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Big Data: It’s all about the Use Cases
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Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
James SerraSuivre
Big Data/Data Warehouse Evangelist at Microsoft à MicrosoftPublicité
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Big Data: It’s all about the Use Cases
- Big Data: It’s all about the use cases James Serra Big Data Evangelist Microsoft JamesSerra3@gmail.com
- About Me Business Intelligence Consultant, in IT for 30 years Microsoft, Big Data Evangelist Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM architect, PDW/APS developer Been perm, contractor, consultant, business owner Presenter at PASS Business Analytics Conference and PASS Summit MCSE: Data Platform and Business Intelligence MS: Architecting Microsoft Azure Solutions Blog at JamesSerra.com Former SQL Server MVP Author of book “Reporting with Microsoft SQL Server 2012”
- Use Cases (theory) Use Cases (practice) Popular Technologies
- Popular Technologies
- Harness the growing and changing nature of data What is Big Data? StreamingStructured Challenge is combining transactional data stored in relational databases with less structured data Big Data = All Data Get the right information to the right people at the right time in the right format Unstructured “ ”
- Connectivity Data AnalyticsThings IoT = sensor-acquired data
- Using a Data Lake Modern Architecture All data sources are considered Leverages the power of on-prem technologies and the cloud for storage and capture Native formats, streaming data, big data Extract and load, no/minimal transform Storage of data in near-native format Orchestration becomes possible Streaming data accommodation becomes possible Refineries transform data on read Produce curated data sets to integrate with traditional warehouses Users discover published data sets/services using familiar tools CRMERPOLTP LOB DATA SOURCES FUTURE DATA SOURCESNON-RELATIONAL DATA EXTRACT AND LOAD DATA LAKE DATA REFINERY PROCESS (TRANSFORM ON READ) Transform relevant data into data sets BI AND ANALYTCIS Discover and consume predictive analytics, data sets and other reports DATA WAREHOUSE Star schemas, views other read- optimized structures
- What is Hadoop? Microsoft Confidential Distributed, scalable system on commodity HW Composed of a few parts: HDFS – Distributed file system MapReduce – Programming model Other tools: Hive, Pig, SQOOP, HCatalog, HBase, Flume, Mahout, YARN, Tez, Spark, Stinger, Oozie, ZooKeeper, Flume, Storm Main players are Hortonworks, Cloudera, MapR WARNING: Hadoop, while ideal for processing huge volumes of data, is inadequate for analyzing that data in real time (companies do batch analytics instead) Core Services OPERATIONAL SERVICES DATA SERVICES HDFS SQOOP FLUME NFS LOAD & EXTRACT WebHDFS OOZIE AMBARI YARN MAP REDUCE HIVE & HCATALOG PIG HBASEFALCON Hadoop Cluster compute & storage . . . . . . . . compute & storage . . Hadoop clusters provide scale-out storage and distributed data processing on commodity hardware
- Can I use the cloud with my DW? • Public and private cloud • Cloud-born data vs on-prem born data • Transfer cost from/to cloud and on-prem • Sensitive data on-prem, non-sensitive in cloud • Look at hybrid solutions
- MPP Logical Architecture “Compute” node Balanced storage SQL“Control” node SQL “Compute” node Balanced storage SQL “Compute” node Balanced storage SQL “Compute” node Balanced storage SQL DMS DMS DMS DMS DMS 1) User connects to the appliance (control node) and submits query 2) Control node query processor determines best *parallel* query plan 3) DMS distributes sub-queries to each compute node 4) Each compute node executes query on its subset of data 5) Each compute node returns a subset of the response to the control node 6) If necessary, control node does any final aggregation/computation 7) Control node returns results to user Queries running in parallel on a subset of the data, using separate pipes effectively making the pipe larger
- NoSQL databases • Non-relational databases (semi-structured data) • Types: Document, Key-value, Column, Graph • MongoDB, Cassandra, HBase, DocumentDB, Riak • Large-scale OLTP (i.e. popular web application) • Scale-out solution • High-availability • JSON data • Cons: data consistency, join data, use SQL, quick mass updates, skillset • Bad solution for a data warehouse, but can have a place in a big data solution • Polyglot Persistence: use the right tool for the job
- Use Cases (theory)
- Speed/Real-time Batch/Traditional Hybrid
- Modern Data WarehouseThe Dream
- The Reality
- Let’s set off light bulbs in your head
- Recommenda- tion engines Smart meter monitoring Equipment monitoring Advertising analysis Life sciences research Fraud detection Healthcare outcomes Weather forecasting for business planning Oil & Gas exploration Social network analysis Churn analysis Traffic flow optimization IT infrastructure & Web App optimization Legal discovery and document archiving Data Analytics is needed everywhere Intelligence Gathering Location-based tracking & services Pricing Analysis Personalized Insurance
- The Internet of Things – Manufacturing GLOBAL OPERATIONS I can see my production line status and recommend adjustments to better manage operational cost. I know when to deploy the right resources for predictive maintenance to minimize equipment failures and reduce service cost. I gain insight into usage patterns from multiple customers and track equipment deterioration, enabling me to reengineer products for better performance. MANUFACTURING PLANT Aggregate product data, customer sentiment, and other third-party syndicated data to identify and correct quality issues. Manage equipment remotely, using temperature limits and other settings to conserve energy and reduce costs. Monitor production flow in near-real time to eliminate waste and unnecessary work in process inventory. GLOBAL FACILITY INSIGHT Implement condition- based maintenance alerts to eliminate machine down-time and increase throughput. THIRD-PARTY LOGISTICS Provide cross-channel visibility into inventories to optimize supply and reduce shared costs in the value chain. CUSTOMER SITE Transmits operational information to the partner (e.g. OEM) and to field service engineers for remote process automation and optimization. Management R&D Field Service
- The Internet of Things – Oil & Gas Utilize advanced 3D and 4D visualizations based on analytic algorithms to model subsurface geology Production Manager Onsite personnel Establish near real-time communication and automatically publish events and alarms to the field to guide and protect onsite personnel and assets Integrate all upstream data onto a unified platform to facilitate analytics, information sharing, and organizational transition 1. Exploration 2. Development 3. Drilling4. Production Geologist Consolidate data from surveys, drill logs, and external sources to generate advanced reservoir models and production forecasts Maximize recovery by monitoring near real-time production data and generating alerts for conditional maintenance needs Combine near real-time drilling and seismic data to optimize drilling trajectories and recovery potential, while minimizing environmental risk Operations Control Center Find new hydrocarbon reservoirs quicker with seismic data uploaded to the cloud and prepared for analysis NORTH SHORE PRODUCTION
- PHARMACY The Internet of Things – Pharma Customer Service Monitor device data to make more timely health decisions, such as adjusting dosages Enable advanced product tracking and authentication to prevent counterfeits Develop better products, faster, informed by a much larger data set based on patient outcomes R&D Anticipate medical device maintenance needs, and alert patients to schedule a doctor visit for replacement or repair Healthcare Provider Monitor medical device functionality for better customer service, reduced risk, and insight to improve product designs Manage equipment remotely, using appropriate KPIs Reduce machine downtime with condition-based maintenance alerts Patient Home DistributionManufacturing Aggregate and correlate data from disparate medical devices with medications and health outcomes for advanced insight
- Producers Event Ingestion Storage Transformation Presentation & action Event Hubs (Service Bus) SQL Database Machine Learning Azure Websites Heterogeneous client agents Table/Blob Storage HD Insight Mobile Services External Data Sources DocumentDB Stream Analytics Notification Hubs External Data Sources Cloud Services Power BI External Services Microsoft Azure services for IoT Event Hubs (Service Bus) Stream Analytics SQL Database Azure Websites Mobile Services Notification Hubs Power BI External Services Table/Blob Storage DocumentDB{ } HD Insight Machine Learning
- Use Cases (practice)
- Manufacturing
- Manufacturer of Automobiles Manufacturer One of the leading multinational automobile corporations that is one of the largest companies in the world by revenue. They manufacture over 10 million vehicles a year. Part 1: What They Did | Produces Internet of Things insights for their automobiles Challenge Needed to analyze the telemetry being emitted from their luxury car line in real-time. Wanted to build a scalable, reliable, and highly available solution that has the ability to receive and process a large volume of vehicle information and maintenance events Solution Use Azure Blob, HDInsight, Storm in HDInsight, HBase in HDInsight, Event Hubs, DocumentDB, Machine Learning, and Power BI Collect IoT data from automobiles: • Telemetry data comes in real-time • Able to process and generate insights around vehicle information and maintenance events Internet of Things BK1
- BK1 Manufacturer of Automobiles Part 2: How They Did It | Produces Internet of Things insights for automobiles How They Did It Collect data from automobiles • Send events in real-time to Event Hubs • Stored into Azure Blobs Retrieve reference data and do predictive analytics • Get reference data stored in HBase • Run ML algorithms on the telemetry to predict outcomes Store into queryable store DocumentDB • Stored in DocumentDB for Power BI to display as a dashboard • Trigger Apache Storm in HDInsight to process and return results back to the vehicles Internet of Things HDFS Store ML No SQL Store Live Dashboard Event Hubs Azure Blob HBase Azure ML DocumentDB PowerBI Event Hubs Apache Storm on HDInsight
- Power and Utilities & Oil and Gas
- Industrial automation company partnering with multinational oil company Oil and Gas Leading industrial automation company who employs over 20,000 people. partnering with Leading multinational oil and gas company (one of the six oil and gas super majors) who employs over 90,000 people. Part 1: What They Did | IoT internet-connected sensors to generate analytics for proactive maintenance Challenge Manage sites used for dispensing liquefied natural gas (clean fuel for commercial customers who do heavy-duty road transportation) Built LNG refueling stations across US interstate highway Stations are unmanned so they built 24x7 remote management and monitoring to track diagnostics of each station for maintenance or tuning Built internet-connected sensors embedded in 350 dispenser sites worldwide generating tens of thousands data points per second • Temperature, pressure, vibration, etc. Data needs outgrew company’s internal datacenter and data warehouse Solution Chose Azure HDInsight, Data Factory, SQL Database, Machine Learning Dashboards used to detect anomalies for proactive maintenance • Changes in performance of the components • Energy consumption of components • Component downtime and reliability Future: Goal is to expand program to hundreds of thousands of dispensers IoT, Analytics
- BK1 Industrial automation company partnering with multinational oil company Part 2: How They Did It | IoT internet-connected sensors to generate analytics for proactive maintenance How They Did It Collect data from internet-collected sensors • Tens of thousands data points per second • Interpolate time-series prior to analysis • Stored raw sensor data in Blobs every 5 minutes Use Hadoop to execute scripts and Data Factory to orchestrate • Hive and Pig scripts orchestrated by Data Factory • Data resulting from scripts loaded in SQL Database • Queries detect site anomalies to indicate maintenance/tuning Produced dashboards with role-based reporting • Azure Machine Learning , SSRS, Power BI for O365 • Provide users with customizable interface • View current and historical data (day-to-day operations, asset performance over time, etc.) • Leveraged Azure Mobile Notification Hub for real-time notifications, alarms, or important events Use Azure ML to predict • Understand which pumps, run at what speeds, maximized water supply while minimizing energy use IoT, Analytics
- Government
- Secretary of Finance and Public Credit - Government Government Government organization that handles finances, taxes, budget, income, and national debt for their country. Part 1: What They Did | Fraud and Money Laundering Detection Challenge The government passed a law to have all invoice submission to be in electronic format The tax department allows clients to uploads their digital documents (pay stubs, expenditure slips) and now have 4 billion documents uploaded Want to get insights into the data to do analysis and identify trends and fraud and ensure compliance with tax obligations Solution Built electronic digital invoicing solution to upload invoices • Paystubs, expenditure slips Use HDInsight to run queries and to process the electronic invoices to gain insights Needed to scale to a peak of 150+ million invoices uploaded / day Do Fraud detection by understanding what people are doing to detect anomalies (ie. tax fraud, money laundering, etc.) Output of the system saved to SQL Server on-premises databases to run ad hoc queries Fraud Detection
- BK1 Secretary of Finance and Public Credit - Government Part 2: How They Did It | Fraud and Money Laundering Detection How They Did It Store electronic digital invoices as XML document in Azure Blobs • Store approximately 4 billion invoices total • Store 40 million – 180 million files every day • Data is stored as XML files with metadata information • Average size of each XML document is 5-10KB Use Azure HDInsight (>140 node clusters) • Do batch querying • Use Hive, Pig, and MapReduce • Hive external tables to make files queryable • Run once per day • Detect anomalies / fraud Send to SQL Server in IaaS VM and then to SQL Server On-premises • SQOOP data from Azure Blobs to SQL Server VMs • ETL to SQL Server on-premises • Do BI on top of SQL Server as a data mart Fraud Detection Website to submit electronic documents
- Entertainment and Gaming
- Game Development Company Gaming A predominantly mobile-based game development company. While they are a mid-sized organization, they have partnered with media giants on various gaming projects Part 1: What They Did | In-game Analytics Challenge As a game development studio, they wanted to do in-game analytics to understand their players more and what they do in the games Solution Chose Azure HDInsight (MapReduce and Storm), Service Bus and also use SQL Server for reporting Switched from Amazon AWS EMR Collects telemetry and logging data to gain in-game analytics: • How many players using the game • How many players invited their friends • How far along did players get into the tutorial • How many attempts did they make on one level/stage In-game Analytics
- BK1 Game Development Company Part 2: How They Did It | In-game Analytics How They Did It Collect data from games in Azure Blobs • Game sends telemetry/logging data as JSON files • Contains every action of user in the game • Data is pushed to Azure Service Bus as real-time • Tens of Gigabytes of data captured daily HDInsight picks up real-time data and processes • From Service Bus, HDInsight processes using Apache Storm and MapReduce • Constantly running experiments to determine insight • A/B testing • In-game metrics and analytics • Spin up 32-node cluster nightly for four hours Output sent to SQL Server for BI • Transfer data to SQL Server for BI In-game Analytics Service Bus SQL Server On-premises
- Non-Profit
- JustGiving, Non-Profit Non-profit JustGiving, a global online social platform for giving. It's a financial service (not a charity) that lets you "raise money for a cause you care about" through your network of friends. Their goal is to become "Facebook of Giving" Part 1: What They Did | Recommendation Engine Challenge They wanted to identify what was personal and relevant to people and what they cared about, so that they could suggest further causes that may inspire continual involvement. With 22 million customers this meant storing and processing huge amounts of data that their existing infrastructure simply couldn’t support. Solution Chose SQL Server on-premises, Azure HDInsight, Blobs, Tables, Cache, and Service Bus Deployed a network of “social giving” for people to make it a group activity to support a cause • Built a way to inform givers a charity goal based on a person’s position in their social graph • Help identify causes that a user might be interested in (based on demographics, and their social graph) • Recommend people to add to their social graph as well as other charitable causes Recommendation
- JustGiving, Non-Profit Part 2: How They Did It | Recommendation engine How They Did It Collect data in Azure Blobs • Move data from SQL Server through an Agent to Azure Blobs HDInsight processes data for insights • Input data is 20-30GB / job • Use MapReduce jobs to create a graph • Further job to denormalize activity feeds for all users • Generates an activity recommendation Generates a real-time recommendation • Real-time activity feeds/events coming in from Service Bus (~50 events/second) • Activity recommendation coming out of daily HDInsight job • Sent to web-site Recommendation SQL Server On-premises Agent Azure Blobs Azure HDInsight Activity Feeds Give Graph Azure Tables Web API Website + Event store Service Bus Serves results Azure Cache
- Resources The Modern Data Warehouse: http://bit.ly/1xuX4Py Should you move your data to the cloud? http://bit.ly/1xuXbKU Presentation slides for Modern Data Warehousing: http://bit.ly/1xuXcP5 Presentation slides for Building an Effective Data Warehouse Architecture: http://bit.ly/1xuXeX4 Hadoop and Data Warehouses: http://bit.ly/1xuXfu9
- Q & A ? James Serra, Big Data Evangelist Email me at: JamesSerra3@gmail.com Follow me at: @JamesSerra Link to me at: www.linkedin.com/in/JamesSerra Visit my blog at: JamesSerra.com (where this slide deck will be posted)
Notes de l'éditeur
- Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions. I’ll talk about theory and then show you how it’s really being applied in practice. I talk with large companies every day about how they are solving big data.
- Fluff, but point is I bring real work experience to the session
- Level set on what the popular technologies mean
- Key Points: Businesses can use new data streams to gain a competitive advantage. Microsoft is uniquely equipped to help you manage the growing volume and variety of data: structured, unstructured, and streaming. Talk Track: Does it not seem like every day there is a new kind of data that we need to understand? New data types continue to expand—we need to be prepared to collect that data so that the organization can then go do something with it. Structured data, the type of data we have been working with for years, continues to accelerate. Think how many transactions are occurring across your business. Unstructured data, the typical source of all our big data, takes many forms and originates from various places across the web including social. Streaming data is the data at the heart of the Internet of Things revolution. Just think about how many things in your organization are smart or instrumented and generating data every second. All of this means that data volumes are growing and bringing new capacity challenges. You are also dealing with an enormous opportunity, taking all of this data and putting it to work. In order to take advantage of all this data, you first need a platform that enables you to collect any data—no matter the size or type. The Microsoft data platform is uniquely complete and can help you collect any data using a flexible approach: Collecting data on-premises with SQL Server SQL Server can help you collect and manage structured, unstructured, and streaming data to power all your workloads: OLTP, BI, and Data Warehousing With new in-memory capabilities that are built into SQL Server 2014, you get the benefit of breakthrough speed with your existing hardware and without having to rewrite your apps. If you’ve been considering the cloud, SQL Server provides an on-ramp to help you get started. Using the wizards built into SQL Server Management Studio, extending to the cloud by combining SQL and Microsoft Azure is simple. Capture new data types using the power and flexibility of the Microsoft Azure Cloud Azure is well equipped to provide the flexibility you need to collect and manage any data in the cloud in a way that meets the needs of your business. Big data in Azure: HDInsight: an Apache Hadoop-based analytics solution that allows cluster deployment in minutes, scale up or down as needed, and insights through familiar BI tools. SQL Databases: managed relational SQL Database-as-a-service that offers business-ready capabilities built on SQL Server technology. Blobs: a cloud storage solution offering the simplest way to store large amounts of unstructured text or binary data, such as video, audio, and images. Tables: a NoSQL key/value storage solution that provides simple access to data at a lower cost for applications that do not need robust querying capabilities. Intelligent Systems Service: cloud service that helps enterprises embrace the Internet of Things by securely connecting, managing, and capturing machine-generated data from a variety of sensors and devices to drive improvements in operations and tap into new business opportunities. Machine Learning: if you’re looking to anticipate business challenges or opportunities, or perhaps expand your data practice into data science, Azure’s new Machine Learning service—cloud-based predictive analytics— can help. ML Studio is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning. Document DB: a fully managed, highly scalable, NoSQL document database service Azure Stream Analytics: real-time event processing engine that helps uncover insights from devices, sensors, infrastructure, applications, and data Azure Data Factory: enables information production by orchestrating and managing diverse data Azure Event Hubs: a scalable service for collecting data from millions of “things” in seconds Microsoft Analytics Platform System: In the past, to provide users with reliable, trustworthy information, enterprises gathered relational and transactional data in a single data warehouse. But this traditional data warehouse is under pressure, hitting limits amidst massive change. Data volumes are projected to grow tenfold over the next five years. End users want real-time responses and insights. They want to use non-relational data, which now constitutes 85 percent of data growth. They want access to “cloud-born” data, data that was created from growing cloud IT investments. Your enterprise can only cope with these shifts with a modern data warehouse—the Microsoft Analytics Platform System is the answer. The Analytics Platform System brings Microsoft’s massively parallel processing (MPP) data warehouse technology—the SQL Server Parallel Data Warehouse (PDW), together with HDInsight, Microsoft’s 100 percent Apache Hadoop distribution—and delivers it as a turnkey appliance. Now you can collect relational and non-relational data in one appliance. You can have seamless integration of the relational data warehouse and Hadoop with PolyBase. All of these options give you the flexibility to get the most out of your existing data capture investments while providing a path to a more efficient and optimized data environment that is ready to support new data types.
- What is IoT really? IoT really comes down to four key things: Physical “things” such as LoB assets, devices and sensors Those “things” that have connectivity to either the internet or to each other or humans These things have the ability to collect and communicate information – this information may include data collected from the environment or inputted by users And then the analytics that comes with the data enable people or machines to take action Getting started on the Internet of Things enables you to transform your current business and enable new growth opportunities: IoT provides better insights to your internal, geographically dispersed operations to manage your operations, processes, asset performance and utilizations, and customer usage insights IoT enables you to expand your business models to include proactive/predictive management and maintenance services on assets on behalf of your customers In all industries, IoT helps you drive down costs and improve efficiency by monitoring and tracking the health of your assets
- Key goal of slide:: Land Microsoft’s unique and differentiated point-of-view on the Internet of Things: the Internet of Your Things. Microsoft believes the Internet of Things doesn’t have to be overwhelming. Businesses can start small, with a few changes that make a big impact. It’s not about the billions of things that can be connected, it’s about YOUR THINGS. And, it’s already happening! Slide talk track: The Internet of Your Things is not about ripping and replacing technologies in your enterprise. It’s about leveraging what you have, adding on to your existing systems, using your existing things in new ways, and innovating and optimizing so everything works better together. If you’re a retailer, think about how smarter POS terminals can increase cross-selling and up-selling. If you’re in healthcare, think about how connecting patient monitors, tablets, signage and other equipment can streamline patient care. For manufacturers, sensors on the factory floor can “talk” to diagnostic monitors to improve production efficiency and reduce down time. The Internet of Things starts with your things. It is about the things that matter most to your business. Build on the infrastructure you already have. Connect the devices you already own…then add to your existing investments. Tap into the data that already exists. The Internet of Your Things is about getting away from spending all your time just running your business, and thinking about finding ways to make it thrive. Start realizing the potential of the Internet of Your Things.
- Why move relational data to data lake? Offload processing to refine data to free-up EDW, use low-cost storage for raw data saving space on EDW, help if ETL jobs on EDW taking too long. So can actually use a data lake for small data – move EDW to Hadoop, refine it, move it back to EDW. Cons: rewriting all current ETL to Hadoop, re-training I believe APS should be used for staging (i.e. “ELT”) in most cases, but there are some good use cases for using a Hadoop Data Lake: - Wanting to offload the data refinement to Hadoop, so the processing and space on the EDW is reduced - Wanting to use some Hadoop technologies/tools to refine/filter data that are not available for APS - Landing zone for unstructured data, as it can ingest large files quickly and provide data redundancy - ELT jobs on EDW are taking too long, so offload some of them to the Hadoop data lake - There may be cases when you want to move EDW data to Hadoop, refine it, and move it back to EDW (offload processing, need to use Hadoop tools) - The data lake is a good place for data that you “might” use down the road. You can land it in the data lake and have users use SQL via Polybase to look at the data and determine if it has value
- http://readwrite.com/2014/12/26/big-data-will-get-bigger-in-2015 Key goal of slide: Communicate what Hadoop is Slide talk track: Everyone has heard of Hadoop. But what is it? And do I need it? Apache Hadoop is an open-source solution framework that supports data-intensive distributed applications on large clusters of commodity hardware. Hadoop is composed of a few parts: HDFS – Hadoop Distributed File System is Hadoop’s file-system which stores large files (from gigabytes to terabytes) across multiple machines MapReduce – is a programming model that performs filtering, sorting and other data retrieval commands across a parallel, distributed algorithm. Other parts of Hadoop include Hbase, R, Pig, Hive, Flume, Mahout, Avro, Zookeeper which are all parts of the Hadoop ecosystem that all perform other functions to supplement.
- Lambda architecture Solutions to follow are about building a data warehouse, but more are about IoT solutions Capture some of the IoT data for a real-time solution, and capture all of the IoT data into a data warehouse for long-term analysis
- Advanced Analytics, or Business Analytics, refers to future-oriented analysis that can be used to help drive changes and improvements in business practices. It is made up of four phases: Descriptive Analytics: What is generally referred to as “business intelligence”, this phase is where a lot of digital information is captured. Then this big data is condensed into smaller, more useful nuggets of information, creating an understanding of the correlations between those nuggets to find out why something is happening (“Diagnostic Analytics”). In short, you are providing insight into what has happened to uncover trends and patterns. An example is Netflix using historic sales and customer data to improve their recommendation engine. Predictive analytics: Utilizes a variety of statistical, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions, or forecasts, about the future. In short, it helps model and forecast what might happen. For example, taking sales data, social media data, and weather data to forecast the product demand for a certain region and to adjust production. Or you can use predictive analytics to determine outcomes such as whether a customer will “leave or stay” or “buy or not buy.” Prescriptive analytics: Goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. The output is a decision using simulation and optimization. In short, it seeks to determine the best solution or preferred course of action among various choices. For example, airlines sift thought millions of flight itineraries to set an optimal price at any given time based on supply and demand. Also, prescriptive analytics in healthcare can be used to guide clinician actions by making treatment recommendations based on models that use relevant historical intervention and outcome data.
- In each of these industries/verticals, specific scenarios have been identified. A selection of scenarios are detailed on the next slides–pick 1, 2 or 3 from these to share and discuss with your customer, or use one of the scenarios as a template to create your own customer-specific scenario. Use this slide as a discovery slide, to get your customer talking about the potential for their business. Tailor the discussion to your customer’s industry for relevance. Either prepare deck or skip to relevant scenario drill-down slides in this deck.
- Manufacturing Plant Manage data remotely from a centralized location and push updates and key notifications to the factory floor, making relevant information available to manufacturing employees. Monitor whether components are arriving at the plant floor as expected, and slow production if needed to reduce or eliminate excess work-in-process inventory Predefine rules for equipment use and plant management (e.g. shut down production or equipment based on demand or environmental data), to optimize productivity and profitability Establish predictive maintenance schedules - with planned maintenance, cost of operations can be reduced and throughput can be increased Identify and correct quality issues – with IoT and the ability to perform Big Data analytics, manufacturers can increase the number of quality checks that are performed, collect more quality inspection data and analyze more data than ever before. This enables them to spot defect patterns, and correct them more quickly. This also enables manufacturers to create predictive algorithms so that times/places where quality issues may occur are identified ahead of time. (e.g. extra humid day might imply more likelihood of quality issues) Customer Site and Third-Party Logistics Continue to collect data from products once they leave the factory floor (e.g. throughout the distribution process and once implemented into customer sites) to help drive predictive maintenance and inform product improvements E.g. a water pump could send data indicating that it will break down in 2-3 weeks. An analyst at the customer’s OEM or service provider could determine whether the pump is in need of simple repairs, or whether it makes sense to refurbish it. The analyst could also identify a spare parts promotion that the customer could take advantage of so the customer is prepared for future maintenance needs Access more data than ever before and integrate with 3rd party syndicated data to improve process and quality control (e.g. use data on regional weather patterns to determine locations where weather conditions will result in higher demand, or view data about fuel and other input prices to predict profit margins) Provide ‘single pane of glass’ visibility across all distribution/sales channels – this enables better inventory management and savings on logistics and distribution costs because the full channel would know would where products are, so redundancies can be reduced Global Operations powerful BI tools – derive business insights from Big Data analytics Make data available to the right individuals for a variety of purposes based on role, such as product development, facility and device servicing, or business and operations management
- Microsoft Azure services for IoT in the oil and gas industry consolidates data collected during the various phases of onshore and offshore hydrocarbon extraction onto a unified platform for use in advanced analytics and information sharing – reducing the cost of operations and enabling you to do more with less. Exploration: Utilize IoT to find new hydrocarbon reserves quicker. As geologists run seismic surveys, data collected from an array of instruments and sensors, such as high-performance geo-phones, uploads to the cloud. From there, the seismic data is processed and can be analyzed in near real time. Development: Using advanced modelling and data analysis techniques, and data from seismic surveys and drilling logs, model hydrocarbon reservoirs and the changes that progress over time. Provide geologists with access to advanced 3D and 4D subsurface visualization tools. They can search for the seismic records of an area and view them together with changes over time—and develop more precise production forecasts. Drilling: Utilize drilling data and subsurface modeling to find the optimum drilling path and approach. Informed by a near real-time database, geosteering teams can send commands back to the drilling rig to optimize trajectory—and ultimately the extraction of hydrocarbons. Events and alarms are also published to the oil/gas field to guide and protect onsite personnel and assets. Near real-time communication and optimized drilling approaches support safety and security throughout the drilling process. Production: Produce more from existing hydrocarbon reserves—sustaining production levels for the maximum timeframe while reducing environmental and safety risks. Monitor production data in near real-time with highly-visual, searchable production reports, KPIs and dashboards. Enhance recovery by predicting production declines and providing recovery options. Evaluate root causes of safety and security events and predict important incidents before they happen via machine learning. Monitor permanent well instrumentation and enable predictive maintenance. Operations Control Center: Reduce the cost of operations by moving field work to centralized locations. Use Perceptive Pixel technologies in operations control centers where subject matter experts and local teams can work collaboratively to fix issues or provide guidance. Unified Communications and Collaboration is integrated with the visualization layer to enable near real-time collaboration with the different stakeholders of asset teams. In this way, IoT enables you to do more with less, facilitating organizational transition to a more advanced and capable operating level.
- OEM The manufacturing industry is going through a profound transformation in which manufacturers are shifting from delivering products to delivering ongoing business services across the entire lifecycle of product ownership. IoT enables Auto OEMs to leverage data needed to improve services. Through the connected vehicle, OEMs can now have an ongoing relationship with consumers, and dealers also have new ways to monetize connectivity with consumers. R&D Data from IoT helps to drive better vehicle reliability, easier maintenance, and the ability to incrementally add new features that increase customer satisfaction. Focus groups are great at providing an indicator of what new features are desirable in cars, but actual data that provides insights to actual use patterns is extremely valuable. IoT provides the insight into what features customers really use and how often, so you can be smarter about your design and enhance your bottom line. Remote vehicle diagnostics enabled by IoT then show how various vehicle components perform in the field to inform your product planning efforts. By connecting vehicle telemetry data directly to your own service, you can also bypass the dealership so that you are no longer dependent on them. Save time and money by reducing the need the study the products claimed as defective. Marketing Data plays an important role in sales and marketing. Gain telemetry data to inform your marketing efforts – data such as what vehicles are being sold by geography and how they are used in the field. Brand loyalty With IoT, influence with every customer can continue throughout ownership of the vehicle – until they are back in to buy their next vehicle. If someone buys a car and has a good experience, the chances of him buying the same brand for his wife or kids or for is next car is high. Through the following services, we’ll see how you can provide differentiated experiences that drive brand loyalty. Connected consumer services As people spend more and more time on the road, they want the same communication and entertainment features they use at home and at work. Integrate people's digital lives with their cars, connecting drivers to all sorts of services and devices wherever they go. Vehicle diagnostic and navigation data, traffic information, fuel costs, and points-of-interest information can all be delivered in a user-friendly way while consumers drive. Pay-as-you-go insurance Customers can sign up for new services like pay-as-you-go insurance to reduce cost, or they can take advantage of location-based services. Conditional maintenance They can also receive proactive alerts when maintenance or repairs are needed. Our goal is to improve the driving experience with more information. Imagine someone is driving down the road with their kids and the check-engine light comes on – and worry sets in. You have the opportunity to engage customers and deliver rich context – and explain to the driver what the light means. The system can be automatically connected to a dealership reservation service to make an appointment for the driver. Through this connected service, the customer will feel safe and will trust your brand. These connected experiences drive brand loyalty. Extended warranties Offer an extended warranty based on actual vehicle usage data a year or two following the vehicle purchase, when the customer may also be more receptive to the thought of an extended warranty. IoT can also aid recall management by helping to track the vehicles involved. DEALERSHIP After the initial warranty period has ended, dealerships often capture less than 20% of the vehicle repair business – which represents a huge opportunity. Provide value to dealerships by sharing data they can monetize – the data that enables them to maintain customer relationships and bring more vehicles back for services. As we’ve mentioned, with vehicle telemetry data, dealerships can send customers alerts regarding conditional or preventive maintenance needs and prompt the customer to take action. They can schedule an appointment directly through the car. They can also advertise promotions, such as offering discounted services during slow business times. Overall, this will help drive customers back to the dealership when they need service, or when they need a new car, or their kid needs a car, because they've had good experiences. THIRD PARTY SERVICES Third party services like insurance providers and smart vehicle charging networks can also benefit from the data collected from connected vehicles. For example, with the right permissions, insurance companies can offer customers deals on pay-as-you-go or driver behavior-based policies. Additional third party services may include navigation, weather, trip planning, location-based and targeted ads, and safety and emergency services.
- With Microsoft Azure services for IoT, pharmaceutical companies can engage deeper than ever before with healthcare providers, patients, and other customers, giving them the opportunity to scale globally and reengineer products as well as the total business ecosystem. The big transformation involves monitoring your products in use. With this visibility, you can understand the way customers use the product, the way the product performs, and the way the market is opening up for white space opportunities. Microsoft Azure services for IoT, can help you at every stage of the product lifecycle. The lifecycle is shown here in a particular order, but the order may vary in real-life scenarios. R&D IoT enhances your R&D processes, enabling you to bring products to market faster and better than ever before. Everything learned about your products through IoT comes back to fuel innovation to improve products and create the products of the future. For example, enable customized medicine based on individual patient data. Rather than a limited number of samples in a controlled environment, collect data from a large set of devices in use. Learn about the performance of your devices from the hands of your customers, and use that data to inform the next generation of your products. The cloud is a great enabler for these scenarios. Data from disparate devices as well as external data, including that from healthcare and customer service systems, is aggregated and correlated. Conduct Big Data analysis and use machine learning models to generate reports. View how equipment is being used and its affect on patients, observe trends in device performance, and improve devices over time. Manufacturing Consider the value of IoT in your manufacturing plant. Connect devices and sensors on the production line for better visibility into appropriate KPIs and more efficient management. For example, add temperature sensors to more accurately measure environmental stress on equipment so you can optimize maintenance schedules and reduce equipment downtime. Enable connected endpoints to issue maintenance orders, capture usage patterns, request inventory replenishment, and update in near real time. Combine data from the plant floor with other data sets, and apply advanced analytics for predictive models that discover patterns before something goes wrong. Enable near real-time troubleshooting and repair of machinery as well as production management from remote locations. Equip workers with all the relevant information important to them, on any device. Present the data as easy-to-use dashboards, and view advanced data visualizations. Analyze Big Data from the entire system and improve production processes with a much better picture of operations. Cut minutes from one process and reclaim thousands of hours across the business. Easily identify where bottlenecks emerge and where optimization can increase throughput. You can update legacy systems without wholesale replacement. Add sensors and tap into data to add years to the life of your operations—identifying issues before they happen. Distribution Utilize IoT to authenticate product shipments. Easily track devices and medications throughout the supply chain—enabling recipients to more assuredly verify shipments as your products rather than counterfeits. For medications or devices requiring special handling requirements, easily monitor temperatures and other parameters during the distribution process. Patient Home Connected medical devices offer a lot of value, even life-saving value, for end customers. From patient homes, collect data from embedded or wearable medical devices. For example, stream data—such as blood pressure and temperature—from a wireless blood pressure monitor through a patient’s smart phone or an IP gateway. Collect and aggregate data from different kinds of devices, whether sensors under the skin, insulin pumps or pacemakers, as well as other streams of data. Data fed to the cloud can then be analyzed and consumed by appropriate parties. The data can trigger automatic events. Doctors gain near real time access to vitals and receive alerts to act in a timely manner. Send notifications to the patient’s smart phone, such as a message informing the patient about the possibility of a device failing long before it does. Family members can receive similar visibility and notifications and take appropriate action. Healthcare Provider Physicians can view patient vitals and other health information from at-home patient devices or embedded sensors in near real time and receive automatic alerts regarding patient condition. Family members can be granted similar access to monitor patient health status and take appropriate action. This advanced visibility regardless of patient location enable more timely, informed decision-making. Physicians can view device functionality as well to know, for example, if a patient requires replacement or repair of a critical embedded device. Automatically customize medication dosages for specific patients based on data collected. Improve the effectiveness of medication by more easily monitoring side effects through device data and administering no more or less than the patient can tolerate. Aggregate and correlate data from disparate medical devices with medications, doses, and health outcomes for advanced insight. Customer Service Provide proactive customer service and achieve greater satisfaction. Predict medical device maintenance needs, and automatically alert patients to schedule a doctor visit for replacement or repair. Monitoring medical device functionality enables not only better customer service but also reduced risk.
- Microsoft Azure services for IoT, can help transform public sector transportation systems—by automating manual processes, spotting equipment issues before they cause service disruptions, analyzing operational data to improve business decision-making, fuel economy, citizen satisfaction, productivity and more. The following are just a few quick examples to get you thinking. City Manager An IoT solution can help you offer more seamless transit services to achieve outcomes such as boosting economic growth and improving urban quality of life. This includes increased environmental awareness for cleaner air and a smaller carbon footprint. (See the Water vertical graphic for more information on water and energy conservation.) Key solution areas covered here apply to tolling systems, city-owned parking, and public transportation. Reduce congestion and increase revenue through automated tolling systems One way to reduce congestion, pollution, vehicle dependence, and lost productivity is through secure automated tolling systems with visual recognition of license plates and incentivized off-peak travel. With IoT, you can implement dynamic pricing based on time of day, season, vehicle type and other factors. You can also optimize signal timing for smoother flowing traffic throughout the city by analyzing traffic patterns by signal location, time of day, proximity to busy parking outlets, etc. Or better manage factors such as the logistics of inbound freight—for example, providing incentive or regulation to minimize the stopping or double parking of delivery trucks during congested timeframes. Alleviate the hassle of city parking IoT can provide a more modern and efficient system for managing city-owned parking – helping you solve the problem of undisciplined drivers, increase revenue from parking charges, reduce traffic congestion and improve air quality, reduce time to wait for parking, and improve citizen satisfaction. IoT can reduce the time citizens spend finding an open parking space by providing an easy view of city-owned parking availability. Increase the ease and accuracy of payment with secure automatic device payment systems, and increase revenue by implementing dynamic parking rates based on time of day, season, special events, location, or other factors. You can also charge for the exact time of use instead of in intervals. Enable control officers to access the system at any time to check on the status of any parked vehicle, and view all of the data relevant to proper management. Simplifying the parking experience is one of the many consumer-oriented experiences that have to do with how citizens manage their personal vehicles. For example, ride sharing is another topic in this category with potential for IoT implementation. Fleet Manager Gain a nuanced understanding of fleet operations across the city, across all vehicles and routes, and over time—and keep more vehicles in service. IoT data enables a visualization of operational performance by a single driver or across the system. Map your data to see which routes experience the most delays and breakdowns and to associate data on individual driver performance with specific traffic incidents or customer feedback. For example, determine whether a bus was in motion or whether the driver was braking at the time of a collision. Supervisors can bring map data into conversations with drivers, making it easier to review performance and outline steps for improvement. Power Map visualizations also enable executives to more quickly understand the factors that contribute to cost or performance issues and make more informed long-term decisions. You can also improve vehicle fuel efficiency by using sensor data to identify machine malfunctions. Increase transit utilization Identify public transportation routes requiring more frequent vehicles to reduce overcrowding, improve citizen satisfaction and increase revenue. Identify routes that need to scale back, perhaps at certain times of the day, to reduce unnecessary spending and emissions. (Citizen) Provide citizens with a better public transit experience—for example, supply transit information on digital signs and to citizens’ mobile devices. Centrally manage all station/airport assets Modernize the systems that securely monitor, manage and automate your things, everything from escalators, elevators, and HVAC controls to closed-circuit video and communication systems. Connected security networks and personnel heighten security visibility to save more lives. Data from sensors and intelligent edge devices — to closely monitor temperature, vibration, humidity, fault warnings and system alerts — are all available in one central location to provide access to needed information on mobile apps, via a Web browser or through text alerts. Streamline airport operations with IoT. Equip ground crews with advanced information sharing for faster plane turnaround. Integrate sensor, weather, and other data, and use machine learning for more accurate plane arrival predictions that could translate to millions on cost savings and greater customer satisfaction. Save money on maintenance costs Reduce the costs of equipment downtown and provide an advanced field maintenance solution. Eliminate calendar-based maintenance in favor of conditional maintenance. Easily identify mechanical problems and address them quickly, with alerts from vehicle, plane, and train sensors. Enable work flows for utility work crews through hand-held devices – and configure notifications on their devices of maintenance needs. With sensors installed on all crew vehicles, capture and analyze emissions data to identity room for improved efficiency and reduced fuel costs. Similarly, monitor and control smart lighting, heating and cooling systems etc. to save energy costs. The possibilities for improving public sector transportation systems are numerous and within your reach with IoT.
- Water Systems (starting in upper left) Forecast water demand and manage supply by integrating data from smart devices and sensors with third-party data (e.g. environmental and weather data, supply and demand rates, local/regional events, historical trends, etc.) City water management Control water supply or distribution automatically with preconfigured business rules for smart devices (e.g. pumps, shadow meters, flow valves, etc.) by aggregating data in near real-time and making it accessible to providers from a central location Grant citizens, businesses, and government agencies clearer visibility into water usage to enable people-first conservation initiatives Enable faster responses when spills, leaks, sewer overflow, or drainage issues occur by accessing data in near real-time Facility management and energy production Control water distribution or flow, and optimize for conservation and renewable energy integrate water system data with data from waste and energy facilities (e.g. water turbines, smart grids, etc.)
- Patient Home Monitor patients’ health and wellness indicators through data transmitted from smart devices and sensors in homes, such as prescription pill containers and glucose monitors Near real-time data alerts (e.g. captured by sensors, cameras, or wearable devices like bracelets or necklaces with an “emergency” button) might indicate health events Aggregated data from over time (e.g. data from sensors in furniture or wearables) can be used to track and forecast health trends over time – such as level of activity Patient vehicles can also be transformed into health environments that capture data in near-real time to track patient health status and trends Data can also be pushed to smart devices within vehicles to encourage healthy choices or alert patients and healthcare providers of key information Hospital Patient Room Preconfigure smart hospital room devices to respond automatically to predetermined health indicators or patient status (e.g. as patient falls asleep, room temperature and lighting adjust accordingly) to improve patient experience Enable patients to interact with their care teams remotely (e.g. patient uses tablet or other handheld device to communicate with a nurse or doctor who is elsewhere) Give care providers the ability to access data remotely, from wherever they are, so that they can respond quickly to changes in patient condition Contextualize patient data so that relevant information automatically surfaces for the appropriate members of care teams when needed (e.g. Dashboard of patient’s health data, medications, recent notifications, etc. appears on a doctor’s tablet when they enter the patient’s care room) Analytics Department Aggregate authorized data from traditional and non-traditional sources (e.g. patient data, medical research, regional health information or news, demographic information, etc.) to deliver a holistic view of the health indicators for a patient, improving care treatment and risk management Nurses’ Station Display the latest patient data on screens to “nudge” care providers in the area to take certain actions (e.g. check on a certain patient nearby if needed) Transmit patient data in near real-time to members of collaborative care team (e.g. to handheld medical devices, kiosks, computers, dashboards, etc.) to facilitate coordination and decision making Outpatient Facility Provide care providers with a holistic view of a patient’s medical and health history from a unified point so that providers can optimize each visit by improving treatments and identifying additional health risks earlier Healthcare Ecosystem Integrate data across various fields and make it available to providers, government organizations and other entities within the healthcare ecosystem, improving public health, driving prevention campaigns, improving treatment options, and generally driving innovation and continuous transformation in healthcare
- The Retail industry is dealing with major shifts in consumer shopping behavior, preferences, and expectations, largely driven by new technologies and form factors. As consumers, we expect to access information in ways that are fast but familiar to us in every aspect of our daily lives, including when shopping for groceries, visiting an ATM, or checking out at a store. By building an end-to-end omni-channel solution based on Microsoft and partner technology, you can deliver the seamless, relevant experiences that your customers expect in today’s digital world. With IoT, almost every interaction among customers involving products and services can be captured, measured, and analyzed. You can gather where the shopper goes and everything they do – on Instagram or Twitter, up and down your aisles, on your website. Which freezer case did they open? What products did they carry into the fitting room? Or, what was weather like at the time? Warm and sunny? Cold and rainy? While you once relied on data from the cash register, with IoT you now can collect data from a variety of store sensors, RFID data, integration with external sources like social media and weather reports, and more. All these points of customer interaction, or touch points, can be considered as a sort of “shopstream” – where data exhaust is produced at each step along the customer journey. Millions of data points available from all of these new interactions enable you to create far more granular customer segmentations and make very accurate predictions about their behavior. You can drive increasingly relevant, personalized campaigns with data collected on individual customer behavior. Apply machine learning to all the ways the consumer is interacting with you and get a 360-degree view of the customer—tracking, understanding and gaining insight across all channels—that then informs marketing and merchandising to enable the delivery of the highest value offers. Improve your business performance by turning data into relevant context—turning all that exhaust into relevant insights. Get the right offer back to the customer at the right time – on the web, on their mobile phone, at the shelf, or at the cash register. In our example here, we have a customer on their home computer demonstrating the many ways shoppers are informed about the products and services they want. This phase of the customer journey is a lot like Pinterest, as they plan, combine, view, and get inspiration. Wanting to attract customers into your online and physical storefronts, you want to be in the showcases where your customers are. Monitor Instagram behavior, and turn that into knowledge of your customer. Collect traffic from Twitter, Facebook, Groupon, etc., and use that to drive traffic in and yield greater returns based on relevancy and upsell. The best offers require context and relevance. Use location tracking on carts, cell phones or video cameras to gain a more complete picture of the customer in-store experience. Combine that insight with what you’ve learned from the customer’s pre-shopping experiences, and send personalized recommendations and promotions based on customer location and preferences. Target your customers in store with store apps, so that they continue their product searching in your store. Keep attention in the store with more effective real time offers and segmentation models. Help your customers to see your store as a destination. In Retail, the recent surge in the presence of edge devices such as point of service solutions, digital signage, kiosks and handheld devices has become just as important in the store as applications for mobile phones, tablets, and PCs for consumers. Enable customers to simply tap their smart phone at the cash register to pay—and be on their way, excited to share about their recent shopping success. Create a leading-edge solution based on Microsoft technologies, and you’ll realize the untapped potential of your business data, giving you the competitive edge that comes from providing the best in customer service. Enable a personal, seamless, and differentiated shopping experience.
- Retail Environment Forecast product availability and optimize inventory management by accessing manufacturer, supply chain and distribution data Tailor promotional content to specific audiences by dynamically manage digital promotions across smart devices in stores (e.g. on kiosks, smart vending machines, check out stations, digital advertising signs, etc) Identify the most productive merchandising treatments by integrating data from smart shelves, point of sale, promotions and additional smart devices in the retail environment Facilitate comparisons of retail floors to planograms using data from RFID tags, sensors, and/or smart devices Consumer Home Enable improvements in design, forecast product demand, and track product use with data transmitted from smart devices and sensors (e.g. RFID tags on packaging) Smart fridge, sink, stove, light switch, etc all track resource consumption metrics Better target campaigns and understand consumer purchasing patterns by tracking consumer interactions with promotions across channels and devices (e.g. smart TVs, gaming, computers, smart phones, etc.) Global Ops Customer Insights: Develop a 360 degree view of customer experiences from data transmitted from smart devices and sensors within retail and home environments to better understand consumer sentiments and more accurately segment buyers R&D: Improve existing product design and develop new products through insights from analysis of aggregated data War Room: Measure demand and centralize control of promotions and supply chain from a single location by viewing data from a variety of sources (e.g. manufacturing and distribution chains, retail environments, customer homes, etc.) in near real-time
- IoT enables hospitality and travel organizations to create personal, seamless, and differentiated guest experiences while gaining business agility. The following are just a few quick examples to get you thinking. Airline With the proliferation of mobile and online applications replacing traditional face-to-face interactions, passengers are demanding a seamless traveler experience. Microsoft is delivering capabilities to transform the passenger experience from the curb to the gate. Provide your customers with the best deal, the best experience, and a real relationship with their favorite company. IoT solutions dramatically improves your ability to understand and serve your customers. Enable passengers to self-check bags with RFID luggage tags associated with frequent flyer data, reducing baggage loss and infrastructure requirements and providing passengers with more time to shop in retail stores and order services. Retail stores can send context-relevant promotions as passengers walk by. You can also send notifications regarding gate changes or departure times. Then, enable restaurant recommendations for passengers near a new gate assignment. Save millions of dollars with more accurate arrival time predictions. Use machine learning, sensor data, weather data, and other inputs to fine tune predictions and provide ground crews and passengers with the most accurate arrival information. Differentiate yourself in the highly-competitive airline industry by offering new in-flight customer experiences. Microsoft is helping transform onboard sales into an online, fast, connected experience. Integrate with payment acquirers so that flight attendants get an instantaneous response when swiping a credit card during onboard sales transactions. Engage directly with your passengers, surround them with brand information and entertainment, and bring the richness of online information to the plane. In-flight engagement solutions enable the comprehensive and connected consumer experience and increased ancillary services. With modern electronic flight bag applications, pilots employ a single device for planning, filing, and flying, reducing the weight required for paper flight books on-board. The same device provides collaboration and corporate communications over a highly secure connection, whether swapping shifts with other pilots, consuming training, or simply checking and responding to email. Together, these solutions come together to create a seamless, connected experience for flight crews and travelers. Cruise Use business intelligence to analyze the revenue-generating performance of the various venues and activities onboard ships to identify best practices that generate the most profit and deliver the best guest experience. Tap into existing resources to create new business intelligence—gather information from multiple connected devices and systems, including POS terminals, ticketing systems, and in-room amenities. The more information you can collect about guests, the more you can customize your products for them. For example, as ships travel between ports and passenger loads and demographics change, segment passengers by demographic and analyze their propensity to spend and participate in spa, photo, retail, laundry, and other areas of the ship. Dynamically change your offerings, retail inventory, and activities to match the preferences of the guests onboard. Learning behavior patterns by passenger groups and adjusting offerings and programs to better reflect passenger preferences increases revenue and gives customers a better experience. Centrally monitor critical ship assets and reduce equipment downtime. Streamline formerly manual maintenance processes with proactive response to near real-time data. Meaningful data from sensors and intelligent edge devices are all available securely in the cloud to provide access to needed information on mobile apps, via a Web browser or through text alerts. Provide an advanced field maintenance solution, enabling work flows for utility work crews through hand-held devices. Instead of going to an office, maintenance technicians can get work orders electronically from anywhere on the ship or dock. In the past, it may have taken more than 30 minutes just for technicians to pick up a new work order and return to the site. But now they can quickly retrieve a work order on their devices, finish the job, and notify people that the project is complete. Connecting handheld devices with existing IT infrastructure and food storage equipment can improve workflow throughout the ship, including reducing food-inspection time. To monitor food temperature, deliver inspection tasks and checklists to handheld devices. An employee can use the device’s built-in RFID sensor to read tags installed in coolers. Within seconds, the device downloads temperature records collected during the previous visit and compares them to the current reading. The device immediately alerts the user if the cooler is non-compliant and suggests corrective actions to resolve the problem. Then it sends a message to the facility maintenance team to check the cooler, and the employee moves on to inspect the next station. Use an integrated temperature probe on devices to monitor food temperatures on buffet lines and other open areas. The automated processes replace digital kitchen thermometers and paper logs. Instead of manually transferring records from logs to spreadsheet software, inspectors can immediately run reports against the data collected. Rail Ensure passengers reach their destinations on time and in the greatest comfort possible. Provide passengers with a better experience—for example, supply information on digital signs and to passengers’ mobile devices. Improve on-board service for passengers and gather data to understand purchase patterns and make accurate stock decisions. Implement a point-of-sale solution that connects remotely to financial and business systems and aid transactions from train carts and cafes. Reduce service delays and the cost of equipment downtime. Streamline formerly manual processes with proactive response to near real-time data. Meaningful data from sensors and intelligent edge devices — to closely monitor temperature, vibration, humidity, fault warnings and system alerts — are all available securely in the cloud to provide access to needed information on mobile apps, via a Web browser or through text alerts. Provide an advanced field maintenance solution, enabling work flows for utility work crews through hand-held devices. Centrally monitor critical station assets, such as escalators, elevators, and HVAC control systems. Connected security networks and personnel heighten security visibility to save more lives. Hospitality Drive improvements in your business by reducing travel stress and uncertainty, and by encouraging collaboration among your customers and employees. Implement technologies that offer a more consistent and personalized experience across various channels. Provide guests with a connected tablet to personalize and customize room settings, including the thermostat, lighting, windows shades, and stereo—and save their preferences. When they visit any of your other hotels around the globe, their room can automatically sync with their preferences. Enable the property manager to view room statuses as well, and send staff to repair any issues or change a light bulb as needed. Create a more unique and on-brand experience with guests; device apps can provide guests with information about the hotel, its offerings, and local attractions and act as a valuable source of information that you can use to build customer connections. Offer guests interactive touch-enabled computing to enhance their social experience at your hotels with personalized, highly secure computing experiences. With custom apps, such as an online concierge, capture guest information to improve service delivery and increase competitive advantage. Quick Service Restaurant Improve service with a solution that is fast, secure, and reliable, and drive better decisions with greater data access. Ease business expansion and management. Cut time to open new restaurants and easily push the latest data to existing locations. When you open a new store, create a package that you can deploy quickly though the cloud, and deliver all the necessary information with the click of a button. You can also update software, menu items, prices, coupons and more across multiple locations. Managers have instant access to current sales, inventory, and workforce information. Improve efficiency and enhance the customer experience. Install a point-of-service solution with easy-to-use, engaging self-service touch screens as well as kitchen displays and order confirmation boards connected to a corporate network and cloud-based services. Take orders faster and more accurately so that customers feel good about their visits. Electronically store and search receipts. Control food and labor costs to improve profitability. Managers can look at daily inventory of food items as well as when employees signed in and out of shifts. Connected inventory management enables much more accurate correlation between actual and ideal cost. With alerts from sensors installed in cookers, refrigerators, coffee machines, and more, easily identify mechanical problems and address them quickly. Configure notifications on employee devices of restaurant equipment maintenance needs. Use built-in RFID sensors to read tags installed in coolers. Use devices with integrated temperature probes to monitor food temperatures on buffet lines, in food cases and in other open areas.
- Key goal of slide: As we think about Azure services for IoT, there are a collection of capabilities involved. First there are producers. These can be basic sensors, small form factor devices, traditional computer systems, or even complex assets made up of a number of data sources. Next we have the Event Ingestion capabilities within and around Azure . The primary destination is Service Bus Event Hubs, but this relies on client agent technology either at the edge device level or within a field or cloud gateway. As data is ingressed to Azure, there can be a number of destinations engaged. Traditional database technology, table or blob, or even more complex destinations like Document DB are possible As this data is processed in Azure, there are a number of capabilities that can be utilized. Machine Learning, HD Insight, Stream Analytics are examples of tools that can process the data in various ways. Finally the concept of data presentation uses Azure services. Data may populate a LOB portal, be pushed to apps, or presented in analytics and productivity tools. Through all of these areas, there is the possibility of utilizing existing investments either within your Azure environment, or elsewhere.
- 1)Copy the data into the Azure Data Lake 2)Massage/filter the data using Hadoop (or skip using Hadoop and use stored procedures in SQL DW/DB to massage data after step #5) 3)Pass data into Azure ML to build models using Hive query (or pass in directly from Azure Data lake) 4)Azure ML feeds prediction results into the data warehouse 5)Non-relational data in Azure Data Lake copied to data warehouse in relational format (optionally use PolyBase with external tables to avoid copying data) 6)Power BI pulls data from data warehouse to build reports and maps (Power View) 7)Azure Data Lake captures metadata from Azure Data Lake and SQL DW/DB 8) Power BI and Excel can pull data from the Azure Data Lake via HDInsight
- Connected cow: http://www.ibtimes.co.uk/connected-cattle-how-wearables-cloud-help-farmers-get-their-cows-pregnant-1499220 Face images, voice detection levels
- If you have any questions on any of the customer stories, please contact Oliver Chiu
- If you have any questions on any of the customer stories, please contact Oliver Chiu
- https://customers.microsoft.com/Pages/CustomerStory.aspx?recid=18356
- If you have any questions on any of the customer stories, please contact Oliver Chiu
- If you have any questions on any of the customer stories, please contact Oliver Chiu
- If you have any questions on any of the customer stories, please contact Oliver Chiu
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