At our recent SNIA Networking Storage Forum (NSF) webcast “Revving up Storage for Automotive” our expert presenters, Ryan Suzuki and John Kim, discussed storage implications as vehicles are turning into data centers on wheels. If you missed the live event, it is available on-demand together with the presentations slides. Our audience asked several interesting questions on this quickly evolving industry. Here are John and Ryan’s answers to them. Q: What do you think the current storage landscape is missing to support the future of IoV [Internet of Vehicles]? Are there any identified cases of missing features from storage (edge/cloud) which are preventing certain ideas from being implemented and deployed? [Ryan] I would have to say no, currently there are no missing features in edge or cloud storage that are preventing ideas from being implemented. If anything, more vehicles need to adopt both wireless connectivity and the associated systems (IVI, ADAS/AD) to truly realize IoV. This will take some time as these technologies are just beginning to be offered in vehicles today. There are 200 million vehicles on the road in the US while in a typical year 17 million new vehicles are sold. [John] My personal opinion is no—the development of the IoV is currently limited by a combination of AI training power in the datacenter, compute power within the vehicles, wireless bandwidth (such as waiting for the broader rollout of 5G), and the development of software for new vehicles. Possibly the biggest limit is the slow rate of replacement of existing non-connected vehicles with IoV-capable. The IoV will definitely require more and possibly smarter storage in the datacenter, cloud and edge, but that storage is not what is limiting or blocking the faster rollout of IoV. Q: Talking from a long-term view, is on-board storage the way to go or will we be shifting to storage at the network edge given high bandwidth network like 5G is flourishing? [Ryan] On-board storage will remain in vehicles and continue to grow because vehicles must be fully operational from a driving perspective even if a wireless connection (5G or otherwise) cannot be established. For example, systems in the vehicle required for safe driving (ADAS/AD) must operate independent of an outside connection. In addition, data collected during operation may need to be stored in the event of a slow or intermittent connection to avoid loss of data. Q: What is the anticipated hourly storage needed? At one point this was in the multiple TB range. [John] HD video (1080p at 30 frames per second) requires from 2-4 GB/hour and 4K video requires 15-20 GB/hour, so if a car has 6 HD cameras and a few additional sensors being recorded, the hourly storage need for a normal ADAS would be 8-30 GB/hour. However, a car being used to train, develop or test ADAS/AD systems would collect multiple video angles, more types of data and higher-resolution video/audio/radar/lidar/performance data, possibly requiring 1-5 TB per hour. Q: Do you know of any specific storage requirement, design etc. in the car or the backend, specifically for meeting the UNECE 155/156? It’s specifically for software update, hence the storage question [Ryan] Currently, there are no specific automotive requirements for storage products to meet UNECE 155/156. This regulation was developed by a regional commission of the UN focused on Europe. While security is a concern and will grow as cars become more connected, in my opinion, an international regulation/standard needs to be agreed upon to ensure a consistent level of security for all vehicles in all regions. Q: Does automotive storage need to be ASIL-B or ASIL-D certified? [Ryan] Individual storage components are not ASIL certified as the certification is completed at the system level. For example, systems like vision ADAS, anti-lock braking, and power steering (self-steering), require ASIL-D certification, the highest compliance level. Typically, components that mention a specific level of ASIL compliance have been evaluated at a system hardware level. Q. What type of endurance does automotive storage need, given the average or 99% percentile lifespan of a modern car? [Ryan] It depends on how the storage device is being used. If the device is used for code/application storage such as the AI Inference, the endurance requirement will be relatively low as it only needs to support periodic updates of the code and updates of high-definition maps. Storage devices used for data logging on the other hand, require a higher endurance level as data is written during vehicle operation, uploaded to the cloud later typically through a WiFi connection and then erased. This cycle is repeated every time the vehicle is driven. Q. Will 5G change how much data vehicles can send and receive while driving? [John] Eventually yes, because 5G allows higher wireless/cellular data rates. However, 5G antennas also have shorter range, so more of those antennas and base stations are required for coverage. This means 5G will roll out first in urban centers and will take time to roll out in more rural areas, and vehicles that drive to rural areas will not be able to count on always using the higher 5G data rates. 5G will also be used to connect vehicles in defined environments such as a school campus, bus/truck depot, factory, warehouse or police station. For example, a robot operating only within a warehouse could count on having 5G access all the time, and a bus, police car or ADAS/AD training car could store terabytes of data in the vehicle and upload it easily over a local 5G connection once it returns to the garage or station. Q. In autonomous driving, are all the AI compute capabilities and AI rules or training stored inside each car? Or are AD cars relying somewhat on AI running somewhere in the cloud? [John] Most of the AI rules for actually driving (AI inferencing) must be stored inside each car because there isn’t enough time to consult a computer (or additional rules) stored in the cloud and use them for real-time driving decisions. The training data and machine learning training algorithms used to create the training rules are typically stored in the cloud or in a corporate data center. Updated training rules, navigation data, and vehicle system software updates can all be stored in the cloud and pushed out to vehicles on a periodic basis. Traffic or weather data can be stored in the cloud and sent to vehicles (or to phones in vehicles) as often as several times each minute. Q. Does the chip shortage mean car companies are putting less storage inside new cars than they think they should? [Ryan] Not from what I have seen.  For vehicles currently in production, the designs are locked and with a limited number of vehicles OEMs can produce, they have shifted production to higher-end models to maximize profit. This means the systems in these vehicles may actually use higher amounts of storage to support the features. For new vehicle development, storage capacities continue to grow in order to enable new applications including IVI and ADAS. [John] Generally no, the manufacturers are still putting in whatever amount of storage they originally planned for each vehicle and simply limiting the number of vehicles built based on the supply of semiconductors, and the limitations tend to be across several types of chips, not just memory or storage chips. It’s possible in some cars they are using older, different, or more expensive storage components than originally planned in order to get around chip shortages, but the total amount of storage is unlikely to decrease. Q. Can typical data storage inside a car be upgraded or expanded? [Ryan] Due to the shock and vibration vehicles encounter during operation, storage devices typically come in a BGA package and are soldered onto a PCB for higher reliability. Increasing the density would require replacing the PCB for a new board with a higher capacity storage device. Some new vehicles are installing external USB ports that can use USB drives to store non-critical information such as security camera footage while the vehicle is parked. Q. Given the critical nature of AD systems or even engine control software, do car makers do anything special with their storage to ensure high availability or high uptime? How does a car deal with storage failure? [Ryan] In the case of autonomous driving, this is a safety critical system and the reliability is examined at a system level. In an AD system, there are typically multiple SOCs not only to handle the complex computational tasks, but also for redundancy. In the event the main SOC system fails, another SOC can take over to ensure the vehicle continues to operate safely. From a storage standpoint, each SOC typically uses its own storage device. Q. You know those black boxes they put in planes (or cars) to record data in case of a crash? Those boxes are designed to survive crashes. Why can’t they build the whole car out of the same stuff? [Ryan] While this would provide an ultimate level of safety for passengers, it is unfortunately not economically feasible. To scale a black box with the approximate volume of a 2.5” hard drive to over 120 ft³ (interior passenger and cargo volume) of a standard mid-size vehicle would be cost prohibitive. [John] It would be too expensive and possibly too heavy to build the entire car like a “black box” data recorder. Also, a black box just needs to be designed to make one small component or data storage very survivable while the entire car needs to act as an impact protection and energy absorption system that maximizes the survivability of the occupants during and after an accident. Q. What prevents hackers from breaching automotive systems and modifying the car’s software or deleting critical data? [John] Automotive systems are typically designed with fewer remote access paths and tighter security to make it harder to breach the system. Usually, the systems require encrypted keys from the vehicle manufacturer to access the systems remotely, and some updates or data deletion may be possible only with physical access to the car’s data port. Also, certain data may be stored on flash or persistent memory within the vehicle to make it harder to delete. Still even with these precautions, a mistake or bug in the vehicle’s software or firmware could allow a hacker to gain unauthorized access in rare cases. Q. Would most automotive storage run as block, file, or object storage? [John] Most of the local storage inside a vehicle and anything storing standardized databases or small logs would probably be block storage, as that typically is easy to use for local storage and/or structured data. Data center storage for AI or ADAS training, vehicle design, or aerodynamic/crash/FEA simulation is usually file-based storage to allow for easy sharing and technical computing across multiple servers. Any archived data for vehicle design, training, simulation, videos, telemetry that is stored outside the vehicle is most likely to be object storage because these are typically larger files with unstructured data that don’t change after creation and need to be retained for a long time. Q. Does automotive storage need to use redundancy like RAID or erasure coding? [Ryan] No, current single-device storage solutions with built-in ECC provide the required reliability.  Implementing a RAID system or erasure encoding would require multiple drives significantly driving up the cost.  Electronics currently account for 40% of a new vehicle’s total cost and it is expected to continue growing.  Switching from an existing solution that meets system requirements to a storage solution that is multiple times the cost is not practical.

The convergence of 5G, Edge Compute and Artificial Intelligence (AI) promise to be catalyst for continued digital transformation. For many industries, it will be a game-changer in term of how business in conducted. On January 27, 202, the SNIA Cloud Storage Technologies Initiative (CSTI) will take on this topic at our live webcast “5G Industrial Private Networks and Edge Data Pipelines.” Advanced 5G is specifically designed to address the needs of verticals with capabilities like enhanced mobile broadband (emBB), ultra-reliable low latency communications (urLLC), and massive machine type communications (mMTC), to enable near real-time distributed intelligence applications. For example, automated guided vehicle and autonomous mobile robots (AGV/AMRs), wireless cameras, augmented reality for connected workers, and smart sensors across many verticals ranging from healthcare and immersive media, to factory automation. Using this data, manufacturers are looking to maximize operational efficiency and process optimization by leveraging AI and machine learning. To do that, they need to understand and effectively manage the sources and trustworthiness of timely data. In this presentation, our SNIA experts will take a deep dive into how:

• Edge can be defined and the current state of the industry
• Industrial Edge is being transformed
• 5G and Time-Sensitive Networking (TSN) play a foundational role in Industry 4.0
• The convergence of high-performance wireless connectivity and AI create new data-intensive use cases
• The right data pipeline layer provides persistent, trustworthy storage from edge to cloud

I encourage you to register today. Our experts will be ready to answer your questions.

Cloud to Edge infrastructures are rapidly growing.  It is expected that by 2025, up to 75% of all data generated will be created at the Edge.  However, Edge is a tricky word and you’ll get a different definition depending on who you ask. The physical edge could be in a factory, retail store, hospital, car, plane, cell tower level, or on your mobile device. The network edge could be a top-of-rack switch, server running host-based networking, or 5G base station.

The Edge means putting servers, storage, and other devices outside the core data center and closer to both the data sources and the users of that data—both edge sources and edge users could be people or machines.

 This trilogy of SNIA Networking Storage Forum (NSF) webcasts will provide:

1. An overview of Cloud to Edge infrastructures and performance, cost and scalability considerations
2. Application use cases and examples of edge infrastructure deployments
3. Cloud to Edge performance acceleration options

Attendees will leave with an improved understanding of compute, storage and networking resource optimization to better support Cloud to Edge applications and solutions.

At our first webcast in this series on January 26, 2022, “Storage Life on the Edge: Managing Data from the Edge to the Cloud and Back” you‘ll learn:

• Data and compute pressure points: aggregation, near & far Edge
• Supporting IoT data
• Analytics and AI considerations
• Understanding data lifecycle to generate insights
• Governance, security & privacy overview
• Managing multiple Edge sites in a unified way

Register today! We look forward to seeing you on January 26th.

Cloud to Edge infrastructures are rapidly growing.  It is expected that by 2025, up to 75% of all data generated will be created at the Edge.  However, Edge is a tricky word and you’ll get a different definition depending on who you ask. The physical edge could be in a factory, retail store, hospital, car, plane, cell tower level, or on your mobile device. The network edge could be a top-of-rack switch, server running host-based networking, or 5G base station. The Edge means putting servers, storage, and other devices outside the core data center and closer to both the data sources and the users of that data—both edge sources and edge users could be people or machines. This trilogy of SNIA Networking Storage Forum (NSF) webcasts will provide:

1. An overview of Cloud to Edge infrastructures and performance, cost and scalability considerations
2. Application use cases and examples of edge infrastructure deployments
3. Cloud to Edge performance acceleration options

Attendees will leave with an improved understanding of compute, storage and networking resource optimization to better support Cloud to Edge applications and solutions. At our first webcast in this series on January 26, 2022, “Storage Life on the Edge: Managing Data from the Edge to the Cloud and Back” you‘ll learn:

• Data and compute pressure points: aggregation, near & far Edge
• Supporting IoT data
• Analytics and AI considerations
• Understanding data lifecycle to generate insights
• Governance, security & privacy overview
• Managing multiple Edge sites in a unified way

Register today! We look forward to seeing you on January 26th.

The ability to run analytics from the data center to the Edge, where the data is generated and lives creates new use cases for nearly every business. The impact of Edge computing on storage strategy was the topic at our recent SNIA Cloud Storage Technologies Initiative (CSTI) webcast, “Extending Storage to the Edge – How It Should Affect Your Storage Strategy.” If you missed the live event, it’s available on-demand. Our experts, Erin Farr, Senior Technical Staff Member, IBM Storage CTO Innovation Team and Vincent Hsu, IBM Fellow, VP & CTO for Storage received several interesting questions during the live event. As promised, here are answers to them all. Q. What is the core principle of Edge computing technology? A. Edge computing is an industry trend rather than a standardized architecture, though there are organizations like LF EDGE with the objective of establishing an open, interoperable framework. Edge computing is generally about moving the workloads closer to where the data is generated and creating new innovative workloads due to that proximity. Common principles often include the ability to manage Edge devices at scale, using open technologies to create portable solutions, and of ultimately doing all of this with enterprise levels of security. Reference architectures exist for guidance, though implementations can vary greatly by industry vertical. Q. We all know connectivity is not guaranteed – how does that affect these different use cases? What are the HA implications? A. Assuming the requisite retry logic is in place at the various layers (e.g. network, storage, platform, application) as needed, it comes down to a question of how much can each of these use cases tolerate delays until connectivity is obtained again. The cloud bursting use case would likely be impacted by connectivity delays if the workload burst to the cloud for availability reasons or because it needed time-sensitive additional resources. When bursting for performance, the impact depends on the length of the delay vs. the length of the average time savings gained when bursting. Delays in the federated learning use case might only impact how soon a model gets refreshed with updated data. The query engine use case might avoid being impacted if the data has been pre-fetched before the connectivity loss occurred. In all of these cases it is important that the storage fabric resynchronizes the data to be a single unified view (when configured to do so.) Q. Heterogeneity of devices is a challenge in Edge computing, right? A. It is one of the challenges of Edge computing. How the data from Edge devices is stored on an Edge server may also vary depending on how that data gets shared (e.g. MQTT, NFS, REST). Storage software that can virtualize accessing data on an Edge server across different file protocols could simplify application complexity and data management. Q. Can we say Edge computing is an opposite of cloud computing? A. From our perspective, Edge computing is an extension of hybrid cloud. Edge computing can also be viewed as complementary to cloud computing since some workloads are more suitable for Cloud and some are more suitable for Edge. Q. What assumptions are you making about WAN bandwidth? Even when caching data locally the transit time for large amounts of data or large amounts of metadata could be prohibitive. A. Each of these use cases should be assessed under the lens of your industry, business, and data volumes to understand whether any potential latency that’s part of any segment of these flows would be acceptable to you. WAN acceleration, which can be used to ensure certain workloads are prioritized for guaranteed qualities of service, could also be explored to improve or ensure transit times. Integration with Software Defined Networking solutions may also provide mechanisms to mitigate or avoid bandwidth problems. Q. How about the situation where data resides in on-premises data center and machine learning tools are in the cloud to build the model and the goal is not to move the data (security) to cloud, but run and test model only on-premises and score and improve and finally implement? A. The Federated Learning use case allows you to keep the data in the on-premises data center while only moving the model updates to the cloud.  If you also cannot move model updates and if the ML tools are containerized and/or the on-premises site can act as a satellite location for your cloud, it may be possible to run the ML tools in your on-premises data center.

Everyone knows data is growing at exponential rates. In fact, the numbers can be mind-numbing. That’s certainly the case when it comes to genomic data where 40,000PB of storage each year will be needed by 2025. Understanding, managing and storing this massive amount of data was the topic at our SNIA Cloud Storage Technologies Initiative webcast “Moving Genomics to the Cloud: Compute and Storage Considerations.” If you missed the live presentation, it’s available on-demand along with presentation slides. Our live audience asked many interesting questions during the webcast, but we did not have time to answer them all. As promised, our experts, Michael McManus, Torben Kling Petersen and Christopher Davidson have answered them all here. Q. Human genomes differ only by 1% or so, there’s an immediate 100x improvement in terms of data compression, 2743EB could become 27430PB, that’s 2.743M HDDs of 10TB each. We have ~200 countries for the 7.8B people, and each country could have 10 sequencing centers on average, each center would need a mere 1.4K HDDs, is there really a big challenge here? A. The problem is not that simple unfortunately. The location of genetic differences and the size of the genetic differences vary a lot across people. Still, there are compression methods like CRAM and PetaGene that can save a lot of space. Also consider all of the sequencing for rare disease, cancer, single cell sequencing, etc. plus sequencing for agricultural products. Q. What’s the best compression ratio for human genome data? A. CRAM states 30-60% compression, PetaGene cites up to 87% compression, but there are a lot of variables to consider and it depends on the use case (e.g., is this compression for archive or for withing run computing). Lustre can compress data by roughly half (compression ratio of 2), though this does not usually include compression of metadata. We have tested PetaGene in our lab and achieved a compression ratio of 2 without any impact on the wall clock. Q. What is the structure of the Genome processed file? It is one large file or multiple small files and what type of IO workload do they have? A. The addendum at the end of this presentation covers file formats for genome files, e.g. FASTQ, BAM, VCF, etc. Q. It’s not just capacity, it’s also about performance. Analysis of genomic data sets is very often hard on large-scale storage systems. Are there prospects for developing methods like in-memory processing, etc., to offload some of the analysis and/or ways to optimize performance of I/O in storage systems for genomic applications? A. At Intel, we are using HPC systems that are using an IB or OPA fabric (or RoCE over Ethernet) with Lustre. We are running in a “throughput” mode versus focusing on individual sample processing speed. Multiple samples are processed in parallel versus sequentially on a compute node. We use a sizing methodology to rate a specific compute node config to provide, for example, our benchmark on our 2nd Gen Scalable processors. This benchmark is 6.4 30x whole genomes per compute node per day. Benchmarks on our 3^rd Gen Scalable processors are underway. This sizing methodology allows for the most efficient use of compute resources, which in turn can alleviate storage bottlenecks. Q. What is the typical access pattern of a 350G sequence? Is full traversal most common, or are there usually focal points or hot spots? A. The 350GB is comprised of two possible input file types and 2 output file types. For input file types they can be either a FASTQ file, which is an uncompressed, raw text file, or a compressed version called a uBAM (u=unaligned). The output file types are a compressed “aligned” version called a BAM file, output of the alignment process; and a gVCF file which is the output of the secondary analysis. This 350GB number is highly dependent on data retention policies, compression tools, genome coverage, etc. Q. What is the size of a sequence and how many sequence are we looking at? A. If you are asking about an actual sequence of 6 billion DNA bases (3 billion base pairs) then each base is represented by 1 byte so you have 6 GB.  However, the way the current “short read” sequencers work is using the concept of coverage. This means you run the sequence multiple times, for example 30 times, which is referred to as “30x”. So, 30 times 6GB = 180GB. In terms of My “thought experiment” I considered 7.8B sequences, one for each person on the planet at 30x coverage. This analysis use the ~350GB number which all the files mentioned above. Q. Can you please help with the IO pattern question? A. IO patterns are dependent on the applications used in the pipeline. Applications like GATK baserecal and SAMtools have a lot of random IO and can benefit from the use of SSDs. On the flipside, many of the applications are sequential in nature. Another thing to consider is the amount of IO in relation to the overall pipeline, as the existence of random IO does not inherently mean the existence of a bottleneck. Q. You talked about Prefetch the data before compute which needs a compressed file signature of the actual data and referencing of it. Can you please share some details of what is used now to do this? A. The current implementation of Prefetch via workload manager directives (WLM) is based on metadata queries done using standard SQL on distributed index files in the system. This way, any metadata recorded for a specific file can be used as a search criterion. We’re also working on being able to access and process the index in large concatenated file formats such as NetCDF and others which will extend the capabilities to find the right data at the right time. Q. For Genome and the quantum of data do you see Quartz Glass a better replacement to tape? A. Quartz Glass is an interesting concept but one of many new long term storage technologies being researched. Back in 2012 when this was originally announced by Hitachi, I thought it would most definitely replace many storage technologies, but it’s gone very quiet the last 5+ years so I’m wondering whether this particular technology survived.

“Big Data” has pushed the storage envelope, creating a seemingly perfect relationship with Cloud Storage. But local storage is the third wheel in this relationship, and won’t go down easy. Can this marriage survive when Big Data is being pulled in two directions? Should Big Data pick one, or can the three of them live happily ever after? This will be the topic of discussion on October 21, 2021 at our live SNIA Cloud Storage Technologies webcast, “Cloud Storage and Big Data, A Marriage Made in the Clouds.” Join us as our SNIA experts will cover:

• A short history of Big Data
• The impact of edge computing
• The erosion of the data center
• Managing data-on-the-fly
• Grid management
• Next-gen Hadoop and related technologies
• Supporting AI workloads
• Data gravity and distributed data

Register today! Our speakers will be ready to take your questions and black-tie is not required for this wedding!

The study of genomics in modern biology has revolutionized the discovery of medicines and the COVID pandemic response has quickened genetic research and driven the rapid development of vaccines. Genomics, however, requires a significant amount of compute power and data storage to make new discoveries possible. Making sure compute and storage are not a roadblock for genomics innovations will be the topic of discussion at the SNIA Cloud Storage Technologies Initiative live webcast “Moving Genomics to the Cloud: Compute and Storage Considerations.” This session will feature expert viewpoints from both bioinformatics and technology perspectives with a focus on some of the compute and data storage challenges for genomics workflows. We will discuss:

• How to best store and manage large genomics datasets
• Methods for sharing large datasets for collaborative analysis
• Legal and ethical implications of storing shareable data in the cloud
• Transferring large data sets and the impact on storage and networking

Join us for this live event on September 9, 2021 for a fascinating discussion on an area of technology that is rapidly evolving and changing the world.

Data gravity has pulled computing to the Edge and enabled significant advances in hybrid cloud deployments. The ability to run analytics from the datacenter to the Edge, where the data is generated and lives, also creates new use cases for nearly every industry and company. However, this movement of compute to the Edge is not the only pattern to have emerged. How might other use cases impact your storage strategy? That’s the topic of our next SNIA Cloud Storage Technologies Initiative (CSTI) live webcast on August 25, 2021 “Extending Storage to the Edge – How It Should Affect Your Storage Strategy” where our experts, Erin Farr, Senior Technical Staff Member, IBM Storage CTO Innovation Team and Vincent Hsu, IBM Fellow, VP & CTO for Storage will join us for an interactive session that will cover:

• Emerging patterns of data movement and the use cases that drive them
• Cloud Bursting
• Federated Learning across the Edge and Hybrid Cloud
• Considerations for distributed cloud storage architectures to match these emerging patterns

It is sure to be a fascinating and insightful discussion. Register today. Our esteemed expert will be on-hand to answer your questions.

Recently, the SNIA Cloud Storage Technologies Initiative (CSTI) hosted a live webcast “Cloud Analytics Drives Airplanes-as-a-Service” with Ben Howard, CTO of KinectAir. It was a fascinating discussion on how analytics is making this new commercial airline business take off.  Ben has a history of innovation with multiple companies working on new flight technology, analytics, and artificial intelligence. In this session, he provided several insights from his experiences on how analytics can have a significant impact on every business. In the course of the conversation, we covered several questions, all of which were answered in the webcast. Here’s a preview of the questions along with some brief answers. Take an hour of your time to listen to the entire presentation, we think you’ll enjoy it. Q: What’s different about capturing data for Machine Learning? A: There’s a need to ensure that the data you’re capturing is valid data, and that it will contribute to the bottom line. But AI/ML is less rigorous than some other analytics in that it can absorb a broader array of data formats. Q: What are you gleaning from all the other data sources KinectAir is using? A: KinectAir uses a variety of sources for info, including things like weather, other airline’s schedules and flight plans, FAA data, customer preferences, and many other pieces of data.  This allows it to make quick decisions on relocating aircraft to take potential passengers during a weather or mechanical delay by larger airlines. It also allows the company to make intelligent decisions on flight pricing that can make flight options more attractive to customers. Q: How does predictive analytics impact the business? A: By focusing on the passenger, and identifying the true origin and destination of each passenger, the airline can adjust for different potential airports as well as traffic and weather info to route the passenger. For example, the passenger can be routed to a regional airport slightly farther away than his or her house to allow the airplane to pick up other passengers, thus making the flight less expensive. Airplanes can also be staged near large airports that typically have weather delays to pick up potential passengers with a flight cancelled. Q: Explain how KinectAir is using a Monte Carlo model, and how that works. A: The actual comment was: “So, essentially you’re gambling.”  Ben explained how the company uses all the available information to make an informed bet on what passengers will pay to connect to a specific route. In this way, the company can weigh the odds and find a way to generate a price that will make a sale, but also make a profit. This creates an environment to, “always say yes,” to a customer in a way that works for the customer and the company. In the course of the discussion, we not only discussed KinectAir, but we also talked about using analytics for other businesses. Ben discussed using visualization to improve farming, how to create an analytics strategy to run 100 miles, and how to listen to what customers want while providing what they actually need. We hope you enjoy watching this webcast as much as we did making it.