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Data Analytics

Single Customer View (SCV)

Each year a typical Consumer Business (CB) organisation will process hundreds of millions of customer transactions across business functions. This vast pool of data presents a huge opportunity for generating customer insight and improving customer experience.

With the priorities for CB organisations being customer acquisition, loyalty and retention, the exploitation of data through analytics should be at the heart of these businesses’ core competencies. However there is a marked difference between the ‘haves’ and ‘have-nots’ - where CB organisations that can effectively exploit customer data through the formation of a Single Customer View (SCV) are seeing a step change in their ability to execute their strategies and hence increase their profitability.

Why is it an issue?

  • Lack of alignment between organisational strategy and customer data strategy results in incorrect prioritisation of customer data captured, created and maintained
  • Complex system landscapes, and fragmentation of data across systems and channels prevent organisations from effectively managing and securing critical customer information
  • Lack of controls and governance over sensitive personal data sources exposing organisations to legal, regulatory and compliance risks
  • Establishing a solid framework that will ensure the quality of the data and identify the data owners for each information asset is vital for the accuracy of the insights driven by customer information

Service overview

  • Leveraging our leading Analytics practice, Deloitte take a holistic approach to SCV, we believe that people and process must underpin the technology
  • We work collaboratively with clients to understand the customer data landscape, build the vision and strategy for customer data, understand the benefits of a single customer view, and plan to deliver them

Our data services help clients to:

  • Chart the customer data landscape: identify and record all systems, business units and third parties that collect, handle or store customer data
  • Measure data quality: measure the suitability and quality of the data against requirements for a unified customer view and the desired insight to support current and future propositions
  • Understand how customer data is managed: review the management of customer data to establish how security and privacy are controlled whilst assessing the governance framework effectiveness
  • Develop a strategy around customer confidence in data: create a strategy to enable elevated customer confidence with respect to the use of their data.

Find out more about SCV


Data Governance

Human nature means people are sometimes resistant to change. Data Governance exists to counteract this traits and ensure that the people, processes and policies in place to manage data are effective. With increasing volumes of data being generated and handled, organisations face new challenges in protecting their information and deriving value from it. Achieving maturity in this area requires a long-term focus on building the key capabilities that enable accurate reporting, risk mitigation, customer analytics, and data integration.

Why is it an issue?

Without effective data governance, organisations face the following challenges:

  • Lack of trust and confidence in data, disabling decision-making
  • Inability to execute core business processes efficiently
  • Dysfunctional and non-trustworthy reporting
  • Poor legal and regulatory compliance
  • Lack of data traceability and transparency
  • Increased costs
  • Poor customer experience
  • Increased time spent validating and reconciling data

Service overview

Data Governance can be decomposed into organisational competency across eight key areas:

  • Vision – understanding the current state of Data Governance within the organisation, articulating the data strategy, and implementing a roadmap of transformational activities
  • Quality – ensuring that data is accurate, consistent, complete, timely, valid and supports sound decision-making
  • Ownership – definition and implementation of an organisational structure that will be empowered and accountable for managing and governing the organisation’s data
  • Policies – creating and enforcing policies and procedures that the organisation will adhere to whilst executing data processes
  • Processes – defining and managing the processes that ensure that data is properly entered and maintained
  • Standards - definition of common enterprise-wide entities, attributes and their inter-relationships as well as definition of records of authority for each data object and data element
  • Security – assuring logical and physical protection of data across the organisation from theft or accidental loss
  • Technology - selection of technology for the data repository, business process management, data integrity and governance monitoring, and the design and implementation of the system landscape for data.

Find out more about Data Governance


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