Translating Big Raw Data Into Small Actionable Information
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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.
Translating Big Raw Data Into Small Actionable Information
- 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