translating big raw data into small actionable information

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

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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

Data Collection Across Organisation Operating Landscape

April 12, 2016 7

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

April 12, 2016 9

Core Dimensions Of Big Raw Data Collection

April 12, 2016 10

External Parties

Channels

Interactions

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

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


April 12, 2016 13

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

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


• 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 Across Organisation Operating Landscape

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Use Cases

Use Cases

Use Cases

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

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

April 12, 2016 22

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

April 12, 2016 25

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

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

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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Some Big Data

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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

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

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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?

April 12, 2016 39

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

April 12, 2016 46

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

April 12, 2016 50

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

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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

April 12, 2016 54

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

April 12, 2016 55

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

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

April 12, 2016 59

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

April 12, 2016 62

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


Management

Core Operational Processes With Cross Functional Linkages


Develop and Manage

Products and Services

Market and Sell Products and Services

Deliver Products and

Services

Manage Customer

Service

Human Resource

Management and

Development


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

April 12, 2016 68

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

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


Management



Human Resource

Management


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


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


12 April 2016 76
