As executives strive to meet profitability targets in this new world, they face a constant drumbeat to reduce costs across the enterprise. In a recent survey of data management professionals working in capital markets, Accenture and Greenwich Associates found that the costs and impacts associated with poor data quality are underestimated at best and sometimes ignored entirely.
Make no mistake: Data quality issues are affecting the top and bottom lines of firms across the industry. Pre-crisis underinvestment in data quality now poses a threat to post-crisis control, costs, and future growth and expansion. High capital ratios, changes in risk-weighted asset levels and increasingly stringent regulatory reporting rules are profoundly impacting business fundamentals. Meanwhile, bad data quality is limiting the ability of banks to act decisively and respond effectively.
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Sources: Accenture Research, Burton-Taylor International Consulting, LLC
of firms indicate data quality affects costs
actively measure the cost of bad data
understand the problem but cannot quantify the cost
Give your CDO the authority and tools to tackle the problem.
Source: Accenture Research, Greenwich Associates
Results from our recent study suggest that data quality is a much larger issue than you might think.1 Although 70 percent of firms cite data quality as their biggest day-to-day issue, barely 11 percent track or measure the cost of bad data. In other words, most firms have no way of knowing the true impact of bad data on their business performance.
There are many examples of enterprise-wide data initiatives being abandoned due to cost or insufficient benefit. Throwing money at the problem has proved unfruitful, yet more than a third of survey respondents are still projecting increases in their data and processing costs. There seems to be a considerable gap between data quality issues and potential solutions. It’s time to stop grasping at straws. No single initiative will solve the problem. A broad, holistic approach is the answer.
Many initiatives disappoint because they fail to truly comprehend the problem they are trying to solve. There remains a serious disconnect between the perceptions of business owners, or data users, and internal data solution providers. All too often, users are complaining about the consistency, completeness, structural integrity or functionality of the enterprise’s data solutions at the same time that internal data providers are claiming to have built a best-in-class solution.
Just 17 percent of firms that participated in our survey reportedly develop their data management strategies directly in response to the needs of the business. Many approach the challenge as simply an IT or operations issue, completely ignoring the underlying business needs. Others believe that data quality is a matter of governance, but like other issues in the investment banking industry, the problem runs much deeper. Data quality needs to be viewed as an enterprise-wide business challenge impacting all parts of the firm’s operations. Virtually all calculations performed by the firm, from capital allocation to regulatory reporting, must have a margin of error built in to account for bad data.
59% understand the problem but cannot quantify the cost
Source: Accenture Research, Greenwich Associates
A chief data officer (CDO) can be the key to success here, assuming the role is well defined. Virtually every bank has a CDO in place, but there seems to be no industry-wide consensus on what that role should entail, with some firms treating it as an IT function and others viewing it as highly theoretical (setting policy without veering into execution).
Specifically, the CDO should have control of the entire data lifecycle—one of the primary barriers to achieving efficiency across the organization. With those elements in place, the CDO can focus on:
Governance: Creating an effective control framework for defining and executing policy throughout the firm.
Business solutions: Identifying strategic and tactical tasks that will drive business value while improving data quality.
Rationalization and efficiency: Identifying and acting on opportunities to improve processes and reduce operational waste.
Innovation: Finding the hidden value in data that has been plagued by quality issues.
The first step a CDO should undertake is to create a strategy or approach that is practical, tactical and focused on business challenges. Forty-seven percent of firms in our survey find it difficult to deliver solutions that can adapt to the evolving requirements of their businesses, mainly because their data management strategies are not driven by business needs. Too often, we see a rush to action, with a focus on potential solutions: implement this EDM package, plug in this utility or define a firm-wide taxonomy. To be clear: none of these options is inherently bad, but they all fail to address the issue at hand.
Rather than trying to address data needs with a one-size-fits-all approach, careful consideration must be given to the challenges and best practices in each business vertical. Viewing data as an asset instead of a liability can help firms manage it appropriately and see new ways to extract its hidden value.
At this point, the CDO becomes instrumental in driving business priorities for data solutions and securing sponsorship across the firm. To achieve measurable business benefits, the enterprise must take concrete steps to increase data quality instead of embarking on ambitious programs that fail before completion or struggle to deliver the promised results.
One of the biggest obstacles to improving data quality is finding an effective way to measure it. Most often, firms estimate data quality based on the impact of data errors on their business (e.g., trading errors and regulatory fines). What they really need is an objective way to measure quality that fits within the capabilities of their organization.
Analytics can unlock that metric and shed light on how to improve data procedures. Firms can use analytics to understand which processes use which data, who purchases data and from whom, and where processing bottlenecks exist. Advanced analytics can be incorporated throughout the data management lifecycle to generate data-driven insights for advanced monitoring and running of core operations.
When confronted with a data quality challenge, the sheer volume of issues can be daunting— especially when data cleansing costs are fully quantified. The typical solution for repetitive and non-subjective tasks is low-cost business process outsourcing (BPO), but these jobs are prone to error, often require knowledge of in-house processes and/or systems, and are subject to processing peaks and troughs. Investment banks need a scalable, flexible and sustainable solution.
Robotic process automation has the potential to fundamentally change how the world and companies in it operate. Oxford University predicts that 35 percent or more of US jobs could be automated in the next 10 to 20 years.2 Organizations that have already adopted robotic process automation solutions are driving the change, using robotics technology to eliminate human error from key processes and reduce processing costs by up to 80 percent.
With the right support, CDOs can be data champions and catalysts. They can help resolve data quality issues by driving pragmatic and cost-effective change in a reference data program and using that success to build a culture of quality throughout the organization.
But, it all starts at the top of the house. Chief operating officers (COOs) at investment banks can begin to tackle the data challenge by asking themselves some key questions:
How much did bad data cost our firm last year? If an answer is not available and a reliable estimate is not even possible, it is time to take action.
Do we have a CDO to manage this issue? If not, the time is now.
Does our CDO have the tools and mandate to succeed? If not, what steps can we take to provide that support?
Let’s be clear: The journey to quality data is long, but the steps mentioned above can help ensure that firms are well positioned to reach that goal.
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