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.

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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

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Data management maturityAre you ready for Big Data?

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