Data Risk Services

The legislation of recent years has put data governance firmly in the spotlight with Sarbanes-Oxley, Solvency II, Basel III, sanctions screening, data protection and more, driving regulators’ focus on the data management process and associated controls.

In this context data governance defines the policies and identifies the people who govern the retention and disposition of corporate information. It plays a key part in unlocking the value of data, and is vital in successfully implementing data programmes.

Governance is made up of four enabling tools: processes, policies, reports and organisational structure. When successfully designed and implemented, these tools can enable the organisation to realise the value from data.

Deloitte’s capability extends from assessment and benchmarking against industry standards, through to the design and implementation of tailored data governance frameworks according to each organisation’s specific characteristics and needs. We can provide specialists to lead data governance programmes, helping you to quickly demonstrate business value, whilst also guiding and training on how to govern data effectively once we step back.

As organisations rely heavily on the information produced by their systems, assurance of data quality is critical. The quality of data in an organisation can be degraded due to multiple sets of the same information across divisions, poor data import, poor control over data acquisition and data transfers during mergers and acquisitions, etc. This can potentially lead to reputational damage, poor decision making caused by incorrect information, wasted investment, errors in financial statements and compliance issues. Improved data quality can provide faster and more accurate management information reports, greater customer insights and facilitate regulatory compliance so that organisations can confidently place higher reliance on their data accuracy.

Data quality is a key component of Deloitte’s Information Management framework. From identifying point-in-time data quality issues, through to the full development of a data quality sustainment cycle, our end-to-end approach is structured into three main concepts:

• Build programme foundation: including stakeholder sponsorship; data quality committees and their terms of reference; data definitions.
• Enhance data quality: including data quality profiling; root-cause analysis and impact; risk qualification; prioritisation of remediation activities; roadmap design.
• Data quality sustainment cycle: including process implementation and efficiency; awareness and training; continuous monitoring and reporting framework; upgrade and enhancements.

Data Analytics focuses on using raw data to make inferences based on existing known relationships or hypotheses. The inverse of this is data mining, which involves heavy duty processing of raw data to discover previously unknown relationships and patterns within the data which can then be interpreted to gain greater insight of a given topic. The effective use of these techniques will increasingly separate the market leaders from the laggards, as those that get it drive value from their data to manage their risks and costs, and improve their competitive position in the marketplace. Although Data Analytics isn’t a new concept, it is often applied only in targeted areas of the business. To enable your organisation to make decisions on fact-based analysis and scientific thinking, you need to push Data Analytics capabilities deeper into the organisation – from the C-suite to the front lines.

That takes a new level of focus and dexterity from organisations that are already grappling with plenty of other challenges. With effective implementation, you can:

• Enhance business performance and outcomes
• Strengthen innovation and competitive advantage
• Increase efficiency and lower costs
• Discover hidden insights
• Improve compliance
• Lower costs.

Successful business analytics requires three powerful engines: deep sector knowledge, broad functional capabilities and a high degree of technical sophistication. Deloitte brings a big-picture approach, combining each of these strengths to provide unmatched services.

Unless an organisation is already using leading edge systems with integrated data, there will inevitably be a need to migrate and integrate data as new systems are implemented and old ones mothballed. During this process it’s almost certain data will need to be migrated – and odds are, there will be problems, leading to delays, cost overruns and substandard data in your new ERP, CRM, Data Warehouse or Billing system. Net result: the return on investment suddenly looks a lot less attractive.

In successful migrations, the project team recognises from the outset that migration is far more than a technical ‘lift and shift’ exercise. As critical business data – customers, finance, assets and so on – is being moved and altered, it requires strong business direction, suitable control mechanisms, testing and business sign-off. Data conversion integration can be a complex undertaking that is often underestimated and executed poorly. Deloitte can help you deliver successful data migrations, helping you manage and mitigate the associated risks and implement the governance required, by providing assurance over the processes and controls you have in place.