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Vincenzo SciaccaIBM Insight Cloud Services Evangelist

The Insights Economy

Data is transforming industries and professions.

The world is being reinvented

in code.

In the Cognitive Era digital intelligence meets

digital business.

Leveraging the new mix of data

Developers, platforms & infrastructures

Solutions & Smarter Industries

Analytics strategic imperatives & their investment axes

Accelerate outcomes & create deeper business

relevance

Analytics for everyone on an open, fluid & unified

architecture

Agile integration & governance of internal,

external & machine-based data

The Insights Economy

Power digital-physical

innovation

Increase operational efficiency

Manage risk

Transform customer engagement

• 100% prediction of aircraft-on-the-ground events for high-risk engines

• 97% accuracy in predicting engine events that lead to airline disruption

• 270% increase in cross-sales of accessory products

• 50% increase in effectiveness of retention campaigns

• Reduces energy costs by up to 20%• Saves up to $25M per year keeping

refrigerators at optimal temperatures

• Remote diagnosis of refrigerators to streamline labor efforts

• 40% increase in identifying suspicious transactions

• 80% increase in productivity• 200% increase in reporting capabilities

Data, Insights, and Action

Data Insight Action Customer name, address Customer segment Marketing campaigns

+ Customer service requests Propensity to churn, customer value

Retention action decision

+ Customer transactions Customer value Next best action

+ Twitter Life Event detection Targeted ads and offers

Utility operations + Weather history + Weather forecast

Energy consumption forecasts Energy production plan

+ Outage history + asset data Outage prediction Crew and resource dispatch

Weather forecast + HC data Asthma risk score Patient alerts, ER staffing

POS data Category Sales performance Pricing actions

+ Weather history Weather adjusted sales performance

Inventory repositioning

Weather history + Ag yield Variability and Correlation Price Crop Insurance

Equipment sensor data + weather

Failure prediction Maintenance schedule

Enterprise HR Data Attrition Risk Compensation optimization

Tax payment details Single View of the tax Payer Improve loss prevention

Twitter Where will demonstration happen

Law enforcement crew dispatch

Agricolture Business context

Resources

Users

Supply chain

Supporting functions

FarmersAgriculture

CompaniesCooperatives Government R&aD

InputsSeeds, Fertilizers

Primary ProductionCrops, Farmland Management

Plan Prepare Seed Care Harvest Stock

Natural ResourcesWater, Energy

EnvironmentWeather, Soil

Equipments

Finance Logistics

Labor

The Process to Deliver Actionable Insights

Step 1: Raw Ingredients…Data Acquisition

Harvesting of that raw data separates the usable ingredients from the unusable, cleansing ensures the data has consistent quality and is

contaminant free, and distribution makes the ingredients available in a convenient location.

The discovery of a new insight takes experimentation with different data ingredients and the particular mix in which they are used. Once discovered, this recipe is repeatable and provides value for many applications.

Insights can now be combined into interesting combinations and applied to specific industry scenarios, improving and optimizing the experience and

making them actionable. There is a wide range of value and application to these insight combinations.

Raw data forms the base ingredients for all insights and actionable processing. The knowledge of what data is available and where to find it is the first level of value.

Step 2: Harvested & Cleansed Ingredients…Data Cleansing & Curation

Step 3: Ingredients Combined into a Recipe…Insights Processing

Step 4: Recipes Combined to make Meals…Service Delivery

Step 1: Finding the right Raw Ingredients

1. Start with Global Contextual Data through partnerships, purchase, and open data sources:

Weather Geospatial Boundaries & Points of Interest Location Aggregation (people & vehicle traffic) Social Media Network & Content Demographics City, State, Federal Government Open Data Business Registries Event Calendars

2. Add Specific Commercial & Industry Data

Infrastructure Networks (Utility, Transportation)

Insurance & Risk Financial Transactions

Customer Relationship Management Asset Management Systems

Data Acquisition Strategy

Step 2: Harvest & Cleanse the Ingredients

1. Catalog and classify data for appropriate usage, understanding and governance2. Purchase already processed data from selected data providers and partners3. Statistical profiling and identification of historical patterns and data entry errors4. Cleanse: correction, elimination of bad data values, sampling for confidence

levels5. Normalize: mapping tables and value translation6. Manual tool improvements to improve cleansing productivity7. Selectivity of data values to cleanse, focusing on “just enough integration and

cleansing”

Data Cleansing & Curation Strategy

Step 3: Combine the Ingredients into a Recipe

Insights Processing Strategy

1. Create incubation areas that blend the harvested and cleansed data combined with a specific set of customer data.

2. Enable a team of Data Scientists to create analyses and models that can improve specific line-of-business processes.

3. Create datasets and analytics results that can be used across multiple industries and applications (weather forecasts can impact retail, government, consumer products, insurance, life sciences, travel and transportation, and energy and utilities).

4. Create measurement systems that show the effectiveness of the models in the business process, for continuous improvement.

5. Construct processes to validate and improve insights.

“In the end you should only measure and look at the numbers that drive action, meaning the data tells you what you should do next.”

Alex Peiniger CEO, quintly

Step 4: Combine Recipes into a Meal

Solutions Delivery Strategy

1. Weave the developed analytics model into an insight that can be run at scale, with easy to understand and integrate results.

2. Provide those results through standard API libraries and in some cases, User Interfaces on web or mobile clients.

3. Create a unique customer experience area that enables self-education, self-evident value, and immediate self-gratification.

“Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes.”

Harvard Business Review

Network of 190,000+Weather sensors

Global LightningDetection Network

Proprietary Radar Algorithms

Industry BestForecast Modeling

State-of-the-Science Forecast Technologies

PhD

220+ Full-time Meteorologists

15-30 day hurricane forecast

Largest collection of worldwide forecasts

Proprietary weather data analytics

A big data and IoT approach to Weather:The Weather Company delivers 26 billion forecasts per day

4GB of new data each second

Atmospheric data from 50,000 flights

per day

2.2 Billion weather forecast locations Data from 40 Million

mobile devices

The Weather Company generates differentiated insights that can be processed and distributed on a massive scale

Ope

n &

Gov

t D

ata

National Weather Service Weather Stations

High Resolution Radar

Oceanographic Data

The

Wea

ther

Com

pany

Pro

prie

tary

and

S

ourc

ed D

ata

The Weather Company Weather Models

127K Global Stations

40M+ Mobile Phones

50K Flights a Day

Air Quality and Pollen

Global Lightening

Traffic / Incident Data

Historical

Current

Predictive

Global

Ultra-local

Weather

Atmosphere

Sources Types

National Weather Service

The Weather Company

Condition Updates

1 per hour

Forecast Updates

Every 6 hours

Data and Analytic Processing

60 per second

Every 15 minutes

40M+ Mobile Phones Handles 26 billion requests a day3 Billion forecast reference points

Generates 4GB of new data each second

Data Ingest and Distribution

Italy coverage : Personal Weather Station Network• Over 4500 Personal Weather Stations in Italy today• 5 Minute Reporting Frequency• Feeds into our 500 m^2 resolution Forecast on Demand Engine, especially for next 6 hours• Historical Data back to 2005 - More sensors online as time progresses

Historic Data Points from Public SourcesPublic Sources vs. Weather channel sensors

Weather.com sensors for collecting weather information

Public sources sensors for collecting weather information

Public sources sensors for collecting weather information

METAR Sensors + NWS NWS + WSI + PWS Sensors

Integrated Weather and Crop Modeling

Irrigation scheduling. Minimizes water wastage. (2-10 day) forecasts.

Farm Operations. Proactive management of disease /pests. Reduce wastage(2-10 day) forecasts.

Continuously run weather forecast and crop /disease model

Generate advisorieson likelihood of rain,Highlight conditionsfor disease outbreak etc.Advisories on fertilizer and pesticide application.

Farmer plans activities such as fertilizer application,pesticide application,harvesting,sun drying grain.

Continuously run weather, hydrologymodels and sensorsto determine water requirement at paddy fields.

Use forecasts to supply water or drain excess.

Farmers edge partners with the weather company

IBM Weather Company and Farmers Edge: integration of hyper-local forecasts from Weather’s Forecasts on Demand weather forecasting engine into its field-centric approach to predictive modeling

IBM Weather Company Farmers Edge will provide real-time data from the field, highly precise, predictive models to make decision on: critical crop stages, the timing of field operations, pest and disease pressure, equipment deployment, soil needs, and nutrient requirements