A Custom Technology Adoption Profile Commissioned by Trillium Software November, 2011 Introduction Enterprise organizations indicate that they place significant importance on data quality and make a strong effort, both financially and operationally, to ensure that data is supporting business process improvements. Today, there is movement toward investing and implementing data quality management technologies and best practices as the majority of companies believe their data quality management maturity is below average. This profile examines why data quality is critical to core business processes and functions in North American enterprises of 1,000 employees or more and reveals ways these organizations implement data quality improvements today and through what delivery mechanisms. This market demand is pushing companies to adopt data quality, MDM, and other complementary technologies for a variety of use cases. Strong Interest In Data Quality Impacted By Poor Business Process And Data Alignment According to Forrester Research s November 2010 Global Master Data Management Online Survey, 90% of North American enterprises are interested/considering, implementing/implemented, or expanding/upgrading data quality management technology in the next 12 months (see Figure 1). Business drivers for data quality implementation are plentiful and often differ based on industry and business sponsorship. No matter the industry, many organizations build their business case for data quality investments to increase revenue through improved direct marketing and account management, reduce costs through improvements to operational efficiencies, and mitigate and control regulatory and financial risk. Highly regulated industries, such as financial services, banking, insurance and healthcare, often use compliance and risk management as an entry point to their data quality strategies, while other commercial sectors, like manufacturing, retail, consumer packaged goods, and technology, look to improve business performance and efficiencies while reducing costs with better quality data.
Figure 1 Strong Interest In Use Of Data Quality Management Technologies What are your firm's plans to implement or expand its use of data quality management technology in the next 12 months? Interested/considering 35% Implementing/implemented Not interested 3% Expand/upgrade existing implementation 25% Piloting 4% Decreasing 1% Removing Don't know 2% Base: 188 MDM-experienced IT professionals Source: November 2010 Global Master Data Management Online Survey An omnipresent disconnect between trusted data (typically driven by IT organizations) and process transformation initiatives (typically driven by business leaders) is a symptom of the pervasive silos across business and IT stakeholders. The effective collaboration between business process and data management professionals is the key to success for both data quality and business process management (BPM) efforts. Business processes will break down if they don t use trusted data, and data quality initiatives will fail to deliver business value if the data doesn t support your organization s most critical business processes and decisions. In a commissioned survey conducted by Forrester Consulting on behalf of Trillium Software in November 2011, only 38% of respondents replied that their organization sees data quality as a bottleneck, or negatively impacting, their business processes, while 48% did not. This disconnect is often recognized by data management pros who often face an uphill battle attempting to educate business process pros about the synergies between data management and BPM initiatives. In fact, according to the November 2010 Global Master Data Management Online Survey, 28% of respondents indicated that their data management and BPM initiatives operate separately with minimal interaction, and another 18% did not know what the relationship between the two were, while only 26% of North American enterprises shared that BPM and data initiatives interact on a regular basis to coordinate development efforts and ensure data quality and team members work together daily to develop business solutions. Poor Data Quality Is A Recognized Bottle Neck For Business Process Improvements In November 2011, Trillium Software commissioned Forrester Consulting to look closer at the urgency of B2B and B2C enterprises to purchase data quality solutions and uncover how these organizations use data quality to improve their business process results and performance. The resulting survey of 53 North American enterprise business process and data management professionals shows that the level of importance of addressing data quality problems or investing in data quality improvements is extremely high, with 85% of respondents indicating it s an Page 2
important part of their overall business (see Figure 2). This feedback provides a strong indication that data quality initiatives have finally graduated from being nice-to-have tactical projects to a more strategic initiative with senior sponsorship and prioritization. With the high level of priority now being assigned to mitigate data quality issues, organizations next must address their low levels of data quality maturity. In Forrester s November 2010 Global Master Data Management Online Survey, only 13% of respondents consider their data quality maturity to be high, while 48% felt their DQ maturity was low (see Figure 3). Figure 2 Most Organizations Place High Importance On Data Quality Improvements On a scale of 1 to 5, what is the level of importance your organization places on addressing data quality problems or investing in data quality improvements? (5=very important, 1=not at all important) Very important 55% 4 3 11% 2 4% Not at all important 0% Base: 53 business and IT professionals familiar with their company s data quality efforts Source: A commissioned study conducted by Forrester Consulting on behalf of Trillium Software, November 2011 Page 3
Figure 3 Data Quality Maturity Not Where Most Organizations Require It How would you rate your organizations current level of data quality maturity (implementation of rules/technology to cleanse, standardize, validate, enrich, match, and merge data)? Very high (data quality shared services standardizing on single data quality platform for the enterprise. Multiple systems using same DQ rules) 2% High (standardize on one to two DQ platforms, with significant reuse and skilled DQ professionals) 11% Average (leverage multiple DQ tools implemented within system silos but have skilled and experienced resources available) 36% Low (no DQ tools standard; DQ logic primarily embedded in ETL workflows but with minimal focus on advanced DQ and no skilled DQ professionals on staff) 42% Very low (minimal to no focus on data quality issues within data management environment) 6% Don't know 3% Base: 188 MDM-experienced IT professionals Source: November 2010 Global Master Data Management Online Survey Data is not just collected as part of the business process, but it is also consumed and changed based on its ability to initiate a step in a business process or communicate actions taken at additional points in a process, ensuring the quality of data that cascades through a business process and across the organization is critical to optimize business processes, decisions, and customer interactions. So one of the greatest challenges organizations face is how to scope and prioritize their data quality investments and resources. In the commissioned survey for this research, we asked which core processes and functions were most impacted by poor data quality and found that 55% of respondents share that improving efficiencies within cross-functional operational processes were most dependent on quality data (see Figure 4). Importantly, the survey also demonstrated that front-office customer-facing business processes in the context of customer relationship management (CRM) and customer experience strategies were also very impactful at 45% and 40%, respectively. These customer-facing business priorities often include core processes, such as account management, customer service, and cross-channel self-service (e.g., Web, mobile apps, IVR, etc.). Interestingly, marketing processes while still somewhat impactful at were viewed in the survey as the least impactful relative to all others. This does not mean that marketing is not interested or concerned with data quality. In fact, direct marketing is one of the most popular and mature use cases for data quality improvements. Marketers have invested in data quality solutions for years to cleanse, dedupe, household, and enrich their customer data to reduce wasted marketing costs, provide a single view of the customer to create more targeted campaigns and promotions to improve campaign response rates, and identify more cross-sell and upsell opportunities. Since the functional processes listed in the survey are not mutually exclusive, it s likely that some respondents associated some strategic marketing activities with their CRM and customer experience responses while using the marketing response to focus more on direct marketing and other tactical efforts. This lower Page 4
marketing score may also be an indication that the business risk of a marketing campaign based on faulty data while potentially costly for that campaign is much less impactful across the enterprise relative to other process breakdowns, where financial, regulatory, and customer satisfaction issues can significantly impact the business as a whole. Figure 4 Operational, Customer Experience, And Finance Processes Most Impacted By Poor Data Quality For the following core processes and functions, on a scale of 1 to 5, what is the level of business impact introduced by poor data quality? (5=impactful to the business, 1=not at all impactful to the business) Operational processes 55% Customer experience Finance 45% 45% Customer relationship management (CRM) Product management Supply chain management (SCM) Procurement Payment processing Enterprise resource planning (ERP) Order management Marketing 40% 40% 36% Most impactful to the business (4 and 5 responses) Base: 53 business and IT professionals familiar with their company s data quality efforts (Graphic only showing responses 3 through 5 to show impact; responses 1 and 2 are hidden) Source: A commissioned study conducted by Forrester Consulting on behalf of Trillium Software, November 2011 Organizations Leverage Multiple Approaches To Mitigate Data Quality Issues Indentifying how poor quality data impacts your most critical core business processes is only step one. Once you ve scoped and prioritized the processes and supporting data that require data quality mitigation of some kind, the next challenge is to build multipronged people, process, and technology data management capabilities to address your business challenge. There are a number of approaches to consider: Align business process and data management initiatives. Business process transformation and optimization efforts require trusted data. Data management efforts cannot deliver business value if no one understands the context in which the data needs to be consumed and used by the business within business processes strategic, tactical and operational decision-making, and to support optimized customer interactions. However, based on the results of Forrester s MDM Survey in Q4 2010, only 16% of survey respondents Page 5
shared that their data management and BPM efforts were coordinated organizationally. A hopeful sign was that 17% shared that their BPM and data management initiatives were separate but do interact on a regular basis. The remaining 67%, however, clearly had no structured coordination between these efforts. Formalize an enterprise data governance program to define DQ standards and policies. No matter whether your data quality initiative is a tactical, project-based effort targeting DQ issues within a specific application, department of functional area, or a strategic cross-enterprise program supporting a complex environment, you need to define your data governance strategy. A data governance program defines the policies, business rules, and standards that must be embraced across your data life cycle (i.e., data capture, update, transformation, classification, consumption, and retirement). Bottom line: you can t improve the quality of your data if you can t get business stakeholders to define what quality means data governance is the collaborative forum to accomplish that. Define data stewardship roles, responsibilities, and processes to mitigate DQ issues. Data governance is the collaborative framework by which data quality efforts will be scoped and prioritized, business cases will be defined and validated, and organizational alignment will be achieved. But data governance cannot succeed without also defining and deploying data stewards across both the business and IT. Business stewards are line of business functional and geographic subject matter experts who can best articulate which of their core processes depend on what data. IT stewards often your enterprise architects (e.g., data architect, BI architect, integration architect, application architect) are best versed in how data is used to support crossfunctional processes and how they are shared across disparate applications and systems. The collaboration and workflow defined between these stewards will determine the ultimate success or failure of your DQ efforts. Deploy data quality cleansing and validation capabilities. Investing in data governance and stewardship best practices is an important enabler to achieving higher levels of data quality, but the data currently in your systems remains below par and needs to be addressed. In addition, once the data governance program defines business rules and standards to ensure better quality data ongoing a systematic solution must be put in place to automate and ensure compliance with these standards. That s where data quality software comes in. This category of data management software enables organizations to automate and implement batch and transactional data cleansing and validation capabilities to standardize, cleanse, validate, verify, match, merge, household, and enrich your most critical data based on the rules and standards you ve defined. Implement master data management capabilities. Where data quality software allows you to define and centralize the business rules to ensure high quality data, master data management (MDM) allows organizations to deploy either a physical or federated master data repository that can persist or virtualize a single, trusted view of customer, product, asset, financials, or any other master data domains. MDM takes data quality to the next level by enabling data stewards to directly mitigate issues, manage data hierarchies, and if necessary, override on an as needed basis exceptions to data standards and policies. In addition, the MDM hub acts as the synchronization layer to ensure this trusted view is available to any upstream operational or downstream analytic system that requires it. But you mustn t walk before you run. Data quality competencies are table stakes to deliver business value from MDM capabilities. Roll out data quality monitoring capabilities to identify DQ issues. Without measurement, there is no way to determine if sufficient business value and ROI have been delivered to justify the resource investments for data governance, stewardship, data quality, master data management, and process data alignment efforts. Three levels of measurement must be considered: 1) operational data quality metrics for business and IT Page 6
stewards identify improvements to data quality measures, such as completeness, uniqueness, compliance with standards, uniqueness, etc.; 2) business-value oriented key performance indicators (KPIs) for business sponsors quantify the monetary value delivered through cost saving, revenue increase, or risk mitigation by improved data quality at the functional process level (e.g., wasted direct marketing costs reduced by $100k per quarter by eliminating duplicate and undeliverable customer and address data); and 3) program level metrics for executive sponsors demonstrate qualitative feedback like enterprise engagement (e.g., the number of business units, projects, or applications that participate and how many departments have committed stewardship resources). When asking survey respondents which of the above methods were employed to address data quality problems within specific core processes, the responses demonstrated that organizations use different approaches depending on the use case. When focusing on DQ improvements within cross-functional operational processes, 54% focused on ensuring the alignment between BPM and data management efforts, while 48% implemented data governance, and 46% targeted data quality software and monitoring capabilities. Similarly, when targeting customer relationship management (CRM) efforts, BPM/data management alignment and data governance enablement were the top approaches at 39% and 37%, respectively but listed data quality cleansing capabilities at only 24%. This is likely due to the fact that many past CRM implementations already incorporate at least basic data quality validation and cleansing rules. In contrast, 53% of enterprise resource planning (ERP) efforts ranked implementation of data quality capabilities on top, with 44% of those respondents also prioritizing data governance, data stewardship, and MDM approaches (see Figure 5). Page 7
Figure 5 Mitigation Strategies For Poor Data Quality Often Differ Depending On Core Processes And Functions Impacted How is your organization addressing any identified data quality problems within the core processes and functions you previously mentioned? (Please select all that apply) Operational processes Customer relationship management (CRM) 48% 37% 54% 39% 46% 24% 40% 46% 42% N= 48 N= 41 Customer experience 37% 37% 24% N= 41 Product management 29% 40% N= 38 Finance 53% 42% 18% N= 38 Enterprise resource planning (ERP) 44% 35% 53% 44% 44% N= 34 Procurement 44% 27% 27% 24% N= 34 Payment processing 36% 19% 29% 16% 19% 19% N= 31 Order management 21% 28% 17% 28% 21% 28% N= 29 Supply chain management (SCM) 41% 33% 22% 19% N= 27 Marketing 22% 33% 22% 15% 15% 33% N= 27 Implementing an enterprise data governance program to def ine DQ standards, rules, and policies Aligning business process and data management initiatives Implementing data quality cleansing and validation capabilities Implementing master data management capabilities Implementing data quality monitoring capabilities to identif y DQ issues Implementing data stewardship roles, responsibilities, and processes to mitigate DQ issues Base: 53 business and IT professionals familiar with their company s data quality efforts Source: A commissioned study conducted by Forrester Consulting on behalf of Trillium Software, November 2011 Conclusion Data quality management, historically viewed as a tactical, IT-centric task, has finally emerged as a strategic organizational competency requiring effective collaboration between business and IT stakeholders. Business process management professionals increasingly recognize that their process transformation and optimization efforts face significant risk if they can t ensure that the core process improvements they re driving lacks a foundation in trusted, high-quality data. Unfortunately, business-ownership of data quality is a new concept for many organizations that previously outsourced this to IT. Data management professionals should assist in the necessary change management by educating their business partners on best practices, trends, and methodologies for building out data governance and data quality competencies in-house. Page 8
Methodology This Technology Adoption Profile was commissioned by Trillium Software. To create this profile, Forrester leveraged its Global Master Data Management Online Survey, Q4 2010 and Global Data Quality Online Survey, Q3 2010. Forrester Consulting supplemented this data with custom survey questions asked of 53North American business and data decision-makers with responsibility for data quality at enterprises with 1,000 or more employees that view data quality as a bottleneck or negatively impacting their business processes. Survey questions were related to the importance of data quality, the core processes or functions impacted, and how organizations are addressing identified data quality problems. The auxiliary survey was conducted in November 2011. For more information on Forrester s data panel and Tech Industry Consulting services, visit www.forrester.com. About Forrester Consulting Forrester Consulting provides independent and objective research-based consulting to help leaders succeed in their organizations. Ranging in scope from a short strategy session to custom projects, Forrester s Consulting services connect you directly with research analysts who apply expert insight to your specific business challenges. For more information, visit www.forrester.com/consulting. 2011, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. For additional information, go to www.forrester.com. [1-HOBYOL] Page 9