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Financial Data Quality Management

Financial Data Quality Management – It’s never too big a problem

Many firms identify their financial data quality management as an issue, in that they either want to improve it or get a better understanding of it – but too often firms don’t know where to start. Some believe it to be the kind of problem they can’t get their arms around, so they prioritize other projects. In this piece we’ll show you that not only is it not too big a problem, but also one worth addressing before you embark on any transformation projects.

Accelerating return on investment for transformation, digitization and automation programs

Knowing what data to fix first or what’s fatally flawed and requires a rethink makes a material difference when time to value or outright return on investment are key to the success of a change program, such as business transformation programs supported by DXC.

Identifying which data is your weakest link, which data will expose you to compliance or policy issues, and what data elements are most critical and need fixing as part of your shift to a new operating model, are relatively swift checks to undertake. This allows you to drive financial data quality management change in the right direction. Knowing what data will outright no longer work in the desired ‘to-be’ operating environment is also critical to avoiding hold-ups mid-project.

If a 4-6 week data quality assessment at the outset of a project can avoid delays and overruns of 4-6 months during a project, then that is time well spent. In addition to ongoing financial data quality management activities for continuous improvement and addressing the decay of data at rest in your systems, putting a focus on your data’s fitness for purpose into the future is a valuable foresight.

Removing the “your mess for less” conundrum in outsourcing, cloud migration and ‘as-a-service’ adoption

If you’re looking to make a transformation for your business, it’s best to optimize processes along the way rather than simply make inefficient processes cheaper. It simply doesn’t make sense to carry vulnerabilities into your new environment.

If you set out to leapfrog the limitations of your legacy systems, or remove the overhead of costly business infrastructure or processes that aren’t core to your competitive differentiation, having duff data running through the new set-up undermines the impact of your newly achieved target operating model. Yes, you’ll be paying less, and that’s always a major objective and a worthy achievement in and of itself. However, to reap the full benefits of your new found flexibility, speed and transparency requires that the quality of your data improves in step with the excellence of your capabilities.

The same logic applies if you’re seeking to create a technology environment where innovation can be operationalized fast.  So, it’s valuable to know where you should focus your clean-up initiatives and also have the financial data quality management tools to address your required quality improvements.

Financial Data Quality Management Breathes life into CDO data strategies

The role of the CDO is maturing and becoming ever more impactful for business performance. Whether the CDO strategy is centred on group-wide data standardization, competing through data science, or reputation management through data governance for compliance, having insight and control over financial data quality management makes it possible for those things to happen meaningfully. I.e., to become operational.

A focus on data quality also provides a data-driven or data-centric cultural boost. Cultural change is at least as important as good technology in achieving successful transformation or in adopting a data-driven approach to doing business. Data quality plays a key role in the cultural confidence, acceptance and adoption of such initiatives. A cultural commonality across data science and data quality is the ever-increasing involvement of artificial intelligence (AI). AI is maturing to a point that a number of approaches are fit for inclusion in data management solutions. While AI is a key factor in data science, its adoption can be hindered if poor data undermines the processes to which it is being applied.

CDOs and heads of data science can be undermined in trying to level-up an organization’s approach towards the importance of data as an asset if, for example, after the adoption of stewardship, governance and data ethics policies, people still have to remediate data before it is fit for use. Staff with a tenure of 5 years or so at a firm will sometimes suffer from ‘Heard it all before’ syndrome. This can create doubt and cynicism around ambitions to monetize data or become competitive through analytics. A focus on activities that identify and achieve real improvements in financial data quality management have a material impact on the belief in data-related change and the possibility to improve the way every-day processes are undertaken.

Reducing operational risk and the cost of operations with financial data quality management

Simply put, improved financial data quality management reduces errors and their consequences, and it reduces downtime for breaks in processes and the associated cost of inefficiency. The first step is to know where you stand with your data. To make a permanent difference to risk and efficiency, continuous monitoring and data quality improvement is essential. Improvement over time is the way forward, particularly with data types identified as critical data elements (CDE). Get these right and core business processes will run efficiently.

Managing data quality is valuable in preparation for change, and also in support of your current landscape, to get the most value out of it. And maintaining a target level of data quality is easier than fixing it periodically.

Talk to our experts about how best to address you data quality to help achieve your objectives: Contact us

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