Realizing Business Value from Convergence of IoT + Big Data Technologies. Aditya Thadani Phil Andreoli

Transcription

1 Realizing Business Value from Convergence of IoT + Big Data Technologies Aditya Thadani Phil Andreoli

2 CONVERGENCE OF IOT+BIG DATA TECHNOLOGIES Agenda Business Opportunity & Outcomes Where does IoT (Internet of Things) fit in Business strategy for Industrial Enterprises? What Business outcomes are Industrial Enterprises looking for from IoT? Technical Challenges How do Big Data technologies align with the IoT opportunity? What are key Technical challenges in realizing these Business Outcomes? Organizational Challenges What are key Organizational challenges in realizing the IoT+Big Data Architecture? 11/25/ MACC CONFERENCE MAKING IT MATTER 2

3 BUSINESS CONTEXT About Ecolab Diversified - Oil&Gas, Food processing, Dairy, Pharmaceuticals, Pulp & Paper, Mining, Textile Care, Hospitality, Food Retail, Restaurants, Healthcare, Global footprint with aggressive growth in emerging markets 11/25/ MACC CONFERENCE MAKING IT MATTER 3

4 IOT IN INDUSTRIAL ENTERPRISES Business Opportunities Where does IoT/M2M fit in business strategy for Industrial Enterprises? What business outcomes are we looking for? 11/25/ MACC CONFERENCE MAKING IT MATTER 4

5 IOT IN INDUSTRIAL ENTERPRISES Strategic Alignment - Ecolab Perspective Market Differentiation through Innovation Value Proposition based on Customer Outcomes Innovation KPI - Vitality Index For the third consecutive year, Ecolab was named to Forbes list of The World s Most Innovative Companies. ranked 33rd out of 100 companies 11/25/ MACC CONFERENCE MAKING IT MATTER 5

6 IOT IN INDUSTRIAL ENTERPRISES Business Opportunities - Ecolab Perspective Upstream Downstream Key Messages Opportunity to extract latent value from an Asset base Network of sensors in Customers Operations environment 11/25/ MACC CONFERENCE MAKING IT MATTER 6

7 IOT IN INDUSTRIAL ENTERPRISES Business Opportunities Differentiate in the Marketplace Maintain price-premium against competitors through Value-added services Demonstrate Contract value to enterprise customers through KPIs Drive Product Innovation Use real-world application data to refine & develop new Products & Applications Develop sustainable solutions that reduce consumption of Scarce resources Monetize New Information Services Higher-value Operations Management service offerings to existing customers Data-driven Insights to Extend service life of Capital assets 11/25/ MACC CONFERENCE MAKING IT MATTER 7

8 IOT CONCEPTUAL ARCHITECTURE Opportunities & Challenges As IoT Economics shift: # of Networked Sensors is exploding Shifting from Capture only What s needed to Capture in case it s needed Use Sensor data for: Operations Monitoring (NOC-like service) Near-real time Predictive Analytics Insights for Product Innovation (RD&E) 11/25/ MACC CONFERENCE MAKING IT MATTER 8

9 IOT IN INDUSTRIAL ENTERPRISES Market Opportunity & Challenge Our ability to make sense of the data The Challenge The Opportunity Explosive growth in Networked devices 11/25/ MACC CONFERENCE MAKING IT MATTER 9

10 IOT+BIG DATA TECHNOLOGIES How Does Big Data Align with IoT Challenges? Volume Millions of Sensors Velocity Data streaming in every minute Variety Diverse Sensor Apps Big Data Platform While in B2C, Big Data platforms were initially used to deal with Search & Social data In Industrial Enterprises, Big Data platforms can be leveraged to add Machine data to the mix 11/25/ MACC CONFERENCE MAKING IT MATTER 10

11 IOT+BIG DATA ARCHITECTURE Technical Challenges What are some of the Technical Challenges in realizing these business outcomes? What architecture approaches can we employ to overcome these challenges? 11/25/ MACC CONFERENCE MAKING IT MATTER 11

12 IOT+BIG DATA ARCHITECTURE Technical Challenges Velocity - Keeping up with High rate of IoT/M2M Data generation Before: After: Solution Approach Captured Only What s Needed from a limited # of networked devices Build a Data processing platform that can ingest larger & more frequent data transmissions from a growing # of networked devices - Capture Everything in case it s Needed Architect a platform for Schema-less processing of M2M data on Write AND Schemabased on Read for Analytics Switch from ETL-based processing to ELT-based processing; Relocate Transformation workload to an MPP platform 11/25/ MACC CONFERENCE MAKING IT MATTER 12

13 IOT+BIG DATA ARCHITECTURE Technical Challenges Variety - Friction between Sensor & Enterprise Data Infrastructure Before: After: Solution Approach Sensor data lived largely on an island (or even several islands) Build a Digital business that integrates the M2M data in Product development, Marketing, Sales & Service operations Switch from Data Pipeline or Data Supply Chain to Zone-based Data Architecture to bring together Sensor data & Enterprise Systems data Leverage MPP platforms to support processing engines that span the boundary between Schema-less M2M & Schema-based Enterprise data 11/25/ MACC CONFERENCE MAKING IT MATTER 13

14 IOT+BIG DATA ARCHITECTURE Conceptual Data Management Architecture Data types Machine and sensor data Enterprise App data Image and video Enterprise content Social data Market data Data Processing Platform MPP SQL+NoSQL ELT Master Data & Governance Landing Zone Data Warehouse Streaming Analytics Data Marts Deep Analytics Actionable Insights Prescriptive Predictive Diagnostics Descriptive Discovery Exploration 11/25/ MACC CONFERENCE MAKING IT MATTER 14

15 IOT+BIG DATA ARCHITECTURE Organizational Challenges What are the Organizational Challenges in realizing the IoT+Big Data Architecture? What approaches can we employ to overcome these barriers? 11/25/ MACC CONFERENCE MAKING IT MATTER 15

16 IOT+BIG DATA ARCHITECTURE Organizational Challenges Veracity - Changes in Data Quality & Governance Before: After: Solution Approach Consumers of data expect high quality data from Enterprise platforms Different Business uses strike a different balance between latency, detail & quality Establish distinct expectations of data quality across zones Establish Data Governance policies by Zone - Landing Zone (Least Governed) Data Marts & Analytics Zone (Locally Governed) EDW (Most Governed) Shift focus from Data Quality measures to Fit for Purpose Shift Data Governance emphasis from Control to Informed consumers 11/25/ MACC CONFERENCE MAKING IT MATTER 16

17 IOT+BIG DATA ARCHITECTURE Organizational Challenges Value - Extracting Value from the Data Before: After: Solution Approach We delivered Validated, Nicely-packaged, Limited Data sets to Users We are asking analysts to mine Dirty Unstructured Data sets for hidden gems AND turn those into product/service offerings Develop Organizational capabilities to go from Discover Insights Turn Insights into Products/Services Operationalize Insights Model after traditional Product Development processes that go from R&D Product Development Manufacturing Engineering Product Delivery 11/25/ MACC CONFERENCE MAKING IT MATTER 17

18 IOT+BIG DATA ARCHITECTURE Organizational Challenges Data Security - Protecting Customer Data Before: After: Solution Approach No Silver Bullets Sensor data stayed on disconnected islands limiting the Attack surface Companies are aggregating, storing & moving vast amounts of operationally sensitive customer data potentially creating a far bigger Attack surface Combine & Adapt Security measures developed & deployed in Industrial & B2C markets 11/25/ MACC CONFERENCE MAKING IT MATTER 18

19 Thank You Aditya Thadani Phil Andreoli /25/ MACC CONFERENCE MAKING IT MATTER 19