translating big raw data into small actionable information
DESCRIPTION
Any approach to Big Data needs to be based rigorously on business value. Big Data exists across the organisation’s operating landscape and not just for customers. Such data presents the potential for significant value that can enhance the way organisations do business and interact with external parties. There is a need for a realistic and achievable approach to translating Big Raw Data into Small Actionable Information. Big Data is intrinsically linked to digital operations and associated digital transformation.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.The approach to generating real value needs to encompass:1. Definition and understanding of Big Raw Data landscape including data sources, platforms, systems and applications parties, journeys and interactions2. Identification and selection of high potential value use cases for implementation for selected parties3. Definition of IT strategies, facilities, tools, techniques and resources to reduce the volume of Big Raw Data to translate it into Small Actionable Information4. System and application changes to actualise use cases5. Understanding and appreciation of wider operational context – Campaign Management, Customer Relationship Management, Customer Experience Management, Customer Value Management 6. Implementation of underpinning data governance and data privacy protocols7. Organisational and process changes to identify, implement and operate use casesThere 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.Exploiting Big Raw Data to generate business value requires resources. This means management commitment and sponsorship.TRANSCRIPT
Translating Big Raw Data Into Small Actionable Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney
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
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
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
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
Big Raw Data And Digital
• Big Raw Data is intrinsically linked to digital operations and associated digital transformation
April 12, 2016 8
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
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Extended Dimensions Of Big Raw Data Collection
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External Parties
Channels
Steps and Actions Within
Interactions
Activities Across
Channels And Other
Data
Roles Within Parties
Interactions
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
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Translating Big Raw Data Into Small Actionable Information
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Small Actionable Information
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
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Taking A Value-Based Approach To Big Raw Data
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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
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
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
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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
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Definition Of Use Cases
• For each use case, define the following to describe it:
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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
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
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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
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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
Use Cases And External Party Stages – Customer Journey Stages Examples
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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
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
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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
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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
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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
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
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Business Model Canvass
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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
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
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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
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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
April 12, 2016 35
Statistics
• I can take all this …
• … And give you one derived number (average) − 107941.931
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
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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
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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
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?
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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
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
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
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
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
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
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
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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
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
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
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
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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
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
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
Additional Big Raw Data Layers
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Business Processes
Big Raw Data Strategy
Actionable Information and Business Value
Skills and Resources
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
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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
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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
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
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
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Core Big Raw Data Reference Architecture – Data Storage Component
• Provides data storage and data access facilities including backup, recovery
April 12, 2016 60
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
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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
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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
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
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
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
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
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
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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
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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
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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
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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
Organisation And Process Changes To Use Small Actionable Information
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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
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
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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
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More Information
Alan McSweeney
http://ie.linkedin.com/in/alanmcsweeney
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