Gartner Symposium 2018: BMW Group Presentation
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Boulos El-Asmar's presentation on how BMW Group is driving innovation with machine learning and IIoT with Splunk
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Gartner Symposium 2018: BMW Group Presentation
- 1. © 2018 SPLUNK INC.© 2018 SPLUNK INC. Driving Innovation with Machine learning and IIoT at BMW Group Boulos El-Asmar | Research Scientist, AI Project Lead at BMW Group Logistics Matthias Maier | Product Marketing Director, Splunk
- 2. © 2018 SPLUNK INC. 10 USD Each Donation For UNICEF
- 3. © 2018 SPLUNK INC. Splunk Company Overview Global HQs San Francisco London Hong Kong 4,000+ Employees Globally NASDAQ: SPLK Annual Revenue $1.25B+ 89 of the Fortune 100 Rely on Splunk Across 110+ Countries Customers Range From Small Businesses to Large Organizations Largest License: 4+Petabytes/day 16,000+ Customers Splunk4Good $100 Million Commitment to Support Nonprofits, Research & Education
- 4. © 2018 SPLUNK INC. Traditional Data Types Relational Data Reference Data Collectively, relational and reference data represent around 20% of all data.
- 5. © 2018 SPLUNK INC. MACHINE DATA Your data is talking. Are you listening?
- 6. © 2018 SPLUNK INC. Why Is This So Hard? Machine data is real time, messy and unpredictable You don’t always know which questions to ask Requires massive scale
- 7. © 2018 SPLUNK INC. © 2018 SPLUNK INC. OUR MISSION
- 8. © 2018 SPLUNK INC. Alerts, Dashboards, Reports MLTK Industrial Asset Intelligence Easy Data Querying 2014 Vehicle Data Analytics Platform Predicting Traffic Patterns Manufacturing Operations Natural Language 2016 2017 2018 BMW Group and Splunk IT-Security (SIEM) IT-Operations (Monitoring)
- 9. © 2018 SPLUNK INC. Traffic patterns
- 10. © 2018 SPLUNK INC.
- 11. © 2018 SPLUNK INC.
- 12. © 2018 SPLUNK INC. Business experts can access information using text or voice Natural language has allowed us to do easy, intuitive data querying
- 13. © 2018 SPLUNK INC. Ask Alexa
- 14. © 2018 SPLUNK INC.
- 15. © 2018 SPLUNK INC. Making an Ultimate Driving Machine
- 16. © 2018 SPLUNK INC. BMW Group Logistics 1000+ Logistics and Supply Chain Planners
- 17. © 2018 SPLUNK INC. Splunk for Industrial IoT Splunk Machine Learning Toolkit Splunk Natural Language Splunk for Industrial IoT
- 18. © 2018 SPLUNK INC.
Editor's Notes
- Hello Everyone, thanks a lot for joining our session. First of all – I want to congrats everyone who participated – we are doing so well at Splunk and are many Years at Symposium and wanted to give back – not just with an inspiring session with showcasing what BMW Group does with data – we want to give back
- We want to give monetary back and will donate 10 USD for every participant of this session to Unicef
- Your team can start with Splunk on a Laptop and scale up – our largest customer is ingesting every day over 4 peta byte of new data into Splunk and asking business critical questions against it! But about what kind of data are we talking here? As we all know, the world is generating data faster than ever. Every organization I engage with highlights the need to become “data driven.”
- There are two traditional types of data…. There’s relational data, the kind that fits nicely into rows and columns. You’re very familiar with this, and are paying companies like Oracle or Microsoft SQL Server to store it, and to periodically report on it. There’s also reference data, things like Office or Google documents. You know, random files stored in some location. These types of traditional data require a defined structure, or schema, up front. Unfortunately this means, you have to know what you want to do with the data in advance. Making changes to the structures can be very time consuming, and can be very costly. Most of the executives we talk to are still relying on this kind of data management system. They’re getting static reports letting them know what happened in their business yesterday, last week, or last month. At Splunk, we enable customers to make real-time decisions, based on a bigger picture of what’s happening in their business. We do this by making a third type of data available. It’s called machine data, and we make it easily accessible, usable, and valuable.
- Machine data represents the majority of data generated in the world today. So why wouldn’t you want to use it? We are surrounded by machine data. Computers, phones, networks and connected devices are generating massive quantities of it in real time. So can you imagine, tapping into this machine data can give you insights, eliminate blind spots, and enable better and faster decision making? Unfortunately, legacy data management systems don’t work with machine data. I can illustrate this with a scenario we’re all familiar with - booking a flight or a hotel room online. When we book a room online, the hotel stores information in a database that describes who we are, which credit card we use, and whether we’re in their loyalty program. They’d also have the details of our reservation, which property we’ll be staying at, and for how long. If they were using their machine data, they would also know, in real time, details such as: Where we were, from an IP address perspective, when we made the reservation. Whether we experienced any pain resulting from server downtime or backend ecommerce issues while attempting to make the reservation. If we were able to make the reservation on the first… or third try. Which room options we browsed through before selecting a particular option. And if we are indeed who we say we are versus a bot or intruder that is trying to enter the system, which is a potential risk. Machine data is incredibly valuable. So if you were responsible for driving this hotel’s revenue and customer satisfaction up, wouldn’t you like to know in real-time that transaction time is lagging? How about if you were responsible for security? Wouldn’t you like to know in real time that this reservation was made from a potentially risky or unwanted IP address? What about if you were on the marketing team? Would you like to know in real time, versus next month, how your loyalty program guests were reacting to your online specials?
- Getting any insight from machine data has been challenging. This is because legacy systems aren’t built to handle machine data. There’s too much of it. It’s too varied in format. Attempting to predict up front what you want to do with it, or which questions you want to ask of it, is futile, because requirements change so often.
- Splunk was created to address these challenges. We unlock machine data so anyone can ask any question, and get clear answers in real time. MM: To show you the business impact you can have with utilizing machine data for your business we have invited Boulos Asmar, Research Scientist and AI Project Lead at BMW Group Logistics.
- BMW started its journey with one security use case and a small Splunk Enterprise license. We have expanded Splunk usage into IT operations, application delivery, and most recently into natural language search and industrial operations. Today, we are getting so much more value from our data. We can find answers quickly and easily, thanks to Splunk's analytics platform. I want to share with you our journey-- and hopefully inspire you to further your own data journey. <Matthias> Boulos – Mobility is for BMW Group key going forward – however it can be like weather- you can’t predict it. But I understand BMW is changing this using Splunk’s Machine Learning Toolkit to understand how traffic typically flows around Munich. Can you share some of the work you are doing in this area by utilizing machine data from vehicles?
- <Boulos> Sure. We’ve been working on a project around predicting traffic in Munich. These insights have a direct impact on smarter and more efficient mobility. As you can imagine, this can be extremely valuable to a consortium of car makers by predicting when driving events start in the city, or monitoring test cars and performing fleet management. The impact is really huge. From the get-go, we focused on designing a solution that makes it easy to incorporate other sources of information - such as weather, public transport data or event data… perhaps Oktoberfest. Fun fact - some tourists think it runs the whole of October and unfortunately come too late– actually it starts mid-September and ends already in the first week of October. So that’s a tip for next year to make sure our audience won’t be that guy! Let me show you how it works.
- <Boulos> Splunk’s Machine Learning Toolkit provides a comprehensive set of capabilities. Along with Splunk’s native features we were able to build our solution and create reliable models that predict emerging patterns in real-time. What you see here, is a heat map of car driving events from our test fleet in the city of Munich. After one month of training the algorithm we were able to predict car driving events for the following week – down every hour of every day. This is embedded in a Splunk dashboard giving our business experts visibility into traffic flow and dynamics. At the back end, it was easy to create a custom data model with Splunk. As you can see here, in addition to processing data we used the ML SPL API to easily import and train custom algorithms we built. This along with the interactive dashboards helped speed up time to value. Splunk gives us a visual view of what actual versus predictive dynamics would look like for this application and allows experts to analyze data quickly and get the answers they need. If we think about the new era of data analysis that all our businesses are moving into, predictive analytics is going to be critical to success. BMW and Splunk partnered on this project through Splunk’s Machine Learning Advisory Program. Our team is eager to apply what we’ve learned to other divisions at BMW.
- <Matthias> You are a data scientist, not everyone in this room is. What was your experience working with Splunk and the Machine Learning Toolkit for the non-data scientists in the room? <Boulos> Well, I understand Machine Learning can sometimes be intimidating, but the machine learning toolkit made this easy. The interactive dashboards made it simple to bring data in, and even to create our own machine learning custom models. <Matthias> Now for the 2nd use case, can you share with all of us what BMW is doing with Splunk’s new Natural Language Search capabilities. How does ML and NL come together in a real-world scenario?
- <Boulos> Now we know the power of Splunk SPL, but let's be honest, we all struggle with it sometimes. That's why we were so excited to learn about Splunk's Natural Language capabilities at .last year’s user conference. We've been able to apply it to solve two challenges: One, we saw how we can predict traffic patterns using Machine Learning, and mapping natural language search here has allowed us to do easy intuitive data querying. Secondly- our business experts now have access to domain information in Splunk which is helping them to query and ask questions such as ‘show me car drive events’. Users can now easily get the information they need without the need to know the underlaying logic. When you think of Machine Learning and Natural Language Search, you think--how long will it take us to get value. But in just 4 months, we went from project readiness, to setting up our environment, to getting the POC running.
- <Boulos> Now, for the fun part, I am going to show you how this works–
- <Boulos> <Joke> I will say this, Splunk definitely helps make interaction with Alexa so much easier! Although she has some work to do on her German pronunciation. <Matthias> Finally, Boulos, this is the part I have been most eager to learn about - especially with as Splunk recently announced an IoT solution – Industrial Asset Intelligence. How does Splunk help BMW to actually produce and manufacture “an ultimate driving machine”?
- <Boulos> BMW Group uses Splunk extensively in the area of manufacturing to make data available for plant-floor engineers who have to focus on keeping production up and running. That‘s where Splunk bridged the gap for us. Maintenance planners can self-serve to easily correlate data or perform advanced analytics without programming and with very little IT involvement. <Matthias> Now we have heard how BMW is utilizing machine data for predicting traffic and improving mobility, how you use it in the shop floor. Can you give us a taste how you can scale that maybe tens or hundreds of BMW employees can make smarter decisions with machine data?
- <Boulos> At BMW Group logistics, we have been using Splunk to support over 1000 Logistics and supply chain planners around the world. Each planner can login to the Logistics app and work with custom view according to his role and access rights. Thanks to Splunk, BMW Group Logistics can accurately predict packages need, track costs, and get an overview of production plans of all car models and derivatives. The interactive dashboards bring insights over warehouses and containers storage and filling level, and enable real time tracking of car parts and material from the supplier all the way to the manufacturing line. <Matthias> I think it’s incredible that BMW is using Splunk to solve all these different use cases- application delivery to machine learning to industrial operations. Wow, and you started with a single enterprise license. Tell me, how are teams sharing their knowledge of Splunk with each other?
- <Boulos> We could always be doing more. At a digitized company like BMW you can find uses for data everywhere. With so many applications across multiple use cases we are only just beginning to think about how to harness this knowledge. For example look at the work we’re doing around Machine Learning and how valuable it would be to BMW’s manufacturing operations to predict maintenance needs. It is definitely on our roadmap to start building a Splunk center of excellence to share more.
- <Matthias> Last question, tell us something we don’t know about BMW Group. When are the flying cars coming, and more importantly do you get an employee discount? <laugh> <Boulos> You know, I can’t comment on what’s next. But I can tell you this, I do get a discount :). <Matthias> Thanks a lot Boulos for joining us today – and thanks to the audience to spend the time with us – we hope Boulos did inspire you – you’re ready to utilize machine data so that you can ask and get answers to the questions you have! Some of your questions we might be able to answer at the Splunk booth downstairs at the Expo Area, Boulos will be also with us in the next hour or so at the Booth to answer any Q&A – we are looking forward to welcome you! Thanks!