Across industries, regulatory pressures and integration initiatives are making it more and more difficult for executives to avoid accepting the consequences of poor data on business decision making - especially as data-driven issues in governance, risk and compliance challenges become more critical to performance.
That may not be news to those of us who have been in the industry trenches for some time, encouraging organizations to recognize that good data quality practices are critical to driving long-term risk-adjusted increases in the growth of corporate and shareholder value.
However, the harsh reality for most companies today is that merely "paying better attention" to managing data - as much as that represents a laudable departure from past practices - simply isn't enough. Why? Because addressing data quality isn't just about crowning your organization's first data quality czar or authorizing additional budget funds - on a reactive basis - when suboptimal data quality threatens an operational or decision-support initiative.
Today, addressing data management in a meaningful way is about 1) linking good data management practices to measurable business outcomes, and 2) making sure the outcomes that you're committing the most attention, time and investment to are the outcomes most important to your organization (i.e., your strategic business objectives).
While that may seem like a straightforward notion, the reality is that few companies are in a position to build a well-aligned, properly funded and comprehensively designed data quality program from the bottom up - in one sweep - particularly on a globe-spanning enterprise scale.
Frankly put - especially to executives seeking actionable guidance - two implications are immediately apparent.
The First Implication: Reach for One Milestone at a time
The first implication is that from a pragmatic perspective, for any established organization burdened with legacy infrastructure (and that describes most large companies today), getting and maintaining the appropriate level of data quality requires a staged approach - a careful sequencing of investment and implementation, consistently applied (depending on the organization) on a quarter-to-quarter, week-to-week or even second-to-second basis.
For most companies, that's a tall order. Maintaining a sustained initiative requires simultaneously putting on the ground (and integrating) a number of critical elements - elements which, when absent, tend to rob data quality initiatives of their business value.
These key elements include:
- A clear vision - one that can be used to seed awareness about data quality at every level of the organization, as well as communicate shared objectives in data management to third-party alliances, business partners and suppliers who share responsibility for maintaining data quality across shared applications, systems and networks.
- A disciplined, governance-driven approach to the investment - one that allocates coordinated responsibilities for data management internally to both the IT organization and individual business units, and externally to third-party participants, while also building accountability into all aspects of the process.
- Prioritization processes - ones that align the initiative with the organization's primary business objectives and identify specific responsibilities and processes to adjust these as often as changes in the business environment warrant.
- A best practice-driven integration methodology - one that ensures that the people, process and technology components of a comprehensive data quality management solution are well balanced in order to deliver a clear return on investment in the short term and sustainable value over time.
- Management oversight and follow through - consistent board-sanctioned, executive-led follow through - on a continuous basis.
Because such an organized approach is rarely possible - especially given budget constraints, pressure to commit to other equally important corporate initiatives and internal challenges in building stakeholder consensus - data quality must be seen by executives in light of its impact on corporate performance under circumstances that are not always foreseeable, controllable or manageable.
It's about risk.
The Second Implication: Start Turning the Spotlight on Risk Mitigation
Managing data is about managing risk. And if we're talking about risk, we need to start talking about risk mitigation.
What you will soon begin to see more analysts start talking about - and executives start asking about - is what steps organizations should be taking to manage the business risks emerging from data-related issues when an optimal data quality capability is not fully anchored across the enterprise.
Mitigate the Risks
In order to understand what a pragmatic, risk-based approach to data quality entails in today's business environment, it can be helpful to look at data quality from three perspectives - each explored briefly in this article: 1) What are the trends that are driving corporate demand for data quality? 2) What are the issues in data quality from a risk management perspective? 3) What is the value proposition for executives engaging a risk-based approach?
Emerging Trends in Data Quality
Following are some of the more significant trends driving the demand for better data quality management capabilities.
- Regulatory changes are focusing management attention on data quality. Data quality is more likely than before to be recognized as an enabler for better reporting within the organization. Because accountability for performance is now personal, CEOs are also more likely to give data quality better prioritization and funding.
- Data governance, as a concept, is getting better traction. As more companies struggle with the complexities in their corporate governance, risk and compliance programs, data governance issues are coming up fast and hard and include accountability and ownership of data within the organization and the allocation of roles and responsibilities around data to the appropriate parties within the organization.
- Integration and data sharing initiatives are raising data quality issues. As companies maintain strategic initiatives, such as CRM and spend management, data quality becomes increasingly important, especially when problems such as data duplication and inaccuracies begin to lead to poor business decisions or failed system conversions. However, with costly mistakes also comes learning. As a result, for example, subsequent data-related initiatives are much more likely to have better senior executive support much earlier in the business justification, approval and planning stages.
- Executives are looking for greater transparency in areas beyond finance. One of the emerging trends is a move by companies to continue the forward momentum initiated by regulations such as Sarbanes-Oxley and seek greater operational efficiencies from the information supply chain. These actions are sometimes engaged in conjunction with a commitment to new technologies such as RFID or Web services, neither of which are worth the investment without a singular focus on the quality of the data that drives their value.
- However, even in industries where data quality has historically been viewed as a strategic enabler, executives have a long way to go. A PricewaterhouseCoopers/Gartner survey of financial service providers found that financial institutions have made strides toward addressing operational risk data management. However, much work remains to be done. Survey results revealed that 56 percent of respondents have limited standard terminology to guide their data collection efforts; nearly half of the respondents estimated their operational risk data to be only 50 percent accurate; and 40 percent of respondents could not measure the success of their data management initiatives.
Understanding Data Quality Management from a Risk Perspective
Two common areas that have a direct impact on a corporation's risk position are vendor and customer master data. In fact, just a five percent duplication rate in either one of these data pools can result in a revenue impact to an organization measured in the millions of dollars. Why? Both of these areas are integral components of the corporation's accounts receivable, accounts payable and CRM functions. Poor quality vendor and/or customer master data can increase the following risks:
- Financial risk. Duplicate payments, unapplied credits, etc. have implications on tax reporting and unbalanced accounts. Also, corporations can experience lost revenue due to overpayments and lost discounts due to lack of consistency on payment terms.
- Regulatory compliance and legal risk. Potential fraudulent activity can occur when there are issues with segregation of duties and roles related to access to vendor and customer data.
- Operational risk. Internal controls related to roles and responsibilities in the organization may compromise regulatory compliance requirements. Process inefficiencies may also result due to inaccurate and incomplete vendor and customer data including an increase in manual efforts for inquiry and reporting functions.
- Inaccurate management reporting. Lack of visibility to actual payment and sales information of a vendor or customer can have significant impact on negotiating vendor contracts or monitoring contract compliance.
- Impact on customer service and customer satisfaction. Many corporations have duplicate customer master records that do not provide customer support personnel with a complete picture of all the customer's business interactions. Many problems can occur, including duplicate mailings to customers, slow response time to problems and multiple telemarketing calls.
Identifying the Value Proposition for Executives
A risk-based approach to data management provides clear and compelling benefits across the executive suite. For CEOs focused on building trust with stakeholders, adhering to regulatory compliance requirements, increasing revenues and lowering operational costs, a risk-based approach can improve decision making and trusted reporting. For CFOs concerned about ensuring accurate financial and management reporting, a risk-based data quality program establishes the policies, procedures, controls and measurements necessary to improve accountability, reporting and performance (i.e., appropriate governance). For CIOs seeking to optimize information technology investments, a risk-based data management program improves application effectiveness, lowers infrastructure costs and, in some cases, may establish the ROI-based business justification that accelerates consensus and project approvals in short order.
Final Considerations: Helping Executives Ask the Right Questions
Following are a few comments to organizational managers who are either 1) accountable for the results of a data quality program, or 2) accountable for the business results of any business unit, initiative, system or application whose performance depends in whole or in part on good quality data (a far larger audience).
Don't allow the challenges in managing data to temper your organization's commitment to building the best data quality program possible within the constraints of your organization's resources and the requirements dictated by its business objectives. Deferral of this strategic priority increases both financial costs and business consequences.
Accept that, pragmatically speaking, while a fully aligned best-practice approach to data quality should be a compelling board-level conversation for any company, the difficulties inherent in establishing an enterprise-wide capability quickly will make such an approach a highly elusive achievement for all but a few.
As a result, it is essential that as you look to improve data quality management over time, you also work to ensure that data management in general and data quality in particular are understood at the board and senior executive levels in the context of business and IT related risks - risks that, if unmanaged, are capable of generating a material impact upon financial and operating performance.
This represents a short overview of some of the significant issues, emerging trends and strategic perspectives in data management, data quality and data-related risk management that I believe are critical for organizational leaders with either IT or business accountability to understand today.
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