This document discusses translating big raw data into small actionable information. It begins by outlining some of the challenges with big raw data, such as its wide scope and lack of common definitions. It then advocates focusing on developing approaches and solutions to extract useful insights and business value from raw data. The document describes how to define potential use cases across an organization's various external interactions and priorities. It provides a template for documenting use cases and evaluating their potential value and implementation requirements. Finally, it cautions against an illusion of being able to directly manage outcomes and stresses the importance of influencing them through appropriate use cases and activities.

1. Translating Big Raw Data
Into Small Actionable
Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney

2. Big Raw Data
• Scope is (too) wide and vague
• There is no common understanding with multiple separate
definitions
• Approaches are different and conflicting
• Complexity is very high
April 12, 2016 2

3. Big Raw Data
• Is just that …
• Lots of it
• From different sources
• In different formats
• With different contents
• At different times
• With different measurements
• With variable accuracy
• That changes constantly
April 12, 2016 3

4. Big Raw Data
• So ignore the issues of scope, lack of definition, conflicts,
differences and complexity and focus on the identification,
specification, development and implementation of
approaches, strategies, processes, expertise, solutions and
systems and data that can provide actionable information
to achieve outcomes that produce business value
April 12, 2016 4

5. Organisation Operating Landscape
April 12, 2016 5

6. Organisation Operating Landscape
• Multiple external actors interacting with the organisation
in different ways across different channels
• Many sources and types of data available across external
interacting parties, channels/platforms and types of
interaction
• Focus tends to be on customers and potential customers
− Do not ignore interactions with other parties and their potential
for improvement and the generation of value
April 12, 2016 6

7. Data Collection Across Organisation Operating
Landscape
April 12, 2016 7




8. Big Raw Data And Digital
• Big Raw Data is intrinsically linked to digital operations and
associated digital transformation
April 12, 2016 8

9. Core And Extended Dimensions Of Big Raw Data
• Core dimensions of raw data available
− External Parties – parties performing interaction
− Interactions – processes being interacted with
− Channels – device and channel/platform used for interaction
• Extended dimensions of raw data available
− Roles Within Parties – extend external parties to include roles
− Steps and Actions Within Interactions – extends interaction
− Activities Across Channels And Other Data – extends channel
dimension to include data integrated across different
channels/platforms and from other sources
April 12, 2016 9

10. Core Dimensions Of Big Raw Data Collection
April 12, 2016 10
External
Parties
Channels
Interactions

11. Extended Dimensions Of Big Raw Data Collection
April 12, 2016 11
External
Parties
Channels
Steps and
Actions
Within
Interactions
Activities
Across
Channels
And Other
Data
Roles
Within
Parties
Interactions

12. Core And Extended Dimensions Of Big Raw Data
• Very large volumes of raw data potentially available across multiple
dimensions
• Opportunity exists for organisations to gather extensive data from
multiple sources
• Data can be combined with data from other sources such as existing
systems
• Data presents the potential for significant value that can enhance
the way organisations do business and interact with external parties
• The value needs to be identified and identifying this value in a
prioritised manner will both save and generate money
• Need a realistic and achievable approach to translating Big Raw Data
into Small Actionable Information
• Need to limit what is collected and analysed
• Need to focus on deriving value
April 12, 2016 12

13. Translating Big Raw Data Into Small Actionable
Information
April 12, 2016 13
Small Actionable Information

14. Translating Big Raw Data Into Small Actionable
Information
• Approach to generating real value needs to encompass:
− Definition and understanding of Big Raw Data landscape including data
sources, platforms, systems and applications parties, journeys and interactions
− Identification and selection of high potential value use cases for
implementation for selected parties
− Definition of IT strategies, facilities, tools, techniques and resources to reduce
the volume of Big Raw Data to translate it into Small Actionable Information
− System and application changes to actualise use cases
− Understanding and appreciation of wider operational context – Campaign
Management, Customer Relationship Management, Customer Experience
Management, Customer Value Management
− Implementation of underpinning data governance and data privacy protocols
• Need to be aware of the risks and the reputational damage that unfettered use of Big
Raw Data can give rise to
− Organisational and process changes to identify, implement and operate use
cases
• Big Raw Data can be used to select and then drive the actioning of
use cases
April 12, 2016 14

15. Taking A Value-Based Approach To Big Raw Data
April 12, 2016 15
Define Big
Raw Data
Landscape
High Value
Use Cases
IT
Infrastructure
Understanding
of Wider
Operational
Context
Data
Governance
and Data
Privacy
Organisational
and Process
Changes
System and
Application
Changes

16. Translating Big Raw Data Into Small Actionable
Information
• There are only a limited number of actionable insights
available from Big Raw Data
• There are only a limited number of actions the
organisation can reasonably take
• It is important not to swamp the organisation with lots of
irrelevant pseudo insights
• It is important to prioritise the actions recommended from
the derived insights
April 12, 2016 16

17. Identification Of High Potential Value Use Cases
• Select party or parties included in the use cases
• Select the objective such as sell more, improve service
time, prevent customer loss, reduce cost of service,
increase efficiency
− Not all use cases can be implemented because of time, cost and
resource constraints
• Review use cases to identify those with the greatest
potential
April 12, 2016 17

18. Use Cases Across Organisation Operating Landscape
April 12, 2016 18
Use
Cases
Use
Cases
Use
Cases

19. Use Cases In Operating Landscape
• Potential use cases can occur anywhere in the operating
landscape
• Use cases can be external – linked to external party
interactions and triggered by actions/events – or internal –
within the organisation relating to areas such as improving
operational efficiency, determining sales effectiveness of
products/services, trigger partner care event
April 12, 2016 19

20. Definition Of Use Cases
• For each use case, define the following to describe it:
April 12, 2016 20
Element Details
Use Case Name A meaningful name assigned to the use case
Description A description of the use case that will summarise how the use case is invoked, the flow of information, the
actors involved and the expected outcomes
Use Case Type Use cases can be external – linked to external party interactions and triggered by actions/events – or
internal within the organisation relating to areas such as improving operational efficiency, determining sales
effectiveness of products/services, trigger partner care event
Parties Involved (And Roles) The external and internal parties involved in the use case and their roles
Process/Stage/Step An indication of the expected stage within the party life journey to which the use case applies
Trigger/Action/Event The action or event that triggers the use case
Business Objective The business objective intended by the use case that describes the value generated and contains a
justification for its implementation
Business Metrics The internal business metrics to be used to measure the performance of the use case
Channel(s)/Platform(s) The channels and platforms to which the use case applies
Party Experience Metrics The party experience metrics to be used to measure the performance of the use case
Data Required The data required to enable the operation of the use case
Optional Data Additional and optional data that will add value to the operation of the use case
Data Privacy The data privacy implications of the operation of the use case
Processing The processing performed in the use case
Value Generated A measure of the expected value generated by the use case
Implementation Estimate An estimate of the resources/time/cost to implement the use case
Operation Estimate An estimate of the resources/time/cost to operate the use case after implementation

21. Definition Of Use Cases
• Use the use case analysis to prioritise their
implementation based on a balanced view
• Use cases must be viewed within the context of campaign
management
• Use cases and their associated offers need to be
understood as a whole so there are no gaps or
inconsistencies
• You need to understand the impact of use cases on the
organisation in areas such as increased workload and
affect on revenue and margin
April 12, 2016 21

22. Use Cases And External Party Journey Stages
• Depending on the nature of the organisation and the type
of product/service supplied, external parties will interact
differently
− Once-off products
− Continuous services
• External party interactions will have a standard journey
through processes/functions and exceptions/deviations
from this “happy path”
• External party journey will differ depending on party type
and the type of product/service supplied
April 12, 2016 22

23. Customer Journey For Continuous Service Provider
Indicative Stages
• Design use cases to suit the party journey and the interactions
April 12, 2016 23
Customer Journey
Model
Buying
Be Aware
Observe
Learn
React
Research/
Interact
Request
Detail
Request
Clarification
Select and
Buy
Select
Product/
Service
Place Order
Receive
Using
Use Product/
Service
Use
Review
Usage
Evaluate
Value
Manage
Account
Manage
Profile/
Service
Requests
Service/
Support
Receive Help
Receive
Resolution
Provide
Feedback
Complain
Pay
Review Bill
Verify or
Dispute
Pay
Manage Debt
Sharing
Renew/
Extend/
reduce
Add/
Remove
Products/
Services
Renew
Contract
Recommend
Refer
Product/
Service
Gain Loyalty
Leave
Feedback
Recover
Leave
Return

24. Use Cases And External Party Stages – Customer
Journey Stages Examples
April 12, 2016 24
Be Aware
Research/ Interact New
Select and Buy
Use Product/ Service
Manage Account
Request Service/ Support
Pay
Renew/ Extend/ Reduce
Recommend
Leave
Return
Location Based
Personalised Offers
Device Based
Personalised Offers
Offers Based on
Browsing History
Up Sell/Cross Sell On
Order/Checkout
Research/ Interact Existing
Personalised Offers
While Browsing
Propensity Analysis
for Campaigns
Segmentation
Analysis
Fraud Detection
Personalised Offers Usage Analytics
Personalised Offers
Debt Management
Personalised Offers
Personalised Offers
Pro-Active Care
Propensity Analysis
for Campaigns
Segmentation
Analysis
Propensity Analysis
for Campaigns
Segmentation
Analysis
Recovery Offers
Winback Offers

25. Use Cases And External Party Stages – Customer
Journey Stages Examples
• There are many potential use cases involving the
successful use of Big Raw Data
• Selection of uses cases implementation needs to be done
carefully to balance effort and expected value
April 12, 2016 25

26. Beware Of The Illusion of Outcomes When
Developing Use Cases
• Operation of use cases increases the likelihood that the desired outcomes
will occur
• Outcomes cannot be managed, only influenced
• Outcomes can include:
− Sales
− Sales conversion rate
− Revenue
− Profit
− Cashflow
• Outcomes can only be influenced through activities:
− Improved customer satisfaction
− More sales activity
− Greater value for money
• Focussing on appropriate uses cases processes is a key way to influence
outcomes and deliver value
• Be careful of use cases that generate a lot of activities that do not generate
outcomes
April 12, 2016 26

27. April 12, 2016 27
Illusion Of Attempting To Manage Outcomes
Sell More
Products/
Services and
More
Profitably
Generate More
Profit
Identify, Acquire and Retain the Right
Customers
Fulfil Orders Correctly and Satisfactorily
Manage Customer Relationships
Be Easy to Do Business With
Be an Organisation Customers Want to Do
Business With
Generate and Maintain High Customer
Satisfaction
Develop and Sell the Right Product at the
Right Price
Organisation Objectives and Activities Outcomes
You cannot force
customers to buy
more products and
services …
… But you can make
it easier for
customers to do so
with appropriate use
cases
Sell Additional Product/Services to
Customers
Broaden and Deepen the Relationship
Maintain and Improve Margin

28. April 12, 2016 28
Use Cases In Operating Landscape
Business
Controlling
Process
Processes That
Direct and Tune
Other Processes
Core Processes
Processes That Create Value for the Organisation
Product and
Service
Development
Product and
Service
Market and
Sales
Product and
Service
Sales
Fulfilment
Customer
Service and
Support
Supporting Enabling Processes
Processes That Supply Resources to Other Processes
Channel
Management
Partner and
Supply
Management
Human
Resources,
Legal,
Facilities
Information
Technology
Financial
Management
and Business
Acquisition
Business
Measurement
Process
Processes That
Monitor and
Report the
Results of Other
Processes
External Party Interactions
Partner and Supplier Interactions
Business Environment Interactions
Competitors, Governments Regulations and Requirements, Standards, Economics
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case
Use
Case

29. Business Model Canvass
• Consider using the Business Model Canvas (developed by Alexander
Osterwalder) to each use case
• Divides business into nine elements in four groups
− Infrastructure
• Key Partners - the key partners and suppliers needed to achieve the business model
• Key Activities - the most important activities the business must perform to ensure the
business model works
• Key Resources - the most important assets to make the business model work
− Offering
• Value Propositions - the value, products and services provided to the customer
− Customers
• Customer Relationships - the customer relationships that need to be created
• Channels - the channels through which the business reaches its customers
• Customer Segments - the types of customers being targetted by the business model
− Finances
• Cost Structure - the most important costs incurred by the business model
• Revenue Streams - the sources through which the business model gets revenue from
customers
April 12, 2016 29

30. Business Model Canvass
April 12, 2016 30
Key Partners
• Who are our key partners?
• Who are our key suppliers?
• What Key Resources do we acquire
from partners?
• What Key Activities do partners
perform?
MOTIVATIONS FOR
PARTNERSHIPS
• Optimisation and economy
• Reduction of risk and uncertainty
• Acquisition of resources and skills
Key Activities
• What key activities do our value
propositions require
• What are our distribution channels?
• What are our customer relationships?
• What are our revenue streams?
CATEGORIES
• Production
• Problem Solving
• Platform/Network
Value Propositions
• What value do we deliver to our
customers?
• Which of our customers’ problems are
we helping to solve?
• What bundles of products and
services do we offer to each customer
segment?
CHARACTERISTICS
• Novelty
• Performance
• Customisation
• “Getting the Job Done”
• Design
• Brand
• Status
• Cost Reduction
• Risk Reduction
• Accessibility
• Convenience/Usability
Customer Relationships
• What type of relationship does each of our
customer segments expect us to establish
and maintain with them?
• What ones have we already established?
• How are they integrated into our business
model?
• How much do they cost?
EXAMPLES
• Personal assistance
• Dedicated personal assistance
• Self-service
• Automated services
• Communities
• Co-creation
Customer
Segments
• For whom are we creating
value?
• Wo are our most important
customers?
• Mass market
• Niche market
• Segmented
• Diversified
• Multi-sided platform
Key Resources
What key resources are required by our
Value propositions Distribution channels
Customer relationships
Revenue streams
TYPES OF RESOURCES
Physical
Intellectual
Human
Financial
Channels
• Through which channels do our customer
segments want to be reached?
• How are we reaching them now?
• How are our channels integrated?
• Which ones are most cost-efficient?
• How are we integrating them with customer
processes?
CHANNEL PHASES
• Awareness - How do we raise awareness
about our products and services
• Evaluation – How do we help customers
evaluate our value proposition?
• Purchase – How do we allow customers
purchase specific products and services?
• Delivery – How do we deliver a value
proposition to customers?
• After Sales – How do we provide post-
purchase customer support?
Cost Structure
• What are the most important costs inherent in the business model?
• Which key resources are the most expensive?
• Which key activities are the most expensive?
IS THE BUSINESS MORE:
• Cost Driven – leanest cost structure, low price value proposition, maximum automation, extensive
outsourcing
• Value Driven – focussed on value creation, premium value proposition
SAMPLE CHARACTERISTICS
• Fixed costs
• Variable costs
• Economies of loading
• Economies of scale
Revenue Streams
• What value are customers really willing to pay for?
• What are they currently paying for?
• How are they currently paying?
• How would they prefer to pay?
How much does each revenue stream contribute to overall revenue?
TYPES FIXED PRICING DYNAMIC PRICING
• Asset sale • List price • Negotiation/bargaining
• Usage fee • Product feature dependent • Yield management
• Subscription fees • Customer segment dependent • Real-time market
• Lending/renting/leasing • Volume dependent
• Licensing
• Brokerage fees
• Advertising

31. Business Model Canvass And Use Case Identification
• Locate each use case within the Business Model Canvass to
understand its context and potential contribution to the
business
• This approach provides an understanding of the benefits of
implementing a use case and assists with their definition
April 12, 2016 31

32. Approaches To Translating Big Raw Data Into Small
Actionable Information
• Need an approach to translating Big Raw Data into small
actionable information
− Small data volumes make processing faster and easier
− Small data volumes make analysis and insights faster and easier to
perform and understand
• Key to making big data small is to reduce data volumes while
preserving as much underlying information as possible
− This means taking a large amount of raw data and producing descriptive
summaries
− Enabling you to see the wood from the trees, know the amount and
type of wood and make decisions about the use of the wood
• Create “datalet” for each party that summarises salient
information including segments and flags
April 12, 2016 32

33. April 12, 2016 33
Some Big Data

34. April 12, 2016 34
Sample Information
• 4,000 numbers representing anything
• 100% of the information is available here
• Very hard to see patterns, understand the situation, gain
insight and make effective decisions and understand their
consequences
• The numbers do not lie but they are innocent creatures
and can be made to lie
• Need techniques that extract meaning and provide insight
without losing the information the data represents

35. April 12, 2016 35
Statistics
• I can take all this …
• … And give you one derived number (average)
− 107941.931

36. April 12, 2016 36
Statistic
• 4,000 numbers reduced to 1
• Reduced the amount of data by 99.975%
• But I have lost information
• Average value of 107941.931 is at best a simplistic view of
the data and at worst a distortion that misrepresents the
source data
• If I use the average without looking to understand the raw
data in more detail I am potentially creating a distortion
• Need to balance loss of information with reduction in data
volumes

37. April 12, 2016 37
More Statistics
• Be careful what statistics are used
• Do not generate statistics just because you can
• The use of statistics can give a false impression of certainty or meaning where there is none
Average Sum of all the values divided by the number of values 107941.93
Standard
Deviation
A measure of how widely values are dispersed from the average value 59904.19
Kurtosis Value that describes the relative peakedness or flatness of a distribution
where a positive value indicates a relatively peaked distribution and a negative
value indicates a relatively flat distribution
0.112
Skewness A measure of the asymmetry of a distribution around the average where a
positive value indicates a distribution with an asymmetric tail extending
toward more positive values and a negative value indicates a distribution with
an asymmetric tail extending toward more negative values
0.731
Mode The most frequently occurring value 23958
Median This the number in the middle where, half the numbers have values that are
greater than the median and half have values that are less – also called the
50th percentile
97909.5

38. April 12, 2016 38
Interpreting the Statistics
• I now know that the data is skewed towards lower values and has a
heavy tail indicating a small number of people with larger values
Statistic Value Interpretation
Average 107941.93 The average is higher than the median indicating that the data is
dispersed unequally towards higher values
Standard Deviation 59904.19 The high standard deviation indicates the underlying data is spread
across a wide range of values
Kurtosis 0.112 The positive value indicates that there is a peak in the data
Skewness 0.731 The positive values indicates a distribution with an unequal and
heavy tail extending toward more higher values
Mode 23958 In a large set of data where only a small number of data values are
the same, this has little value
Median 97909.5 When the median is less than the average, it means the data is
unequally distributed with a heavy tail extending toward more
higher values

39. What Actionable Insights Can Be Derived From Big
Data?
• Insights about individual parties based on their behaviour and changes in
behaviour, move to different segment within segmentation type,
propensity to take actions
− Changes in assigned segments, action propensity flags set, changes in behaviour –
level of usage, engagement, revenue, payment
• Grouping of individuals within party type based on types of behaviour and
identification of segments based on clusters of behaviour
− Create segmentations and segments based on characteristics such as value,
engagement, payment that allow appropriate handling of the individual party to
take place
• Create models that indicate propensities to engage in behaviours or take
actions
− Propensities such as increased likelihood of moving to a competitor, buying
additional products/services
• Trends in changes of behaviour of all parties or groups of parties
− What is happening to groups of parties and what are the implications for the
organisation: changes in volumes and levels of usage, engagement, revenue,
payment, profit? What impact are these trends having on the overall business?
April 12, 2016 39

40. Derivable And Actionable Insights
April 12, 2016 40
Individual Party
Insights
Apply
Segmentation
to Parties
Segmentation
Models and
Segments
Propensity Models
and Propensities
Group Trends Apply Propensity
Models to Parties
to Generate
Propensities
Identify
Overall
Trends
Changes in
Segments Can Be
Part of Propensity
Models

41. Party Segmentation
Party
Segments
Party
Segments
Segment
Class 1
Segment
1.1
Segment
1.2
…
Segment
Class 2
Segment
2.1
Segment
2.2
…
Segment
Class 3
Segment
3.1
Segment
3.2
…
Party
Segments
April 12, 2016 41

42. Segmentation
• Multiple segment types or classes can be defined for each party such
as:
− Value (such as Revenue – Fixed Cost – Handling Cost)
− Engagement/Behaviour – Number of Interactions, Number of Complaints
− Usage – products and services bought and levels of usage
− Location – geography
− Attitudes – early/late adopters
• Segments created for segment classes:
− High Value
− Average Value
− Low Value
• There can be multiple segments for each party
− Do not have too many
• Segment classes can be combined
• Approach to creating segments is to identify important sets of
behaviours that drive value
April 12, 2016 42

43. Segments
• Identify segments – groups of
parties that exhibit similar
behaviours and/or
characteristics
• Allocate parties to segments
• Party datalet should contain
segment information
• Not all segments have the
same importance in identifying
potential for value
− Develop segment-based
approaches to party
management
• Monitor party movement
between segments as possible
indicator of actions and trigger
for or target of use case
April 12, 2016 43

44. Party Movement Between Segments
• If a party moves between a segment this may be an
indicator of a potential change, such as
− Increased amount being spent by a customer means the customer
starts looking for alternatives
− Analysis of segment moves should cause a propensity flag to be
set
− Customer datalet should hold this information
April 12, 2016 44

45. Party “Datalets”
• Datalets are summaries of information on an individual party
• Datalet structure is different for each party type
• Datalet can contains details such as:
− Party Details
• Last account access
• Number of account accesses in interval
• Payment history and status
• Usage
• Access location
• Channels/platforms
− Segmentation
• Segment Class 1 segment
• Segment Class 2 segment
− Propensity Flags
• Leave
• Upgrade
− Campaign Details
April 12, 2016 45

46. Party “Datalets”
• Design datalet structure to hold just enough relevant data
to enable operation of use cases
• Datalet contents will change slowly over time
• Datalet is a point-in-time snapshot that drives quick and
effective decision making
• Can be underpinned by larger data structures including
data warehouse
April 12, 2016 46

47. Maintaining Datalets
April 12, 2016 47
Raw Data Sources
Segmentation Analysis
and Creation of
Segment Classes for
Parties
Party Datalet
Update Party
Datalets With
Latest Details
Assign/Update Party
Segments
Aggregated Raw Data
Propensity Models
Assign/Update Party
Propensities
Update Party Datalets
With Propensity Values
Update Party
Datalets With
Segments and
Changes

48. Maintaining Datalets
• Big Raw Data from multiple sources will need to be cleansed,
aggregated and prepared for processing
• Segmentation and propensity models will be developed and
maintained based on analyses of external parties
• Parties will be assigned segment and propensity values based
on behaviour
• Datalet will be updated with usage profile, segment and
propensity values
• Datalet can be interrogated to get a quick understanding of the
party
• Datalet can drive selection of use cases when party interacting
April 12, 2016 48

49. Lots Of Overlapping Disciplines – Customer Party
Example
April 12, 2016 49
Big Raw Data
Management
Campaign
Management
Customer
Experience
Management
Customer
Value
Management
Customer
Relationship
Management
Customer
Master Data
Management

50. Lots Of Overlapping Disciplines – Customer Party
Example
• Customer Value Management – managing customer relationships
for value
• Customer Relationship Management – focussed on the operational
and analytic aspects of managing the entire customer relationship
• Campaign Management – designing, creating, operating and
analysing the results of campaigns
• Customer Experience Management – measurement and
management of customer experience to make the customer journey
comfortable, objective driven and beneficial for service provider as
well as customer
• Customer Master Data Management – creating and maintaining a
single view of the customer across all customer facing systems and
associated data sources
• Big Raw Data Management – approach to handling data from
multiple sources and processing it for value
April 12, 2016 50

51. Lots Of Interconnected Overlapping Disciplines
April 12, 2016 51
Customer Value
Management
Customer
Relationship
Management
Customer Master
Data Management
Customer
Experience
Management
Big Raw Data
Management
Campaign
Management
Defines Approach to
Managing Customers
Defines Approach to Managing
Customer Experience
Feeds Into
Design of
Campaigns
Assists With
Design and
Operation
of
Campaigns
Provides Input to
Single View of the
Customer
Feeds Into Design of
Campaigns Through
Use Cases
Maintains
Single View
of the
Customer
Feeds Into
Design of and
Takes Results
from
Campaigns

52. Lots Of Interconnected Overlapping Disciplines
• Big Raw Data management sits in a wider operational and
organisational context
• Getting value from Big Raw Data management means
being aware of this wider context
April 12, 2016 52

53. Data
Administration,
Management and
Governance
Big Raw Data Indicative Core And Extended
Reference Architecture
April 12, 2016 53
Data Intake
Data Collection
Data Source
Management
Data Import
Data Processing
Data Quality/
Summary/ Filter/
Transformation
Data Aggregation
and Consolidation
Data Management,
Retention
Data Analysis
Data Modelling Use Case Triggering
Analysis and
Reporting
Management and
Administration
Data Storage
Data Storage
External Party Interaction Zones, Channels and Facilities
Platforms, Channels,
Data Sources
Security, Identity ,
Access and Profile
Management
Specific Applications
and Tools
Applications
Delivery and
Management Tools
and Frameworks
Operational and
Business Systems
Security, Privacy
and Compliance
Capacity Planning
Data Access
Physical Data Layer

54. Additional Big Raw Data Layers
April 12, 2016 54
Business Processes
Big Raw Data Strategy
Actionable Information and Business Value
Skills and Resources

55. Big Raw Data Indicative Core And Extended
Reference Architecture
• Core components are that are required to gather, manage
and process data
• Extended components are those that complete the Big
Raw Data picture
April 12, 2016 55

56. Core Big Raw Data Reference Architecture – Data
Intake Component
• Manages data sources and their data streams
• Processes data streams
• Handles large volumes of data
• Handles data variety
• Imports data
• Performs initial data standardisation
• Stores data
April 12, 2016 56

57. Core Big Raw Data Reference Architecture – Data
Processing Component
• Provides facilities for processing and transforming data,
data cleansing, data aggregation, data manipulation
• Enforces data quality
• Enriches data
• Applies data retention policies and standards
April 12, 2016 57

58. Core Big Raw Data Reference Architecture – Data
Analysis Component
• Provides facilities for data analysis and reporting, data
modelling and mining, identification of relationships
April 12, 2016 58

59. Core Big Raw Data Reference Architecture – Data
Administration, Management and Governance
Component
• Provides facilities for management and administration of
data
• Enforces data governance, data privacy
• Manages data capacity
April 12, 2016 59

60. Core Big Raw Data Reference Architecture – Data
Storage Component
• Provides data storage and data access facilities including
backup, recovery
April 12, 2016 60

61. Extended Data Reference Architecture – External
Party Interaction Zones, Channels and Facilities
• Contains components that:
− Generate Big Raw Data
− Implement use cases
− Manage campaigns
− Changes to existing systems and applications
− Supporting systems and tools
April 12, 2016 61

62. Organisation And Process Changes
• Multiple potential impacts across the organisation
− Impact on the organisation to establish and maintain or enhance
existing data function
− Impact on operational processes caused by increases in workload
associated with use cases being taken-up
− Impact on IT caused by the need for data infrastructure and by
the need for changes to systems and platforms to embed use
cases
− Impact on data privacy function caused by greater collection and
use of data
− Impact on sales, marketing and campaign management caused by
use case development and publication
April 12, 2016 62

63. Organisation And Process Changes To Use Small
Actionable Information
April 12, 2016 63
Interacting Parties Take a
Sequential View Of Their
Interactions With The
Organisation:
• I See It
• I Order It
• I Get It
• I Pay For It
• I Want Problems About It
Fixed
• I Want To Change/Upgrade
It
The Organisation
May Not Have Such A
Cross-Functional
View Or Structure

64. Sample Enterprise Business Process Groups –
Generalised Structure
April 12, 2016 64
Vision,
Strategy,
Business
Management
Operational Processes With Cross Functional Linkages
Management and Support Processes
External Party Facing
Processes
Supporting Processes

65. April 12, 2016 65
Sample Organisation Business Process Models –
Generalised Structure
Vision,
Strategy,
Business
Management
Core Operational Processes With Cross Functional Linkages
Management and Support Processes
Develop and
Manage
Products and
Services
Market and
Sell Products
and Services
Deliver
Products and
Services
Manage
Customer
Service
Human
Resource
Management
and
Development
Information
Technology
Management
Financial
Management
Facilities
Management
Legal,
Regulatory,
Environment,
Health and
Safety
Management
External
Relationship
and Partner
Management
Service,
Knowledge,
Improvement
and Change
Management
Vision and
Strategy
Business
Planning,
Merger,
Acquisition
Governance
and
Compliance

66. Sample Organisation Business Process Models –
Generalised Structure
• Core Operational Processes – drive and operate the
organisation, deliver value
• Management and Support Processes – internal processes
and associated business functions that enable the
operation and delivery of the core operational processes
• Vision, Strategy, Business Management – processes that
measure, control and optimise the operational and
support processes and set the direction of the organisation
April 12, 2016 66

67. Core And Supporting Processes And Interactions
• External parties interact with the organisation’s core
business processes
• Core business processes may be logical, cross-functional
representations of multiple, internal operational processes
that may or may not be connected to present a seamless
logical view
April 12, 2016 67

68. Operational Process Develop and Manage Products
and Services – Generic Breakdown
Develop And Manage Products And Services
Manage Product And Service Portfolio
Evaluate Performance Of Existing
Products/Services Against Market Opportunities
Define Product/Service Development
Requirements
Perform Discovery Research
Confirm Alignment Of Product/Service Concepts
With Business Strategy
Manage Product And Service Life Cycle
Manage Product And Service Master Data
Develop Products And Services
Design, Build, And Evaluate Products And
Services
Test Market For New Or Revised Products And
Services
Prepare For Production
April 12, 2016 68

69. Operational Process Market and Sell Products and
Services - Generic Breakdown
Market And Sell Products And Services
Understand Markets,
Customers, And Capabilities
Perform Customer And
Market Intelligence Analysis
Evaluate And Prioritise
Market Opportunities
Develop Marketing Strategy
Define And Manage Channel
Strategy
Define Pricing Strategy To
Align To Value Proposition
Define Offering And
Customer Value Proposition
Develop Sales Strategy
Develop Sales Forecast
Develop Sales
Partner/Alliance
Relationships
Establish Overall Sales
Budgets
Establish Sales Goals And
Measures
Establish Customer
Management Measures
Develop And Manage
Marketing Plans
Establish Goals, Objectives,
And Metrics For Products By
Channels/Segments
Establish Marketing Budgets
Develop And Manage Media
Develop And Manage Pricing
Develop And Manage
Promotional Activities
Track Customer Management
Measures
Develop And Manage
Packaging Strategy
Develop And Manage Sales
Plans
Generate Leads
Manage Customers And
Accounts
Manage Customer Sales
Manage Sales Orders
Manage Sales Force
Manage Sales Partners And
Alliances
April 12, 2016 69

70. Operational Process Deliver Products and Services
- Generic Breakdown
Deliver Products And
Services
Plan For And Acquire
Necessary Resources
Develop Production And
Materials Strategies
Manage Demand For
Products And Services
Create Materials Plan
Create And Manage Master
Production Schedule
Plan Distribution
Requirements
Establish Distribution
Planning Constraints
Review Distribution Planning
Policies
Assess Distribution Planning
Performance
Develop Quality Standards
And Procedures
Procure Materials And
Services
Develop Sourcing Strategies
Select Suppliers And
Develop/Maintain Contracts
Order Materials And Services
Appraise And Develop
Suppliers
Produce/Manufacture/
Deliver Product
Schedule Production
Produce Product
Schedule And Perform
Maintenance
Perform Quality Testing
Maintain Production Records
And Manage Lot Traceability
Deliver Service To Customer
Confirm Specific Service
Requirements For Individual
Customer
Identify And Schedule
Resources To Meet Service
Requirements
Provide Service To Specific
Customers
Ensure Quality Of Service
Manage Logistics And
Warehousing
Define Logistics Strategy
Plan And Manage Inbound
Material Flow
April 12, 2016 70

71. Operational Process Manage Customer Service
- Generic Breakdown
Manage Customer Service
Develop Customer
Care/Customer Service Strategy
Develop Customer Service
Segmentation/Prioritisation
Define Customer Service Policies
And Procedures
Establish Service Levels For
Customers
Plan And Manage Customer
Service Operations
Plan And Manage Customer
Service Work Force
Manage Customer Service
Requests/Inquiries
Manage Customer Complaints
Measure And Evaluate Customer
Service Operations
Measure Customer Satisfaction
With Customer
Requests/Inquiries Handling
Measure Customer Satisfaction
With Customer-Complaint
Handling And Resolution
April 12, 2016 71

72. April 12, 2016 72
Sample Enterprise Business Process Models –
Generalised Structure
Vision,
Strategy,
Business
Management
Operational Processes With Cross Functional Linkages
Management and Support Processes
Human
Resource
Management
Information
Technology
Management
Financial
Management
Facilities
Management
Legal,
Regulatory,
Environment,
Health and
Safety
Management
External
Relationship
Management
Knowledge,
Improvement
and Change
Management
Vision and
Strategy
Business
Planning,
Merger,
Acquisition
Governance
and
Compliance

73. Organisation And Process Changes To Use Small
Actionable Information
April 12, 2016 73
How The
Organisation Actually
Functions
Operational Processes With Cross Functional Linkages
Interacting Parties Take A
Sequential View Of Their
Interactions With The
Organisation:
• I See It
• I Order It
• I Get It
• I Pay For It
• I Want Problems About It
Fixed
• I Want To Change/Upgrade
It

74. Commitment
• Exploiting Big Raw Data to generate business value
requires resources
• This means management commitment and sponsorship
• Management must commit to legal and regulatory
compliance with security and privacy requirements
April 12, 2016 74

75. Summary
• Big Raw Data may not be the answer to any or all of your
business problems
• Big Raw Data can be used to generate value
• It is important to take a value-based approach to ensure
that you are doing it for a valid business reason
• Focus on high-priority value-generating issues
• Getting value from Big Raw Data means organisation and
process changes
April 12, 2016 75

76. More Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney
12 April 2016 76