approaching
- 11. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | EXPERIMENT ANALYZE & ACT Manage, secure and make all data available Connect people to information they need Innovation through data experiments and advanced analytics Transform workplace and workforce through insights AGGREGATE MANAGE 12Confidential – Oracle Internal/Restricted/Highly Restricted
- 12. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | NHS BSA • Responsible for a third of the NHS budget • Manages prescription reimbursement • Delivery of supply chain services to the NHS • NHS Pensions Challenges • 4 million prescriptions processed/day • 30%+ entered manually • Need to find drugs misuse and fraud & error • Unable to monitor best practice (drug administration versus outcomes at national level) • Inability to link structured and unstructured data together
- 13. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal Preventing Fraud for European Health Insurance Card Analyzing Billions of Records in Minutes (prescription) analyzing much larger sets of patient data, the NHSBSA can provide insight that is helping to improve standards of care Analyzing Unstructured Text to Measure Satisfaction DALL - Data Analytics Learning Laboratory Data Scientist, Data Consultant , Statisticians ,Data Lab Coordinator , Information and Data Analyst Team initially supported by Oracle experts
- 14. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal Preventing Fraud for European Health Insurance Card Analyzing Billions of Records in Minutes (prescription) analyzing much larger sets of patient data, the NHSBSA can provide insight that is helping to improve standards of care Analyzing Unstructured Text to Measure Satisfaction DALL - Data Analytics Learning Laboratory Data Scientist, Data Consultant , Statisticians ,Data Lab Coordinator , Information and Data Analyst Team initially supported by Oracle experts Our target for 2015/16 is to highlight at least £200 million of potential savings for the NHS through the DALL. The thermometer below shows what : The DALL, however, isn’t purely about saving money as we can also provide valuable insight into patient care, safety, probity and quality within the NHSBSA and wider NHS.
- 15. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | A change of paradigm in Information Management 16 Build a Data Reservoir to serve as an enabler for more powerful DWH’s Provide all tools needed to get value out of the Data Reservoir Empower business Users to get value from Big Data (not only geeks or data scientists) Data Warehouse Existing Sources Emerging Sources + Existing not used internal sources Data ReservoirData Warehouse
- 16. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 17 Current Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 17 Enterprise Historical Necessary, but no longer sufficient Public Data Vendor Supplied Customer-Generated New data is stored in Data Lakes ALL DATA Success requires organizations to productively and efficiently work with ALL data
- 17. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Oracle Business Analytics - Analisi Geospaziali
- 18. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Oracle Business Analytics - Analisi Geospaziali
- 19. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Oracle Business Analytics - Analisi Geospaziali
- 20. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Platform for Big Data Organize Experiment Operationalize Process GovernStoreManage Machine Learning Graph Spatial Public Streaming Enterprise Data Services Business Analytics Enterprise Apps Prepare AnalyzeData Providers Data Consumers Data Infrastructure Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 21
- 21. 22 • Managed or Automated • Cloud@Oracle or Cloud@Customer • Choose your distribution Big Data Cloud Machine Oracle Cloud machine Public Cloud Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 22 Choice
- 22. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | BIG DATA Manage and secure all data Connect people to information Innovate with experimentation Transform business through insights Integrated Platform 23Confidential – Oracle Internal/Restricted/Highly Restricted
- 24. Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 25 Data Comes from Activity Big Data, as a Global Phenomenon, Is Disrupting IndustriesPROCESSES THINGS PEOPLE
- 25. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Data Lab To Find Savings and Cost Reductions in Health Care Budget • United Kingdom’s National Health Service • Identify billing and identity fraud • Optimize treatment by reducing use of less effective medical procedures • Deployed Oracle Advanced Analytics, and Oracle Business Intelligence on Oracle Exadata and Oracle Exalytics Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Potential savings identified “With one vendor providing the whole solution, it’s very easy for us.” - Nina Monckton, NHS BSA $156M 26Confidential – Oracle Internal/Restricted/Highly Restricted
Editor's Notes
- TALKTRACK I am almost one year into my time here at Oracle and I’ve seen some big transformations this company has made to launch itself into the Cloud – and it’s one of the reasons I joined. Today I want to talk about a transformation in the form Big Data & Cloud both which are now coming together in ways I think you will find compelling. And it’s really an important story that I want to tell about how we got here, specifically… WHO WE ARE WHAT WE DO WHAT IT MATTERS And WHY ORACLE FOR BIG DATA & CLOUD
- I dati come capitale sono sempre stati un element fondamentale delle aziende, nulla di nuovo salvo il fatto che I dati nell’ultimo period sono cresciti in volume e tipologia. Se è vero che come sostengono alcune analisi , nel solo settore sanitario I dati stanno crescendo del come volume 50% anno su anno , è vero che stanno cambiando la tipologia grazie alle nuove tecnologie IoT, Mobile, Social. Questo comporta pero’ un adeguamento dei sistemi di gestione e manipolazione di questi dati. Data is a new capital: like financial capital, it is a resource that needs to be managed, stored and secured and also, very much like financial capital, it needs to be invested and used to gain a competitive edge. Data isn’t a new resource, but it is now, for the first time, both abundant and harnessed. Electricity was a curiosity in the lab for a long time. But when it became widely available to the masses, it changed the industry. Companies that will understand and embrace this revolution first, will gain a competitive advantage and will win
- Uso di queste nuove fonti informative e’ ancora sottodimesionato il tutto dovuto a diverssi fattori: dati/strutturati e non, capacità elaborativa, e capacità di analisi. But only if you can use all that data productively. What we’re seeing is that while we are creating and sometimes collecting mountains of data, our ability to produce it has far outstripped our ability to use it According to a study we conducted with The Economist Intelligence Unit, only 12% of executives feel they understand the impact data will have on their organizations over the next three years.” (Source: http://www.oracle.com/webapps/dialogue/ns/dlgwelcome.jsp?p_ext=Y&p_dlg_id=13367869&src=7634271&Act=143 ) The same is true for many businesses: the information they need to improve products and services already exists, they’re just not quite sure how to use it.
- Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy. Today it isn’t just online and information firms that can create products and services from analyses of data. It’s every firm in every industry LinkedIn, for example, has created numerous data products, including People You May Know, Jobs You May Be Interested In, Groups You May Like, Companies You May Want to Follow, Network Updates, and Skills and Expertise. To do so, it built a strong infrastructure and hired smart, productive data scientists. Google, Amazon, and others have prospered not by giving customers information but by giving them shortcuts to decisions and actions. Thus, the competencies required for Analytics 2.0 were quite different from those needed for 1.0. The Bosch Group, based in Germany, is 127 years old, but it’s hardly last-century in its application of analytics. The company has embarked on a series of initiatives across business units that make use of data and analytics to provide so-called intelligent customer offerings. These include intelligent fleet management, intelligent vehicle-charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. To identify and develop these innovative services, Bosch created a Software Innovations group that focuses heavily on big data, analytics, and the “Internet of Things.” Schneider Electric, a 170-year-old company based in France, originally manufactured iron, steel, and armaments. Today it focuses primarily on energy management, including energy optimization, smart-grid management, and building automation. It has acquired or developed a variety of software and data ventures in Silicon Valley, Boston, and France. Its Advanced Distribution Management System, for example, handles energy distribution in utility companies. ADMS monitors and controls network devices, manages service outages, and dispatches crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use visual analytics to understand the state of the network. One of the most dramatic conversions to data and analytics offerings is taking place at General Electric, a company that’s more than 120 years old. GE’s manufacturing businesses are increasingly becoming providers of asset and operations optimization services. With sensors streaming data from turbines, locomotives, jet engines, and medical-imaging devices, GE can determine the most efficient and effective service intervals for those machines. To assemble and develop the skilled employees needed for this work, the company invested more than $2 billion in a new software and analytics center in the San Francisco Bay area. It is now selling technology to other industrial companies for use in managing big data and analytics, and it has created new technology offerings based on big data concepts, including Predix (a platform for building “industrial internet” applications) and Predictivity (a series of 24 asset or operations optimization applications that run on the Predix platform across industries). UPS, a mere 107 years old, is perhaps the best example of an organization that has pushed analytics out to frontline processes—in its case, to delivery routing. The company is no stranger to big data, having begun tracking package movements and transactions in the 1980s. It captures information on the 16.3 million packages, on average, that it delivers daily, and it receives 39.5 million tracking requests a day. The most recent source of big data at UPS is the telematics sensors in more than 46,000 company trucks, which track metrics including speed, direction, braking, and drivetrain performance. The waves of incoming data not only show daily performance but also are informing a major redesign of drivers’ routes. That initiative, called ORION (On-Road Integrated Optimization and Navigation), is arguably the world’s largest operations research project. It relies heavily on online map data and optimization algorithms and will eventually be able to reconfigure a driver’s pickups and deliveries in real time. In 2011 it cut 85 million miles out of drivers’ routes, thereby saving more than 8.4 million gallons of fuel.
- Thomas Hayes "Tom" Davenport, Jr. (born October 17, 1954) is an American academic and author specializing in analytics, Many of you will be familiar with the work of Tom Davenport from HBR article Provides a useful model for defining capability He observed how some organisations, those that spent more … oriented by fact Outperformed the market considerably He coined this analytics 2.0 More recently he has extended this ideas Analytics 1.0-L'era della "business intelligence". Analytics 1.0 : Nuove competenze erano tenuti così, a cominciare con la capacità di gestire i dati. I set di dati erano abbastanza piccolo in volume e abbastanza statica di velocità per essere segregata in magazzini per l'analisi. Tuttavia, preparando una serie di dati per l'inclusione in un magazzino era difficile. Gli analisti hanno trascorso gran parte del loro tempo a preparare i dati per l'analisi e relativamente poco tempo sull'analisi stessa. grande maggioranza di reporting di business intelligence per attività rivolte solo quello che era successo in passato; hanno offerto spiegazioni o previsioni. .......maggiori efficienza operativa per fare decisioni migliori su alcuni punti chiave per migliorare le prestazioni. Analytics 2.0 l'era delle grandi dati. Le condizioni di base dei Analytics 1.0 periodo predominava per mezzo secolo, fino a metà degli anni 2000, quando le imprese basati su internet e social network in primo luogo nella Silicon Valley-Google, eBay, e così via, hanno cominciato ad accumulare e analizzare nuovi tipi di informazioni Grandi i dati anche venuto a essere distinto da piccola dati in quanto non è stato generato dai sistemi di transazione puramente interni di un'azienda Hanno attirato gli spettatori ai loro siti web attraverso migliori algoritmi di ricerca, le raccomandazioni da amici e colleghi, suggerimenti per i prodotti da comprare, e gli annunci, tutti guidati da analisi radicate in enormi quantità di dati altamente mirati. Analytics 3.0-epoca di offerte di dati arricchito. Analytics 3.0 segna il punto in cui le altre organizzazioni di grandi dimensioni hanno iniziato a seguirne l'esempio Ogni dispositivo, la spedizione, e il consumatore lascia una traccia. Digitale. Una nuova serie di opzioni per la gestione dei dati. Nel 1.0 dell'epoca, le imprese utilizzate data warehouse come base per l'analisi. Nell'era 2.0, si sono concentrati sui cluster Hadoop e database NoSQL. Oggi la risposta è la tecnologia "tutto quanto sopra": data warehouse, database e grandi elettrodomestici di dati, gli ambienti che uniscono ricerca di dati tradizionali approcci con Hadoop (questi sono a volte chiamati Hadoop 2.0), banche dati e verticali del grafico, e altro ancora. quasi tutte le organizzazioni si concluderà con un ambiente di dati ibrido Ci sono sempre stati tre tipi di analisi: descrittiva, che riporta al passato; predittivo, che utilizza modelli basati su dati passati per predire il futuro; e prescrittivo, che utilizza modelli per specificare i comportamenti e le azioni ottimali. Anche se Analytics 3.0 include tutti e tre i tipi, sottolinea l'ultimo. modelli prescrittivi prevedono test su larga scala e l'ottimizzazione e sono un mezzo per incorporare analisi in processi chiave e comportamenti dei dipendenti. Essi forniscono un elevato livello di prestazioni operative ma richiedono pianificazione di alta qualità e l'esecuzione in cambio.
- Punti di attenzione
- This leads us to a key element of the Oracle Strategy for IoT; the nature of the data drives the process and the action. The types and data we are treating are set to diversify and grow rapidly and it’s important to identify that which is important now and that which may have significance later. Talk about difference between event driven data where value decays over time and analytics driven data where value increases with time and volume. This aligns with the broader Oracle proposition of “real-time” data Talk about difference between data in motion: hot data (do something now), warm data (you can continue but we need to start a trouble ticket) data at rest: cold data; value will be realized in time Our goal is to provide technologies and processes that ensure the customer maximises the value of their data; ie above the dotted line
- Challenge SFPark is a classic Internet of Things use-case for how cities are getting smarter by using physical devices connect to a virtual world over an information network. In San Francisco, San Francisco Municipal Transport Association (SFMTA) could not build more roads as the city simply did not have any more space. The city had to find ways to enable San Francisco city public transportation system to operate faster with increased reliability and accommodate the anticipated future trip growth. As San Francisco’s parking supply is a valuable and a limited public asset, the SFMTA had to manage parking effectively through intelligent parking management approaches. The goals for the SFPark project included improving parking convenience by making it easier to park & pay, thereby improving traffic flow for improving Muni (San Francisco city public transportation system) and enabling demand responsive pricing to reduce circling and double parking. Oracle SOA, Oracle Service Bus and Oracle’s BI based solution helped meet these goals. Solution SFPark includes use of innovative and leading edge technology, including parking sensors, new and improved meters, garage data occupancy sensors and roadway sensors for analyzing traffic flow and measuring the impact of smart parking policies on traffic, etc. The scalability and performance including fault tolerance afforded by the Oracle solution helps the project function 24x7 with minimal support requirements. A Service Oriented Architecture (SOA) based approach enables standards based implementation using loosely coupled services and interfaces. SOA helps quickly on-board various vendors and partners while providing them with the flexibility to use their in-house technology and not worrying too much about integrating with existing systems. This enables the overall system to be open, flexible, and scalable enough to accommodate additions and likely future growth in magnitude and complexity of data, number of data sources, and type of data sources. The solution also leverages Oracle Service Bus and Web Services to provide information via XML feed data to various external vendors. Data warehouse (DW) provides analytical and trend reporting of the data points collected. Oracle Service Bus (OSB) provides the backbone for communication of messages such as real-time occupancy of publicly available parking spots, pricing information from vendors etc. OSB performs message transformation, and error handling for HTTP/JMS/FTP/Email type messages via SOAP, XML, Text, Binary etc. Web Services helps relay information to multiple external systems (SFMTA Website, SFMTA Message Signs, Text Messaging Service etc). The Operational Data Store (ODS) communicates with the OSB to collect this data in real-time. The Oracle Data Integrator (ODI) loads the batch data and transform ODS data into data warehouse star schema. The data analysis is handled by Oracle Business Intelligence Enterprise Edition (OBIEE) to help review the vast amount of real time data that is being collected from various sources. The visual mapping rendering of the available spots is accomplished using MapViewer. The OBIEE solution improves efficiency and accuracy to initiate demand responsive pricing changes and meter operational schedule updates for improved Muni operations and reduced congestion. This results in improved city transportation and better experience for all using city roads and transportation services. Business Impact SFpark rolled out this new parking management system at 7,000 of San Francisco’s 28,800 metered spaces and 12,250 spaces in 15 of 20 City-owned parking garages, reducing traffic by helping drivers find parking. Meters that accept credit and debit cards helped reduce frustration and parking citations. Furthermore, demand responsive pricing helped encourage drivers park in underused areas and garages, reducing demand in overused areas. The return on investment for the SFPark project was not just monetary. The project provides for improved Muni operations and reduced congestion thereby increasing citizen satisfaction with city transportation and improved air quality and better experience for all using the city roads and transportation services. Here’s an actual use case of the Internet of Things in action: City Governments are working to make their services more efficient and effective through an initiative called Smart City. Smart City’s goals are to: Gather real-time data from various services, applications and device end points throughout city Use analytics to find areas to improve efficiency and effectiveness of city services Implement programs toward those improvements to reduce carbon footprint and improve the overall quality of life for its citizens One such program is SF Park, a parking management project that dynamically balances the supply of much sought after parking spaces in San Francisco with changes in demand. By analyzing parking use and traffic patterns, rates in vacant lots can be adjusted in real-time, directing traffic flow to areas with available parking spots. This reduces driver frustration (from searching for parking), reduces traffic congestion as well as greenhouse gas emissions.
- Talktrack: I want to walk you through just what we believe are the key elements to a Big Data strategy. There are three elements I want to talk about first. It’s about connecting people to the information no matter what the source – we call this step collecting It’s about Managing information – making it secure and available It’s about analyzing and acting on this information which helps transform the business through new insights Click But there’s also one more element. It’s also about innovating and experimenting on the data, that’s an important step that helps us drive even greater advancements in understanding of our information. Ultimately these steps are iterative. You need the freedom to work with data as you see fit, and as your analysis leads you in different directions. You’re going to jump around inside this process and they all need to work together well. So let’s look at these in a little more detail… Original While there are a lot of moving parts, there is a simple way to start thinking about this. You need to bring in the information you need, you need to manage it so that it’s usable by whoever needs access. And the right analytics on that data will enable the kind of transformations we’ve talked about. It’s not 1, 2, 3 and you’re done. It’s an iterative process. You need the freedom to work with data as you see fit, bringing in new data sources as your analysis leads you in different directions. You’re going to jump around inside this process and they all need to work together well. Let’s drill down a little further.
- The NHS budget for 2015/16 is GBP116 billion and the total funds administered by the NHSBSA amount to circa GBP32 billion, Manages prescription reimbursement The Department of Health asked identify opportunities to reduce costs and eliminate waste. Use the vast volumes of data already collected and held within the organization to help reduce fraud For the DALL there are many elements to analytics, some work is around patient improvement, others patient safety and also financially identify money that can be at risk with recommendations on how that money could be released back into the wider NHS. For the financial year 2015/16 we were tasked with looking for £200 million that could be identified as potential savings for the BSA and the wider NHS. To date we’ve identified £146 million of potential savings. We’re now working with the service departments and external bodies to realise the potential savings that we have identified.
- Qs slide deve rappresentare perche oracle è meglio di tutti nei Big Data Cenno agli investimenti di orcl nel BD 150 devs per sviluppare il BDD
- This slide looks complex, but it really has just three stages (and two clicks in the build) How things used to be (where database was king) How things are now (as organizations want to use more data and data lakes) Reminder that it’s all data (not just the new stuff). This slide looks complex, but it really has just three stages: Part 1 Data management used to be a much simpler affair. Current enterprise data was stored in a relational database (data warehouse) that was the foundation for running the business. This stuff remains essential to the business (try closing your books without something like that to support your finances), but increasingly as there’s more data around, it’s not enough. Part 2 - CLICK Because as more data is available, potentially providing new insights into customers, suppliers, partners and so on, new technologies have emerged to manage them. The key concept is the idea of a data lake, typically based on Hadoop, that can capture this diversity of new data cost effectively. Part 3 - CLICK There’s a temptation to look at this as either/or. That’s not what’s needed. As the bullet says, success requires all organizations to …. Transition to next slide So let’s have a look at how organizations can make use of more data. Three main ways.
- Big data can seem very complex. There are dozens of companies with hundreds of products playing some kind of role to uncover the value in big data. Oracle’s own portfolio is... well let’s call it extensive. But if we break this down, maybe it’s not so complex. It starts with the data providers on the left. You’ve always had data in your enterprise. With big data it’s just a matter of adding more sources, from public data sets to streaming data from sensors and much more. CLICK (note to presenter: if you prefer to do this in less detail, edit the slide to remove the animation with extra information on the three middle circles) Next we have three interlinked components. You need infrastructure to store and process, and to manage and govern all that data. You need to be able to prepare it, which means organizing it, experimenting with it, and doing what’s needed to get it ready for production use. And finally, you need to be able to run sophisticated analytics on it, to uncover new insights that can change the way you do business. CLICK (to remove the extra information) Finally, the results of this preparation and analysis need to be made available to the data consumers in your organization: the people, applications and services that are actually responsible for taking action. Rather than details products, I want to talk about what this means for you. What kind of things should you be able to do with a solid big data solution.
- Talktrack TBD: Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. · It’s about Connecting people to information through tools which help you combine and aggregate data from any source · We provide best-in class capabilities for managing and securing all data – making your applications run optimally, securely · We enable tools to experiment on data which leads to greater innovation on · And it’s about transforming the business through insights. Only Oracle offers this level of breath in offerings with the choice of on-premises or in the Cloud. Only Oracle has a Complete Big Data solution which is integrated together so you can focus on what matters.
- SUMMARY: Coal, sunshine, and water can be harnessed to generate electricity, a very useful resource. Likewise activity generates data, the newest very useful resource. ------------------------------------- Edison worked primarily with electricity, which was, in his day, the new resource disrupting industries. [CLICK] Today, that industry-disrupting new resource is data, both internal and external, created by things, people or processes. Not long ago, an activity like a cab ride meant you hailed one on the street, told the driver your destination, paid in cash. No data. Today, you use your app, track the route via GPS, pay with a credit card and rate the driver on social media. All three types of data are created by that one activity.
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