realizing business value from convergence of iot + big...

Realizing Business Value from Convergence of IoT + Big Data Technologies

Aditya Thadani | Phil Andreoli

CONVERGENCE OF IOT+BIG DATA TECHNOLOGIES Agenda

11/25/2014 2014 MACC CONFERENCE | MAKING IT MATTER 2

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?


• 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

BUSINESS CONTEXT About Ecolab

• Where does IoT/M2M fit in business strategy for Industrial Enterprises?

• What business outcomes are we looking for?


IOT IN INDUSTRIAL ENTERPRISES Business Opportunities

2009 2010 2011 2012 2013

For the third consecutive year, Ecolab was named to Forbes’ list of “The World’s Most Innovative Companies.” … ranked 33rd out of 100 companies …

Market Differentiation through Innovation

Value Proposition based on Customer Outcomes

Innovation KPI - Vitality Index


IOT IN INDUSTRIAL ENTERPRISES Strategic Alignment - Ecolab Perspective

Opportunity to extract latent value from an Asset base – Network of sensors in Customers’ Operations environment

Key Messages

Upstream

Downstream


IOT IN INDUSTRIAL ENTERPRISES Business Opportunities - Ecolab Perspective


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

IOT IN INDUSTRIAL ENTERPRISES Business Opportunities


As IoT Economics shift:

• # of Networked Sensors is exploding

• Shifting from “Capture only What’s

needed” to “Capture in case it’s needed”

IOT CONCEPTUAL ARCHITECTURE Opportunities & Challenges

Use Sensor data for:

• Operations Monitoring (NOC-like service)

• Near-real time Predictive Analytics

• Insights for Product Innovation (RD&E)


IOT IN INDUSTRIAL ENTERPRISES Market Opportunity & Challenge

The Opportunity

The Challenge

Explosive growth in Networked devices

Our ability to make sense of the data


Big Data Platform

Volume

Millions of Sensors

Velocity

Data streaming in every minute

Variety

Diverse Sensor Apps

IOT+BIG DATA TECHNOLOGIES How Does Big Data Align with IoT Challenges?

• 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

• What are some of the Technical Challenges in realizing these business outcomes?

• What architecture approaches can we employ to overcome these challenges?


IOT+BIG DATA ARCHITECTURE Technical Challenges


Velocity - Keeping up with High rate of IoT/M2M Data generation

Before: Captured “Only What’s Needed” from a limited # of networked devices

After: 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”

Solution Approach

Architect a platform for Schema-less processing of M2M data on Write AND Schema-

based on Read for Analytics

Switch from ETL-based processing to ELT-based processing; Relocate Transformation

workload to an MPP platform



Variety - Friction between Sensor & Enterprise Data Infrastructure

Before: Sensor data lived largely on an island (or even several islands)

After: Build a Digital business that integrates the M2M data in Product

development, Marketing, Sales & Service operations

Solution Approach

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



Data types

Enterprise App data

Machine and sensor data

Enterprise content

Social data

Image and video

Market data

Actionable Insights

Prescriptive

Predictive

Diagnostics Descriptive

Discovery Exploration

Master Data & Governance

Streaming Analytics

Deep Analytics

Data Marts

Data Warehouse

Landing Zone

Data Processing Platform MPP | SQL+NoSQL | ELT

IOT+BIG DATA ARCHITECTURE Conceptual Data Management Architecture

• What are the Organizational Challenges in realizing the IoT+Big Data Architecture?

• What approaches can we employ to overcome these barriers?


IOT+BIG DATA ARCHITECTURE Organizational Challenges


Veracity - Changes in Data Quality & Governance

Before: Consumers of data expect high quality data from Enterprise platforms

After: Different Business uses strike a different balance between latency, detail

& quality

Solution Approach

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




Value - Extracting Value from the Data

Before: We delivered Validated, Nicely-packaged, Limited Data sets to Users

After: We are asking analysts to mine “Dirty” Unstructured Data sets for

hidden gems AND turn those into product/service offerings

Solution Approach

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



Data Security - Protecting Customer Data

Before: Sensor data stayed on disconnected islands limiting the Attack surface

After: Companies are aggregating, storing & moving vast amounts of

operationally sensitive customer data potentially creating a far bigger

Attack surface

Solution Approach

No Silver Bullets

Combine & Adapt Security measures developed & deployed in Industrial & B2C

markets


Thank You

Aditya Thadani www.linkedin.com/in/adityathadani

Phil Andreoli www.linkedin.com/in/philnandreoli

http://www.linkedin.com/in/adityathadani

http://www.linkedin.com/in/philnandreoli

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