UK: Data Management Services: Addressing All Elements Of Your Data From The Roots Up.

Last Updated: 30 May 2007
Article by Deloitte Audit Group

Most Read Contributor in UK, August 2017

Corporate Liability Or Corporate Asset?

The Importance Of Data Management

"Ultimately, poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything."

Every organisation depends on reliable data. Managed well, it will drive revenues, reduce costs and mitigate risk. Managed poorly, it can lose customers, inflate costs and expose businesses to unbounded levels of risk.

Today’s ‘information economy’ expects data to support deeper performance insights, enhanced customer experiences and better decision making, but all too often the data is unreliable, unintelligible and out of control. The growth of information is not keeping pace with the growth of data.

Despite this, the demands that are made of data continue to rise – an explosion of data dependent regulatory obligations; mergers and acquisitions based on the promise of process and system synergies; the ‘always on’ customer service culture.

Deloitte’s Data Management team deliver a series of related services that can address the many challenges associated with data, ranging from specific spreadsheet reconciliations through to defining data governance structures. Our services are built on the recognition that each client situation is unique, requiring a different blend of skills, experience and approach to unlock the real value of data, no matter how the challenges may be presented.

Data management can help transform data from a corporate liability to a corporate asset.

The data challenge – why is it getting harder?

Corporate Growth

Expansion, mergers and acquisitions, restructuring


Basel II, SOX, privacy legislation


Proliferation of enterprise data systems such as CRM, ERP and BI

Data Diversity

Multitude of new formats, many lines of business

Data Decay

Data is volatile – customer data deteriorates by up to 25% per annum

Data Denial

Most organisations do not understand the scale, nature or location of data problems

Technical Drivers

New platforms, applications

Economic Drivers

Data is expected to deliver competitive advantage

Our Approach

Deloitte’s approach to data management has three dimensions that uniquely combine to deliver business value. It is founded on many years of experience assuring and advising technology implementations and the recognition that successful data management is much more than simply integrating technology.

Data Risk Management – providing data assurance in the form of investigations, reconciliations, reviews and assessments.

Data Governance – creating the policies and identifying the people who govern the retention and disposition of all corporate information to build the framework for a data-driven enterprise.

Data Management Technology – selecting, implementing, integrating and applying the technology required to ensure effective data management.

Though the focus of any individual data management initiative may be defined by a single dimension, success will require the integration of all three. At the heart of these dimensions is the interaction that defines a successful initiative – one that recognises that only by considering the risk, the governance and the technology will you be able to manage data successfully.

Our unique ‘three dimensional’ approach to delivering data management runs through all the services we provide. The services we offer to our clients are as varied as the challenges of data itself. The following list illustrates some of the broad categories of support we can provide but is by no means exhaustive.

Our services

Data Governance – managing the demands of regulatory compliance

Data governance defines the policies and identifies the people who govern the retention and disposition of corporate information. The legislation of recent years has put data governance firmly in the spotlight with Sarbanes-Oxley, sanctions screening, data protection and more, driving regulators’ focus on the data management process and associated controls.

Deloitte’s Data Management team includes regulation subject matter experts to help our clients understand both what data governance is expected and how this can be achieved.

Data Mining – making data intelligent

Data mining describes several techniques used to uncover the hidden information contained within ostensibly low value and voluminous data. Its applications are many and varied ranging from fraud detection to predicting customer spending patterns; from forensics accounting to security management.

The principal techniques used in the Data Management team include:

  • Data visualisation
  • Cluster/factor analysis
  • Propensity modelling
  • Decision trees
  • Artificial neural networks

Data Migration – 9 out of 10 data migrations fail

Whether you’re implementing new systems or improving old ones, your data will need to be migrated – and odds on, it will fail. Why? Because most organisations set out on this journey without a map or with a map that is wrong – the source data is not fully understood, manual analysis is time consuming and unreliable, complexity is underestimated and rework unpredictable. Systems do not join as expected; planning becomes guesswork.

The Data Management team brings together industry best practice and years of experience to help our clients be part of the 10% who succeed.

Data Quality – making data fit for purpose

Data exists only to support business applications. Whether for billing, customer management, financial reporting or any other purpose, each system will require views and attributes that may well be very different – such as a legal entity for the chart of accounts compared to the product divisional view for performance management; single product views for supply chain management or single customer views for CRM. Data quality can only be defined in the light of the purpose it is designed to satisfy. All data profiling, data cleansing and data matching activity needs to be driven by the ultimate purpose of the data.

The Data Management team uses bespoke and industry leading tools to help our clients understand their data and then, in the light of the difference between what it is and what it needs to be, specify and implement appropriate activities, technology and controls to make data fit for purpose.

Data Benchmarking – measuring performance

What is good performance? Benchmarking enables a team, a business, or an industry to understand performance, facilitate best practice discussion and therefore can lead to substantial efficiency improvements.

Managers are always looking to reduce cost and improve efficiency, but deciding when cost should be the focus as opposed to efficiency, is not as straightforward. Visibility on the performance of peer groups (even if un-named) allows the manager to determine whether cost issues exist and the extent of the problem. Whether benchmarking cost, quality, or timing, using simple ranking metrics or complex statistical algorithms, benchmarking can lead to company- or industry-wide discussion on cost improvement.

Desktop Decision Support – making sensible decisions

A vast amount of data is collected but very little of it is used to inform management decision making. This is often due to poor reporting design and implementation together with a lack of flexibility within ERP systems. Internal IT functions are not configured to address these reporting issues and the perceived effort required to engage external ERP vendors results in a grudging acceptance of the status quo.

Based on desktop applications such as MS Excel and MS Access, the Data Management team can bridge the gap between data and management by providing intuitive, interactive, and flexible tools that tap into the richness of what is available but not currently accessible.

Data Analysis – understand your data - understand your business

There are two distinct flavours to data analysis:

  • Diagnostic analysis is of an audit nature, where we are holding up a mirror to data and saying this is what it looks like. Typically it will be used to reconcile financial information and takes advantage of many of our proprietary tools.
  • Value added analysis sets out with a specific decision making purpose in mind such as predicting marketing responses or creating a classification of customers.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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