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Social Intelligence: Social Media Listing and Response center. 1 Connecting People Sharing Knowledge Social Software Delivering business value across the organization Text mining - Analytics Insights Knowledge Networks Social Networks Defined Processes Self-service Continuous Action Dynamic Measure Retain

Social networking text mining - analytics in km 13.dec.2011

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Discussion points for the use of social software, social networking, text mining, and analytics to enable knowledge management processes.

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Page 1: Social networking   text mining - analytics in km 13.dec.2011

Social Intelligence: Social Media Listing and Response center.

1

Connecting People Sharing Knowledge

Social SoftwareDelivering business value across the organization

Text mining - Analytics

Insights

Knowledge NetworksSocial Networks

Defined Processes

Self-service

Continuous

Action

Dynamic

MeasureRetain

Page 2: Social networking   text mining - analytics in km 13.dec.2011

Table of Contents

2

1 Cover

2 Table of contents

3 - 5 Social Networking

6,7 Linking to KM processes

8 The problem with email and share drive

9 Silos of technology

10 -15 Applying social software and networking to KM communities

16-24 KM example using a global supply chain group

25-28 Example of a browser add-on for crowd sourcing

29 High level capabilities comparison social software vs. group ware

30 Technical view of the ecosystem

31 Thank you

Page 3: Social networking   text mining - analytics in km 13.dec.2011

People love being social

People are socializing across channels with theirphones, TVs, browsers, tablets, cameras, watches, toys

Mobile Interactive Social Media

@ work@ home@ playAnytime

Anywhere

Page 4: Social networking   text mining - analytics in km 13.dec.2011

• Surpasses 750 million users• 900 million objects that

attract people (pages, groups, events, and community pages)

• 30 billion pieces of content• Average users has 80

connections

• 175 million users• 90 million with no

followers

• 10.7 million - GroupOn

• 8.8 million – LivingSocial

• 10,000 - FourSquare• 100 million –

LinkedIn

• 25 million user• Google’s network !

• Bandwidth = entire internet in 2000

• 3 Billion video views per day• 200,000 uploads per day• 300 million accounts• 6% educational content• 7 % from Europe

Page 5: Social networking   text mining - analytics in km 13.dec.2011

THE REST OF THE WEB 2.0+ IS EVEN BIGGER !

Blogs, forums, specialty sites, specialized search engines

Interactive shopping sites, pop-up ads, and more

THE REST OF THE WEB 2.0+ IS EVEN BIGGER !

Internal & External

Blogs, forums, specialty sites, specialized search engines, interactive sites, pop-up ads, and more!

Page 6: Social networking   text mining - analytics in km 13.dec.2011

Social Software is changing the dynamics

6

Publish

Learn & Gather

Categorize & Prioritize

Validate source

Test & Learn

Analytics

Relationships

Individual & ProfessionalInterests

DocumentsProcesses

Apply

Page 7: Social networking   text mining - analytics in km 13.dec.2011

StrategyPlan & Budget Learn & Gather Categorize &

PrioritizeValidateSources Test & Learn Publish Apply Improve

Social software enabled collaboration, text mining, analytics, and crowd sourcingCrowd survey• Assessment• Strategy• Plan• Budget

OrganizationalStakeholders• Executive• Operations• Finance• Manufacturing• Marketing• HR• Channels• Communication

External Social Media• Supplier• Partner• Forums• Blogs• Communities• Industry Forms

Internal Networks• Voice of Customer• SME Forms

• Blogs• Competitions• Polling

• Research

Market Scan• Industry trends• Competitor Analysis

Crowd priority• Strategic objectives• Lifecycle analysis

Member Groups• Organizational depts.• Products and Services• Global LOBs• Channels Management

External• Supplier• Partner

Market Scan• SME competitions• Competitor Analysis• Emerging trends

Internal NetworksProducts and ServicesGlobal LOBsChannels Management

Member groups• Products and Services• Operations• Global LOBs• Channels Management

Internal & External• Forums• Blogs• Communities

Market Scan• Competitions• Competitor Analysis

Go/No Go toll gates used to inform the team at each stage.Uniform metrics enable the team to measure feasibility and performance for each ideaAlign with in-flight initiatives as well as introducing future-state innovations

Design for today’s digitally delivery “rapid” cycles: Creating repeatable processes to gather, process, analyze, deliver KM content

7

The example below uses a typical product innovation lifecycle

Page 8: Social networking   text mining - analytics in km 13.dec.2011

How we work today: The Problem with email and share drives1 Problem + 10 people = 200 emails & 50 calls

Meeting times set

Team is informed

Communication viaEMAIL AND PHONE

Problem / Issue

Individuals respond to

group emails

Correspondence until problem is

resolved

People are overwhelmed by disconnected group emails and callsresulting in lost ideas, ineffective use of time, and hidden costs

Page 9: Social networking   text mining - analytics in km 13.dec.2011

How organization see social network: Silos of activities and resultsLimited or no alignment across social software initiatives

9

Corporate CommunicationSocial media listening and analyticsCommunity Strategy

Marketing• Voice of the customer• Marketing Analytics

Customer Services• Voice to Text• Text mining chat• Call center management

Business Intelligence• ERP reporting• CRM reporting• Sales reporting

Islands of solutions

Islands of Analytics

Little / no integrationwith social

content

Soft-Knowledge

Lost

Knowledge Management• Blogs, forums• Share drives• Communities• Email

Duplicate costs and efforts

Page 10: Social networking   text mining - analytics in km 13.dec.2011

How Social Network shares enterprise connections based on interests1 Problem + 10 people = 0 emails and 5 calls

10

Focus area

Ideas are shared

dynamically

Team is informed

automatically

Social Networking basedcommunications

Individuals view and

respond in real-time

Crowd sourcing the communication cycle

Review & Approval

Team collaboration

Text mininganalytics

Problem / Resolution

Brainstorming, ideation, and knowledge sharingSecure social networking community connects people

No email, single source of reference, saving time and costs

Page 11: Social networking   text mining - analytics in km 13.dec.2011

Shared SolutionsKnowledge management

Communities of practices as a social network act as multi-functional global teams to Learn, Prioritize, Validate, Publish, and Apply

Governed• Processes• Sources• Services

Enable• Ideation• Innovation• Retained IP

11

C-LevelExecutives

SalesLOB

Executives

Marketing Research

LogisticsAccount Relations

Legal Operations

ManufacturingSuppliers

Vendors

Text mining & Analytics Gather, Process, Analyze

Secure social networking communities Search, publish, share, apply

Page 12: Social networking   text mining - analytics in km 13.dec.2011

Transformation to the social enterprise

Personalized

Actionable

WorkspaceIdeation

Social Networking Surfacing

The Social Enterprise

Unstructured

Page 13: Social networking   text mining - analytics in km 13.dec.2011

Social Network

13

Global Manager

Topic Owner SME / Evaluator

Community members

Community 1

Community 2

Topic 2

Topic 3

Topic 4

Request for topic researchMonitor progress

• Crowd sourcing with “nano-survey”• Analyze trends • Surfaces issues• Monitors • Comparative information

Topic Owner • Manage group• Define survey• Monitors topics

• Relevancy• Accuracy

Evaluates the feedback• Tracks the activity using

embedded analytics• Provides direction and

guidance to participants

Social networking with text mining and analyticsConnecting people to quickly discover solutions

Requests and coordination

Dashboards

Page 14: Social networking   text mining - analytics in km 13.dec.2011

Types of integration used on Social Networking

14

Social Network

• “The glue”• Analysts 360

o Communityo Contento Collaborationo Publicationo Analytics

• Cloud-based• SaaS

Crowd Sourcing content

Social Media Listening and Analytics

Enterprise Text mining and analytics

Combining of social networking, text mining and analytic

Categorize & PrioritizeText Mining & Analytics

Publish / ApplyImprovement

Learn & Gather Unstructured Content

Validate / TestAutomated – Manual

Internal

External

Listening

Posts

Page 15: Social networking   text mining - analytics in km 13.dec.2011

Businessvalue

Tapping social networking content with text mining and analyticsProduces results quickly and with far less effort

ActionableIntelligence

Internal• Knowledge Officers

• Executives

• Sales & Marketing

• Product Management

• Corporate Communications

• Operations

• Customer Services

• Human Resources

External• Communities

• Sales and Marketing

• Recruiting

• Social Media

• Social Software

• Advertizing Syndication

Data WarehouseETL – Data Quality

Unstructured Semi-structured Structured

Services• Text Mining

• Taxonomy

• Ontology

• Analytics

• Enriched metadata

EnrichedSocial

Content

Analyze & DeliverGather & Process

Internal Content• Communities of Practice

• Emails

• Surveys

• Chat

• Contact / Call Center

• Online Feedback

• Smart Widgets

• Customer data

• Micro-site

• Partner sites

External Content• Social Networks

• Blog

• Forums

• News

• Facebook

• Twiter

• YouTube

Increase Traffic from Social Media

IncreaseUp-sell

Cross-Sell

ImproveCompetitive Intelligence

Improve marketing analysis

EnableKnowledge

Sharing

Page 16: Social networking   text mining - analytics in km 13.dec.2011

FAST FUN FRIENDLY a global ideation and innovation solution

Member ‘s Self-service

Reputation Builder

INSIGHTs INTO ACTIONsCrowd sourcing the communication cycle

Feedback

Scoring & Analytics

Easy accessChartsGraphsCommunity Navigator Text mining

analytics

Author

Review Feedback

Global Communities DashboardFAST FUN FRIENDLY

Secure role-based access to people and content

Page 17: Social networking   text mining - analytics in km 13.dec.2011

Examples of social networking applications

17

Connect the global community to identify non-standard shipping rates

Reduce the cost basis for non-standard shipping rates

Page 18: Social networking   text mining - analytics in km 13.dec.2011

Social networks link people to topics, posts, and conversations

18

1 2 & 3 4

1 Role and background

2 Personalized tag cloud of all the user’s content

3 Drill-anywhere to view posts and collaboration

4 View number of contributions Click to view all active posts and collaboration

One view = many connections

Page 19: Social networking   text mining - analytics in km 13.dec.2011

Cross-functional story flow: Non-Standard Rates Internal/External Collaboration with Secure - Social Network - Embedded Analytics

Focus Subject

Global Executive

surfaces issue related to excessive shipping charges

India Logistics Analyst

Brazil Logistics Executive

Brazil LogisticsDispatcher

Reviewer

Brazil Logistics Manager

Author

Global Ideation, Innovation to find solutions

Key Findings Action Items Results

Page 20: Social networking   text mining - analytics in km 13.dec.2011

20

Scenario 1 Storyboard: Non-standard rates slide 1

1

2

3

No. Description

1 Access to all communities, workgroups, forums

2 Search any content

3 View the community message board

Page 21: Social networking   text mining - analytics in km 13.dec.2011

21

Scenario 1 Storyboard: Non-standard rates slide 2

1

2

3

No. Description

1 The users set context by selecting a community, workgroup, or project

2 All content is displayed based on the context including content and members

3 Global participants profiles display with location, and background, number of posts

4 Click on the members picture and a “Smart Theme” cloud appears with key words related the member’s posts. • Size and position show relevance• Click any word to drill to the topic

4

Page 22: Social networking   text mining - analytics in km 13.dec.2011

Scenario 1 Storyboard: Non-standard rates slide 3

22

1

2

34

No. Description

1 View a summary of posts, feedback, with the time of posting and member who posted

2 “Smart Theme” cloud links all the activity and posting with key words based on relevance

3 Click on the them cloud words to see all post related to the key word

4 Drill down to the post to view all “conversations”, the comments, feedback, and evaluation from all members who are participating.

Page 23: Social networking   text mining - analytics in km 13.dec.2011

Dashboard view of topics, survey, and activities

23

1

2

3

4

No. Description

1 Listing of topics within a selected category

2 Participants in the topic forums

3 Spotlight chart showing activity & survey results

4 Daily feedback and activity chart

Page 24: Social networking   text mining - analytics in km 13.dec.2011

Cross-functional story flow: Non-Standard Rates Internal/External Collaboration with Secure - Social Network - Embedded Analytics

Focus Subject

Key Findings Action Items

Global Executive surfaces issue

related to excessive

shipping charges

Rates are non-standard and

justified

No action required

India Logistics Analyst

Contact vendor and renegotiate

rates

Rates are non-standard and

unjustified

Brazil Logistics Executive

Brazil LogisticsDispatcher

Results

Reviewers

Brazil Logistics Manager

No action requiredRates are standard

Reduce shipping costs by 10% utilizing a global audit

Author

Page 25: Social networking   text mining - analytics in km 13.dec.2011

25

Employees are using the internet every day.

ID trends to changing markets, news, and key topics on a near-real-time basis

Tap employees expertise via browser-based crowd sourcing

Enable contributors throughout your organization

Capture the source, authors, sentiment Contributor’s insights and comments Sentiment and special tags Start a conversation and gather ideas about the post

Page 26: Social networking   text mining - analytics in km 13.dec.2011

Interactive personalize dashboards

Top contributors

“Reputation Builder”

Access to all your communities

Data range search

Stats

Quick Search

Must use FAST – FUN – FRIENDLY applications

Page 27: Social networking   text mining - analytics in km 13.dec.2011

Size and position of the word show relevance

One-click to list all the content connect to the word

Text mining enable “SMART CLOUDS”

Data search

Content dictionary based Themes

Contributors’ Tags

Page 28: Social networking   text mining - analytics in km 13.dec.2011

One click on a theme or tag cloud Displays all the relevant content Contributor Date Links to conversations

Click the bar to filter based on sentiment

Drill anywhere to read posts, identify sentiment, and share ideas

Page 29: Social networking   text mining - analytics in km 13.dec.2011

Simple comparison of social software to group ware

Components and capabilities Social Software

Group Ware & Shared Drives

1 Business user configuration without programming P E Limited

2 Business user defined social networking community, workgroup, and projects with role based security to data and functions P E Customization

3 Flexible architecture from low cost public cloud-based to private cloud or enterprise P E Limited

4 Embedded text mining and analyticsP E Limited

5 Embedded metadata and categorization P E Limited

6 Multi-tenant software-as-a-service options for B2B or B2C deployment P E Customization

7 Open architecture for content integration with 3rd party providers P E Limited

8 Available to install on multiple operating systems P E Limited

9 Open standards P E Limited

10 Ready for rapid deployment from day one of the project P E Limited

11 Role-based security and privileges to data and functions P P Configuration

12 Ability to process internal and external content P P Configuration

Page 30: Social networking   text mining - analytics in km 13.dec.2011

Create

Gather Sources

Retrieve

Transform

Consolidate

Refresh

Web Metrics

Crowd

AggregateEnrichedSources

Crowd

Enterprise

Social Media

Web Metrics

Marketing

Distribute

Enriched Unstructured Content

Publish

• Community• Workgroup• Project• External• Syndicate

Process

Text miningAnalytics

Deliver

• Knowledge baseo Researcho Product & services offeringo Community connectionso Much more

DW IntegrationEnrich Content

Analyze

Marketing

ActionableInsightsEventsMetadata

TrendsPatternsExceptions

Ecosystem for integration of knowledge sources

Enterprise

Social

Page 31: Social networking   text mining - analytics in km 13.dec.2011

31

Ken [email protected]+1 469 258 8522

Practice Director – Social IntelligenceBI 2.0 PracticeHCL Technologies

Thank you