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Business and Information An Information Management Journey

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Title: Business and Information An Information Management Journey


1
Business and InformationAn Information
Management Journey
  • Steve Bennett
  • Business Intelligence Group
  • Co-founder The Business Intelligence CORTEX
  • Greg Nichelsen
  • Head of Business Intelligence and Analytics, ING
    Direct Australia

2
About Steve
  • Business Intelligence Group
  • CORTEX - an online Australian community of BI,
    PM, DWH, ETL and Analytic professionalsForums
    Research Jobs Local BI
    News Eventswww.tbig.com.au/forums
  • Former Head of BI Analytics at ING Direct
  • 20 years building analytic capabilities
  • Oz Analytics Blog analytics.typepad.com

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3
About Greg
  • Head of BI Analytics at ING Direct
  • Data Warehouse Architect
  • Project Management in Financial Services and
    Travel
  • 20 years IT application and warehouse development

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4
Topics
  • Business and Information Strategy
  • Transforming Decision Making
  • 4 examples
  • What It Takes

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5
ING DirectThe Free Banking Strategy
Strategy
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6
Strategy
7
Business Strategy
  • Banking Plan B for ING Group
  • Online (No Branches)
  • No Fees
  • Straightforward Products
  • Exceptional Customer Service and Value

Strategy
8
IT Strategy
  • Future Proof - capacity, scalability,
    flexibility, security, reliability
  • Standards - follow industry standards
  • Enterprise - replace 'silo' view with layered
    enterprise view
  • Layered approach - basis for achieving
    scalability and flexibility
  • Re-Use - service re-use across Business units
  • Security - enhanced security and system
    management
  • De-duplication - rationalise application suites
    to remove functional duplication
  • Consistency - consistent user experience
  • Leverage - investment across projects

Strategy
9
Information Vision
  • To create a successful organisation using
    analytics for competitive advantage
  • By using technology to deliver business insight,
    enabling better decision making

Strategy
10
Acquiring an Analytic Capability
Strategy
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11
The Information Journey
Strategy
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Enterprise BI Strategy
  • Enterprise solution to create the trusted SSOT
  • Enterprise dashboard down to individual
    transactions
  • Single analytic community (business driven)
  • Single governance structure (business driven)
  • Linked to performance management (salary reviews)

Strategy
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13
Question
  • What is the most important thing linking
    Business, IT and IM Strategies?
  • People
  • Process
  • Data

Transformation
14
TransformationInformation For Competitive
Advantage
Strategy
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15
Solution DPM Value Chain
DPM
Fact-based Decision Making
  • Data Ownership

PerformanceManagementFramework(value drivers)
Predictive Capability
Data Quality
Strategy
Trusted SSOT
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16
Steps To Take ...
  • Look at Information Holistically
  • Classic data quality criteria like accuracy,
    relevance, availability, etc.
  • What decisions are based on the data
  • Impact to the business like lost, reputation,
    etc.
  • Action they initiate like intervening at source,
    etc.
  • DPM value chain links investment to performance

Strategy
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17
Results After 2 Years ...
  • Technology Platform Changed - but the business
    did it
  • Business Ownership and Data Governance
    Established
  • Data Quality much improved
  • IT cares about IM
  • CIO became the COO
  • New Analytic Capabilities (prediction, workflow,
    community)
  • Better decisions resulting in measurably
    improved performance through analysis and
    modelling

Strategy
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18
How To Do ItLeveraging Your IT, Organisation and
People
Transformation
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19
Design
Business Requirements What are the burning
questions? What are the value drivers?
Data Analysis What does the data support?
Logical Model How do we think it should look?
Business Need What does the business want?
  • Listen to the business
  • Talk to the business
  • Get buy in - partnership
  • Agree definitions
  • Understand the business processes
  • Look at the source data
  • Look at the data
  • Look at history and recent data
  • Work on data quality
  • Validate business processes
  • Argue for the Big Picture
  • Understand the big picture
  • Avoid silos
  • Conform dimensions
  • Challenge the business need

Transformation
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20
Data Quality Council
Decide
  • DQ champions IT Architecture and BIA.
  • 13 domain experts from across the Bank
  • Data stewards of every data item and measure in
    the new platform
  • Empowered to make decisions
  • definitions
  • solutions (business processes and IT)
  • Monthly meetings based on DQ issues register

Transformation
21
Question
  • What advantages could the DQC bring to an IM
    initiative?

Transformation
22
DQC Priorities
  • Establish effective governance of All business
    data and their definitions (including reference
    data)Enterprise performance measures and their
    calculation basis Business ownership of data
    items and measures. Prioritise and decide data
    issues. Provide feedback on Data Controls, Data
    Validation and Quality.Business work-arounds
    that deal with issues without requiring
    additional IT work.

Transformation
23
Deliver
Transformation
24
Mini Case Studies
  • Campaign Prediction
  • Deposit Flow Prediction
  • Value-add Numbers
  • Mobile Delivery
  • None (Im bored already)

Examples
25
Example 1Campaign Success Prediction
Examples
Nothing Will Ever Happen To You
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26
Baselined
  • Add filtered campaign dates (from weekend
    filter), add day number and line of best fit data
    points from a previous campaign.Scale curve
    amounts to the new 2bn end target usingratio of
    2bn Powerade campaign end value. 'Scaled
    Amount'
  • Scaled days using ratio of New Previous
    Campaign (117/87).
  • Calculate formula for the new curve.NOTE Note
    MS Excel doesn't do this properly, so used
    www.zunzun.com 2D polynomial data modeler.
  • Using formula for the new curve, calculate the
    whole day number amounts prediction

Best Fit Daily Prediction
Examples
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Examples
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3
2
1
Examples
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Examples
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Examples
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1
2
3
5
4
6
Examples
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Example 2Deposit Flow Trend Prediction
Examples
Well, enough of the coy sexual byplay, Bobby.
Ive got to dive back into the data stream.
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33
Examples
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Examples
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http//chart.apis.google.com/chart?chtlschs250x
100chdt10,15,20,19,25,46,52,80,75,110,100,140,1
60
Examples
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Example 3Transparency
Examples
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Examples
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Example 4Right Time, Right Format
Examples
Know what we do with nosy little creeps like
you? We deprive them of their broadband data
connections, thats what we do.
Page
39
DeliveringThe Mobile Challenge
Examples
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The Mobile PM Challenge ...
  • Blackberry standard
  • No vendor tool installed thatwas acceptable
  • Decision to use the most advanced graphical
    representation techniqueavailable

Examples
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Examples
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Examples
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So hows it feel to doing this stuff?
Lessons
44
Lessons
Page
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What It TakesA Journey Of 1,000 Miles ...4
Things To Avoid, 12 Principles, 968 Actions, 15
Arguments and 1 Reality
Lessons
Page
46
Question
  • What are some of the biggest mistakes
    organisations make when attempting an information
    change?

Transformation
47
Mistake 1
Lessons
Trust us, we know what we are doing
Page
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48
Mistake 2
Garbage In Garbage Out
Lessons
Page
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Mistake 3
We just want a dashboard first
Lessons
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Mistake 4
Arguments and confusion because meanings arent
defined and agreed
Lessons
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Prevention ...
Right information to Right people at Right time
in the Right format so they can make the Right
decisions
Lessons
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52
Establish Principles
Lessons
Hes charged with expressing contempt for
business intelligence.
53
Data Control Principles
Lessons
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Data Quality Principles
Lessons
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968 ETL Quality Activities
Auditing 250History Tracking 178 Calculations
170Validations 158Augmentation 104Fixed Values
63 Hard-Close Dates 43 Keys 2
Lessons
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Data Quality Metrics
  • Accuracy degree of confidence on data being
    free of error/defect Completeness extent to
    which data is not missing and is of sufficient
    breadth and depth for the task at hand
    Consistency degree to which common data across
    different sources follows the same definitions,
    codes and formats Timeliness degree to which
    data is up to date Security degree to which data
    confidentiality, integrity and availability has
    been maintained Fit for Purpose degree to which
    data is relevant, appropriate and meets business
    specifications.

Lessons
57
Dilbert is real ...
Lessons
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What can vendors do for you?
Lessons
59
Lessons
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Outcomes Greater Insight
  • In-your-face daily performance tracking
  • portfolio
  • marketing campaigns
  • Performance predictions
  • Competitor benchmarking
  • Manual review of past decisions
  • Encourage fact-based decisions
  • Better IM and IT coordination

Lessons
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A final word ...
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(No Transcript)
63
Business Intelligence CORTEX
  • A new online Australian community of
  • BI, PM, DWH, ETL and Analytic professionals
  • Forums Research Jobs
    Local BI News Events
  • www.tbig.com.au/forums
  • sbennett_at_tbig.net.au 0413 666 724
  • Oz Analytics Blog analytics.typepad.com

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