big data, business users and opportunities
TRANSCRIPT
![Page 1: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/1.jpg)
© 2014 IBM Corporation
Craig StatchukArchitecture and Strategy, IBM Business Analytics Office of the CTO
November 2014
GeoSpatial Analytics for Business
Rev B
![Page 2: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/2.jpg)
2About Me
Cognos / IBM technical strategy
Geospatial business evangelist
Big data, cloud, search, modeling
Craig [email protected]@statchuk
![Page 3: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/3.jpg)
3Agenda
Enterprise Analytics
The Data Driven Business
Top 5 Business Priorities for GIS
![Page 4: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/4.jpg)
4Quality, Relevance and Flexibility
Data Analytics
Results
![Page 5: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/5.jpg)
5Quality, Relevance and Flexibility
Data Analytics
Results
Relevance
Quality Flexibility
![Page 6: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/6.jpg)
6Mobile Imperatives
Business Analytics Content
UserRole &
Context
Irresistible Mobile Value
![Page 7: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/7.jpg)
7Mobile Imperatives
Business Analytics Content
UserRole &
Context
Irresistible Mobile Value
3 Clicks
10 Seconds
PredictiveWorkflow
![Page 8: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/8.jpg)
8
80% Enterprise Data is Unstructured
Finding Greater Value
![Page 9: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/9.jpg)
9Finding Greater Value
99%95% Across Business Silos
From Government
Structured Data
![Page 10: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/10.jpg)
10First Normal Form (1NF)
1NF Data in columns
Unique keys
Customer Store Product Amount
Beth NYC Sunglasses $89
Ginny Atlanta Tent $323
Beth Toronto Shoes $123
![Page 11: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/11.jpg)
11Third Normal Form (3NF)
Customer Store Product Amount
456 S434 10023 $89
123 S331 40032 $323
456 S416 30014 $123
1NF Data in columns
Unique keys
3NF Dependent keys
No extra data
Cust# Name Phone …
123 Ginny 516-443-5645
456 Beth 816-433-2232
Store# Location Phone …
S331 Atlanta 516-432-3231
S416 Toronto 888-416-2535
S434 NYC 888-231-2222
Prod# Name Cost …
10023 Sunglasses $65
30014 Shoes $55
40032 Tent $223
![Page 12: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/12.jpg)
12New Normal Form (NNF)
Name Phone Customer Location Store Name Product Amount
Ginny 516-443-5645 456 Atlanta S434 Sunglasses 10023 $89
Beth 816-433-2232 123 Toronto S331 Shoes 40032 $323
Ginny 516-443-5645 456 NYC S416 Tent 30014 $123
NNF Lots of rows, columns values and extra data
Lots of duplication (x & y)
![Page 13: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/13.jpg)
13Why NNF matters
• Second guess past assumptions
• More self-serve data preparation
• Data quality is built-in
![Page 14: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/14.jpg)
14Leverage Better Data
Latitude: 45.467836 Longitude: -75.708618
Geospatial attributes expensive to leverage
Small changes = big variations
almost impossible
![Page 15: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/15.jpg)
15Leverage Better Data
Latitude: 45.467836 Longitude: -75.708618
Geospatial attributes expensive to leverage
Small changes = big variations
almost impossible
Context: home, work, commuting
Clients: Hilton, Walmart, Boeing
Time periods: fiscal year, next release
![Page 16: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/16.jpg)
16
Improving
Hospitals
![Page 17: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/17.jpg)
17Not as Smart as we Thought
Ability to process
AvailableData
The gap is what we don’t know
Time
Volu
me
![Page 18: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/18.jpg)
18
Enterprise Quality Data
Uncertain Data
Not as much Quality as we Need
Time
Volu
me
![Page 19: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/19.jpg)
19Get it Right Early
Correct Assertion
Incorrect Assertion
Time
Pro
cess
ing
![Page 20: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/20.jpg)
Watson Plays Jeopardy
![Page 21: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/21.jpg)
Watson: “What is Toronto?”
Category: US CitiesAnswer “Its largest airport was named for a World War II hero; its second largest, for a World War II battle.”
NLP/POS: City where largest airport was named for a World War II hero; City where second largest airport is named for a World War II battle
Strategy: Low Weight on Category since it could be play on words or pun.
Ontology: University of Toronto is member of American Association of Universities; Toronto Blue Jays in the American Baseball League
![Page 22: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/22.jpg)
22
Context: Sales rep driving from SeaTac airport
Metadata Drivers
Launched by Calendar, Email, SMS or Geo-fence Event
Ends in analytics (Customers, History, Issues)or related app (Contacts, maps, email)
![Page 23: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/23.jpg)
23Context driven entry points (Customers)
ContactsMaps, Driving Directions…
Boeing
Sales RepsChat, Connections…
ProductsHistory, licenses…
CompetitiveProducts, Web…
Prospectsdemos, issues…
In the NewsStories, blogs…
Customer
![Page 24: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/24.jpg)
24Metadata Drivers Select Data and Application
CategoriesRevenue, Plans, ChannelsProducts
SupportComments, APARS…
CompetitiveFeatures, Field feedback
Field Resourcesdemos, guides
In the NewsCustomers, Reviews
Boeing
![Page 25: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/25.jpg)
25
Days to close
FY 2014
Num
ber o
f C
alls
Q1 Q2
22 days
Severity
L H
100
200
Q3 Forecast
6 days
20 days
8 days
App, data and formatare different for every customer
Mobile context requires higher flexibility and precision
Support Calls
28 days
![Page 26: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/26.jpg)
26Business Top 55 More CONNECTIONS (existing data)
4 Exploration and DISCOVERY (new data)
3 High value GEOSPATIAL data (leverage)
2 Getting it right EARLY (close the gap)
1 KNOW what I want BEFORE I ask (everywhere)
![Page 27: Big Data, Business users and opportunities](https://reader035.vdocuments.net/reader035/viewer/2022070514/5880dbbc1a28ab9c3a8b71a5/html5/thumbnails/27.jpg)
27
Thank You!