Decision CAMP 2013 - shash hegde - mariner - Is this Skynet? Giving machines a collective memory & intelligence
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The term "internet of things", loosely defined as a world where physical objects are seamlessly integrated into the information network, and where the physical objects can become active participants in business processes has gained popularity in recent years. The talk will describe the case study of a small but visionary power management company in the Carolinas, which is building the next generation of a smart enterprise. An enterprise modeled on an event driven architecture which can sense and respond to stimuli faster than the competition. Achieving the vision has required our team to use a combination of data warehousing, decision management and analytics. Come learn about the vision, the challenges and also the business drivers for making this ambitious project a reality.
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Decision CAMP 2013 - shash hegde - mariner - Is this Skynet? Giving machines a collective memory & intelligence
- 1. Giving machines a collective memory & intelligence Shash.Hegde@mariner-usa.com @DataCzar © 2013
- 3. Power Management • Different business model • Maximize life of asset • Fixed & Low TCO
- 4. Current State The unexamined data is not worth storing Image source: http://innovative-results.com/wp-content/uploads/2011/05/emotional-eating.jpg
- 6. © 2013 Inspect everything manually
- 7. Vision Give machines a memory & intelligence Automate the “information factory” Platform thinking Change strategy on a dime – based on data Scale Semi-autonomous organization Monetize
- 9. Platform Thinking Pipes vs. Platforms
- 10. Self Service Platforms • Analytics – DW + Predictive Analytics + Tableau • Cloud – Microsoft Azure • Decision management – SMARTS by Sparkling Logic © 2013
- 11. © 2013
- 12. Diversify – Add & Assimilate XML Documents Video Twitter ERP JSON CRM Log Files Many data sets combined tell you more than they do separately. Sensor Data © 2013 Legacy E-mail Image Source: http://vi.sualize.us/view/wulfbane/3ca4c6312aa54562c24ef4739df1259c/ OLAP 12
- 13. Forward looking data • Predictive Analytics • Prescriptive Analytics • Machine Learning •+ • “Knowledge University”
- 15. Minimize Decision Fatigue Automate – high volume, repetitive decisions © 2013
- 17. Simplified Architecture Knowledge University Customer site ERP Decision Engine Sensor Management Sensor Sensor Sensor Data Collection Data Warehouse Reports Sensor Service Technician © 2013
- 18. Change strategy on a dime Porter’s 5 forces © 2013
- 21. Monetize Platform as a Service (PaaS) Automated remote device management Customer 1 Customer 3 Customer 2
- 22. Challenges • New to decision management • Cultural & growth issues • Data collection & integration (API’s vs ETL etc.) • Security © 2013
- 23. Summary • Automate • Platform thinking (self service) • Semi-autonomous organization • Change strategy on a dime • Scale & monetize © 2013
Editor's Notes
- I work with a consulting group, Mariner, and this is the story of our customer who is doing some interesting work. Our focus as a company is building data warehouses and doing analytics. 15 years. New territory for us. New territory for the customer.
- Intersection of energy & analytics. Industrial internet. “Internet of things”.Most companies are focused on the consumer space. These guys are focused on enterprise. Distribution centers &Business model innovation: Maximize the life of the asset. Give shaving – razor blade example.Indistribution centers you have lots of big machines like forklifts that need guaranteed power.Increasingly the modern DC’s have robots.Customers don’t want downtime. The Battery companies want to sell more units.
- Ingesting and generating lots of data. Examining it through Microsoft Access. Mom & pop shop. No ERP – quick books. Normally we would disqualify them as a customer but their growth and innovation ideas made for a special acse.
- IT will not scale
- Battery bank.Go check each battery.Imagine going to just battery number 1234.Reduce the number of technicians. Reduce interruptions.Increase the life of the asset. Too much is bad. Too little is worse.
- First 4 of these things have a direct tie in with Decision Management.
- http://platformed.info/why-business-models-fail-pipes-vs-platforms/PIPESPipes have been around us for as long as we’ve had industry. They’ve been the dominant model of business. Firms create stuff, push them out and sell them to customers. Value is produced upstream and consumed downstream. There is a linear flow, much like water flowing through a pipe.We see pipes everywhere. Every consumer good that we use essentially comes to us via a pipe. All of manufacturing runs on a pipe model. Television and Radio are pipes spewing out content at us. Our education system is a pipe where teachers push out their ‘knowledge’ to children. Prior to the internet, much of the services industry ran on the pipe model as well.This model was brought over to the internet as well. Blogs run on a pipe model. An ecommerce store like Zappos works as a pipe as well. Single-user SAAS runs on pipe model where the software is created by the business and delivered on a pay-as-you-use model to the consumer. PLATFORMSHad the internet not come up, we would never have seen the emergence of platform business models. Unlike pipes, platforms do not just create and push stuff out. They allow users to create and consume value. At the technology layer, external developers can extend platform functionality using APIs. At the business layer, users (producers) can create value on the platform for other users (consumers) to consume. This is a massive shift from any form of business we have ever known in our industrial hangover.TV Channels work on a Pipe model but YouTube works on a Platform model. Encyclopaedia Britannica worked on a Pipe model but Wikipedia has flipped it and built value on a Platform model. Our classrooms still work on a Pipe model but Udemy and Skillshare are turning on the Platform model for education.
- This serves as the organizations memory.
- Somewhat like IBM Watson.
- Decision Requirements: Integrate “decision engine” into ETL to generate specific actionsTransport selected data to rules engine for processingBuild a defined set of rulesTransport results back to data mart for inclusion in analysis cubeGraphical user interfaceProvide screen shots of interface and data flowSingle integrated softwareAble to be maintained by business users
- We don’t want technicians making erroneous decisions. Nearly 45% of decisions are made based on habits.
- Charger profile adjustment with manual override.
- The data warehouse serves as the collective memory.The decision rules engine along with some predictive analytics that we run at the warehouse serves as the intelligence.Initially, the goal is to monitor the data and dispatch Now we will be able to predictively
- Each additional customer you add – the better your predictive analytics will become because of a larger corpus of data. And so on.