tinia halfar - transunion - data strategy presentation at the chief data officer forum, africa
TRANSCRIPT
How to develop a vision and strategic plan for data in the organisation
Tinia Halfar
Setting the Data Office direction
6 JUNE 2016
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Why should I invest in data?
Leverage data to predict changes in the marketplace
Personalise your customer experience
Improve internal operations
Prevent potential fraud
The average cost of a corporate data breach increased 15% in the last year
Targeted marketing
Data is growing and becoming more disparate
Bad data quality is costing your business…a lot
Your competition is investing in data and analytics
Data is the best tool for process improvement and optimisation
If analysts are spending 40% of their time manipulating data and creating reports, how much time are they actually spending analysing the data?
A study found that the cost of bad data was equal to between 10% - 25% of the organisation’s revenue
Sources: https://www.import.io/post/5-undeniable-reasons-to-invest-in-data-today/ , http://knowlton-group.com/5-reasons-to-invest-in-data-and-analytics-in-2016/
Giving your data a meaningful voice is about choosing to invest in the right human skills
What is a Data Office?
Steps to articulate your Data Office
Direction
Break-away workgroups
AGENDA
1
2
3
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What is a data office?
The Data Office is accountable for getting their organisations to treat data as an enterprise asset (1)
Enterprise Information Management specialises in finding solutions for optimal use of information within organisations (2)
Enterprise information management is a set of business processes, disciplines and practices used to manage the information created from an organisation's data. (3)
Enterprise data management is a concept that refers to the ability of an organisation to precisely define, easily integrate, and effectively retrieve data for both internal applications and external communication (4)
The data office effectively manages, enhances and publishes data and information assets to the business community.
Sources: 1. Http://www.information-management.com/news/news/key-considerations-in-establishing-a-chief-data-office-10025545-1.html 2. https://en.wikipedia.org/wiki/Enterprise_information_management 3. http://searchcontentmanagement.techtarget.com/definition/enterprise-information-management-EIM 4. https://en.wikipedia.org/wiki/Enterprise_data_management
ALSO REFERRED
TO AS:
Enterprise Data /
Information Management
The Chief Data Office
Business Intelligence
Hub
Big data team
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Session Outcome: 5 steps to articulate your data office direction
The 5 step process navigates your thinking and highlight concepts to consider in order to articulate your data office direction.
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Porter’s Generic Strategies Cost Leadership Differentiation Focus
Description
• Superior profits through lower costs
• Costs can be reduced through improving operational efficiencies
• Effective process controls• Target broad markets
• Creating a product or service that is perceived as being unique throughout the industry and more attractive to a particular target market
• Differentiation from competitors' products as well as a firm's own products
• Concentrating on a limited part of the market
• Enjoys a high degree of customer loyalty
• Premise that a segmented group can be better served by focusing entirely on it
Core Competencies
Required
• Operational excellence • Vendor and supply chain
management (JIT)• Forcasting and planning
• Customer insights• Innovation and research• Marketing analytics• Creative product dev
• Segmentation analytics• Loyalty behaviour • Real-time, personalised
analytics
Example
Business StrategyIdentify your key business goals and objectives
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
Sources: 1. http://www.slideshare.net/dipalij07/porters-generic-strategies-with-examples; 2. https://en.wikipedia.org/wiki/Product_differentiation 3. https://en.wikipedia.org/wiki/Cost_reduction
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I need to adhere to regulatory / compliance requirements
Highly regulated environments – Example: Credit Bureaus and Banks - Twin Peaks model of financial sector regulation,
POPI, Treating Customer Fairly, BCBS 239 Risk data aggregation and reporting.
I want to use my data to make better decisions
Identify customer acquisition strategies. Determine cross sell opportunities. Drive actions through propensity modelling.
Use of predictive analytics to improve decision making. Widely adopted MI reporting. Interactive BI dashboards and
self-service views.
I want to use my data to improve customer experience
360° / single view of a customer. Personalisation via segmentation to enable bespoke and tailor-made
experiences. Moving from a product-centric approach to a customer-centric approach. Frictionless, on-demand and
real-time customer experiences.
I want to monetise my data by creating new products and services
Building and commercialising data products.Selling data as a service or white label your data office
technology or capabilities.
What is your Data Vision?What kind of data office are you setting up?
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
The structure and focus of your data office depend on the objectives that you need to achieve as well as your business strategy. Four distinctly different data office objectives are represented here and you can aspire to achieve more than one of these objectives.
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I need to adhere to regulatory / compliance requirements
Highly regulated environments – Example: Credit Bureaus and Banks - Twin Peaks model of financial sector regulation,
POPI, Treating Customer Fairly, BCBS 239 Risk data aggregation and reporting.
I want to use my data to make better decisions
Identify customer acquisition strategies. Determine cross sell opportunities. Drive actions through propensity modelling.
Use of predictive analytics to improve decision making. Widely adopted MI reporting. Interactive BI dashboards and
self-service views.
I want to use my data to improve customer experience
360° / single view of a customer. Personalisation via segmentation to enable bespoke and tailor-made
experiences. Moving from a product-centric approach to a customer-centric approach. Frictionless, on-demand and
real-time customer experiences.
I want to monetise my data by creating new products and services
Building and commercialising data products.Selling data as a service or white label your data office
technology or capabilities.
What is your Data Vision?What kind of data office are you setting up?
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
The structure and focus of your data office depend on the objectives that you need to achieve as well as your business strategy. Four distinctly different data office objectives are represented here and you can aspire to achieve more than one of these objectives.
Cost Leadership strategy
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I need to adhere to regulatory / compliance requirements
Highly regulated environments – Example: Credit Bureaus and Banks - Twin Peaks model of financial sector regulation,
POPI, Treating Customer Fairly, BCBS 239 Risk data aggregation and reporting.
I want to use my data to make better decisions
Identify customer acquisition strategies. Determine cross sell opportunities. Drive actions through propensity modelling.
Use of predictive analytics to improve decision making. Widely adopted MI reporting. Interactive BI dashboards and
self-service views.
I want to use my data to improve customer experience
360° / single view of a customer. Personalisation via segmentation to enable bespoke and tailor-made
experiences. Moving from a product-centric approach to a customer-centric approach. Frictionless, on-demand and
real-time customer experiences.
I want to monetise my data by creating new products and services
Building and commercialising data products.Selling data as a service or white label your data office
technology or capabilities.
What is your Data Vision?What kind of data office are you setting up?
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
The structure and focus of your data office depend on the objectives that you need to achieve as well as your business strategy. Four distinctly different data office objectives are represented here and you can aspire to achieve more than one of these objectives.
Differentiation strategy
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I need to adhere to regulatory / compliance requirements
Highly regulated environments – Example: Credit Bureaus and Banks - Twin Peaks model of financial sector regulation,
POPI, Treating Customer Fairly, BCBS 239 Risk data aggregation and reporting.
I want to use my data to make better decisions
Identify customer acquisition strategies. Determine cross sell opportunities. Drive actions through propensity modelling.
Use of predictive analytics to improve decision making. Widely adopted MI reporting. Interactive BI dashboards and
self-service views.
I want to use my data to improve customer experience
360° / single view of a customer. Personalisation via segmentation to enable bespoke and tailor-made
experiences. Moving from a product-centric approach to a customer-centric approach. Frictionless, on-demand and
real-time customer experiences.
I want to monetise my data by creating new products and services
Building and commercialising data products.Selling data as a service or white label your data office
technology or capabilities.
What is your Data Vision?What kind of data office are you setting up?
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
The structure and focus of your data office depend on the objectives that you need to achieve as well as your business strategy. Four distinctly different data office objectives are represented here and you can aspire to achieve more than one of these objectives.
Focus strategy
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Data Capabilities
Design and BuildThe capabilities toplan your delivery
ManageThe capabilities to manage your delivery
PublishThe capabilities to expose the data to the end-user
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
Business Intelligence
AnalyticsDiscovery Science
Visualisation
Geospatial Predictive
Business Rules
Governance
Performance Privacy
QA Security
Stewardship
Warehouse
Meta Data
Reference and MDM
Architecture Integration Modelling Development Engineering
Management Information
Internal data sources External data sources
Publish to end user
There are many data competencies required to enable raw data to be transformed and packaged into insight and information for the end-user to digest. The data competencies which build on each other are grouped into 3 categories:
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Capabilities you might want to include
Data Capabilities Regulation DecisionMaking
Customer Experience
MonetiseData
Data ArchitectureData Modelling
Data Governance
Data Warehouse ManagementData Quality Management
Meta Data ManagementBusiness Rules Management
Management Information
Business IntelligenceData Visualisation
Data AnalyticsData Science
Predictive Analytics
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
Illustrative
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Data Capability Maturity Assessment
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
Once you understand the objective of your data office you need to identify the competencies required to achieve this. In order to prioritise your efforts you need to perform a maturity assessment.
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Key Components of Maturity Assessment and Target Setting
DataCompetencies
Current Level
Target Level
GapImportance
Data Science 0 3 3 1
Data Architecture 1 3.5 2.5 3
Data Governance 1 4 3 4
Predictive Analytics 1 2 1 1.8
Data Modelling 1.5 2.5 1 3.5
Business Intelligence 1.5 3.5 2 4
Warehouse Management 3 4.5 1.5 2.5
Management Information 3 3 0 3
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
AS-ISEvaluate your current
capability maturity levels
TARGETThe desired level of maturity
required
GAPThe distance between where a process level
resides and where it needs to be
IMPORTANCEHighlight priority
capabilities by importance: Rating range 1-4. 1= least
important, 4=critical
You can effectively determine your priorities by assessing your current level of maturity and setting the target level required to achieve your objective. Additionally, you need to rank the importance of each one of the capabilities.
Illustra
tiveRatings: Level 0 – Non-existentLevel 1 – Initial / Ad HocLevel 2 – Repeatable but IntuitiveLevel 3 – DefinedLevel 4 – Managed and measurableLevel 5 – Optimised
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Prioritise your next key initiatives
0 1 2 3 40
1
2
3
4
5
Data Science
Data Architec-ture
Data Gov-ernance
Predictive ana-lytics
Data Modelling
Business Intel-ligence
Warehouse Management
Management In-formation
GAP
IMPO
RTAN
CE
Once you understand your key priorities you need to determine how you will reach the target level for each:
Define resource requirementsSourcing strategy: Build vs. BuyAligning architecture requirements to your data strategyRobust change management programme
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
Below, the capabilities were mapped according to the gap between the current and target level of maturity as well as the importance of each. By plotting this on a chart you can easily identify the key priority areas.
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Data Office Delivery model design decisions
Localised
Hub and Spoke
FullCentralisation
Local instance, with selected components
delivered from centralised location
Local instance with LOB delivery and
control
Capability fully controlled and delivered
from a centralised location
Centre of Excellence
Local instance, but governed through
CoE frameworks and standards
Description
• Total central control and management oversight
• Cost savings related to system infrastructure economies of scale
• Specialised skill sets and improved security
• Local instance and process execution improves LOB performance
• Centralised control of key oversight and administrative functions efficiency
• System ownership is shared
• No integration where there is a big difference in product / service
• Data movement performance advantage due to local connectivity
• Faster integration with local systems
• Benefits of autonomy but with centralised controls and standards
• Serve as an advisory role• First step to change from a localised model
to a centralised model
Country Solution Centralised Solution CoEKey
Benefits
• Who will be responsible for data
– Business vs. IT
• Executive buy-in
• Compelling vision for change
• Monetary benefit vs. Investment
• Data vs. Information
• Risks and regulation
• Complexity
Business Strategy
Data Vision
Data Capability
Maturity Analysis
Delivery Model
Other Considerations
The final step in articulating your data office direction is to decide on your delivery model as well as other key considerations to take into account.
Articulate your Data Office direction
Business Strategy
Data Vision
Data Capabilities
Maturity Analysis
Delivery Model
Break-away groups