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June 2013
Areas of assessment
For a PwC Data Maturity assessment, a team of PwC data experts review all key data processes with focus on governance and quality throughout. Below are some example indicators in each area.
© 2013 PricewaterhouseCoopers LLP. All rights reserved. In this document, “PwC” refers to the UK member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details.
130531-140026-TM-OS
Capture
• Data suppliers, customers and owners are identified• Key data attributes are identified including retention, type, volume and quality• Tools and techniques are appropriate for capture methods
Transform
• Extract, transform and load processes are defined and documented• Physical and logical security is maintained throughout staging and transfer processes• Data quality checks confirm completeness and accuracy
Store
• Architecture requirements are known including security, scalability and disaster recovery• Access to data stores is controlled and role dependant• Data is available as a single, integrated source
MI/BI
• Team members are considered experts in their field and can articulate business issues• Reporting is automated, timely and displayed effectively including dashboards and mobile devices
Analytics
• Integration of internal and external data for new solutions• Fully simulated business operations to evaluate decision impact• Predictive analytics and technology boundaries are pushed to fully realise data potential
Dispose
• Compliance with ADISA disposal and information security standards• Data retention roles, responsibilities and policies are defined and documented• Historic data is appropriately summarised for future use
www.pwc.co.uk
Data management maturityAre you ready for Big Data?
Tom Middleton+44 (0) 7779 813506
Cathy Nockles+44 (0) 7730 147787
Dave Williams+44 (0) 7734 959115
Data management – Getting ready for big data
Example data maturity heat map
What’s the challenge?
Data is growing at an exponential rate from diverse and complex sources. It is becoming extremely difficult to manage using traditional data management systems. Data tools and techniques can offer analysis of previously undervalued or unexploited data, giving fresh insight into customer behaviour and business performance which in turn drives competitive advantage.
Performing the assessment
An experienced team of data specialists will perform a review of your data processes to evaluate maturity, highlight key process risks and understand where the business can most benefit from improvement. We will deliver a report and roadmap which shows the maturity across the organisation, detailing the key areas where data is currently undervalued and advantages of taking the next steps to maturity.
Is big data for me?
Many organisations are unsure of what Big Data could mean for their business, how mature their data governance and processes are, and how mature they need to be to best leverage both their own structured data and the wider unstructured (Big Data) to their advantage. Big Data is a hot topic, but few businesses have their own internal data management processes in order to maximise the potential of their own data. For example, only businesses which can gain new and faster insight into customer sentiment will be most successful.
Big Data will give you the information you need to change the way you run your business.
PwC’s data maturity assessment
The PwC Data Maturity assessment provides an enterprise-wide view of governance, people, processes and technology which will both guide and inform on the opportunity for data, areas of required improvement, and the current maturity of the organisation when dealing with data in general.
Steps to data maturity
Gaining a view of maturity across all data processes will dictate the next steps towards fully realising the potential of your data.
Level 1limited
Level 2evolving
Level 3functionalexcellence
Level 4integratedexcellence
Level 5information
premium
Evolutionary stages for achievinginformation advantage capabilities
Low information maturity
Premium information maturity
Evolution
Technology
Data inconsistencyLittle centralisationSpreadsheet based
Departmental approachLocal governanceLittle ownership
Data quality management Defined data strategy
Central approach
MI strategy driving business performance
Single view of data
Timely analysis using core and external datasetsData-driven business
innovation
Organisation
Decision making
TechnologyAppropriate tools,
interfaces and validation.Architecture supportsvolume and security
ETL tools are robust,reliable and suitable
Future cost, capacity and requirementsare understood.
Multi-Tier/single sourcearchitecture
Single version of the truth.Mobile reporting.
Visualisation
Appropriate erasingtechniques
PeopleInput sta� are
appropriately trained.Supplier, customer and
owner are identified
Roles and responsibilitiesare defined and
documented
Access is role dependant(logical and physical).Senior management
commitment toinformation management
MI team addressingcomplex business
challenges.Team given time todevelop predictive
analytics
Role and responsibilitiesfor logical and physical
disposal are defined
Process
Lifecycle begins and isrecorded.
Attributes are known.Importance and criticality
are known
Appropriate controls,automation and
documentation of theETL process
Change and riskmanagement processes.
DR plan.EUC solutionsare controlled
High level of automation.Business processes
challenged by reports.Devolved reporting
(self serve)
Data is date stamped withan agreed retention
period.Automated data retention
processes
Governance Data is timely, adequate, relevant and not excessive
Logical and physical datasecurity is embeddedthroughout process
Enterprise managementembedded in strategy.
Data Protection Act
Centre of excellence for allMI reporting and analysis.
Fast adoption of processimprovement
Compliance with ADISAfor asset disposal
QualityCompleteness andaccuracy checks.Overall quality is
identified
Reconciliations areperformed
Transactional processes inplace. Regular onlinebackups/mirroring
Volume of reports areconstantly challenged.Reporting is suitable
to audience
Useful data is summarised
Capture Transform Store MI/BI andanalytics Dispose