Building a Tangible ROI for Data Quality

Transcription

1 Building a Tangible ROI for Data Quality Harte-Hanks Trillium Software Corporate Headquarters + 1 (978) trilinfo@trilliumsoftware.com

2 How Do I Create a Data Quality ROI? Organizations are cost-conscious nothing new there. Prior to spending It is imperative to understand the relationship between data and processes, and the relationship between those processes and financial results. money on data quality improvements, whether this be adding staff resources, investing in technology, or changing existing processes or workflow, senior management demands a business case that demonstrates the value such efforts will introduce to the organization. Though it is easily assumed that better data will benefit the organization, putting numbers around that benefit often slows the investment down significantly. Organizations that have already addressed data quality improvements in some way within their enterprise often face delays in investing further in data quality initiatives because a knowledge gap exists in the actual value it provides their organization. While many soft benefits can be attributed to better data quality, it is also true that organizations with mature data quality initiatives in place have quantified benefits that have been reflected on their organizations top and bottom line. You may or may not be required to produce a business case or submit some sort of cost justification for a data quality initiative. However, by quantifying the impact of data quality processing, in a methodical way, you will measure the impact of your efforts and the value you are providing to your organization and will establish a tangible return on investment. Later, this ROI may be useful to drive future investments and further promotion of data quality within your organization. Data Quality Metrics: The Short Answer Unfortunately, there is no short answer to the question, What data quality metrics should I be tracking? or Where do I find an ROI for my data quality efforts? Each industry, each project, each organization has different goals, business metrics, and considerations that may impact a resulting return on investment. Fortunately, there is a reasonable process through which a return on investment can be defined and tracked. Organizations that have developed ROI practices associated with their data quality initiatives have

3 paved the wave with demonstrable, repeatable results, most of which have exceeded their initial expectations. The key concept for building a business case or ROI is to understand the relationship between your data and the business process(es) it supports, and then further, the relationship between those processes and business results. When you boil it down, there are three primary areas for business impact: increasing revenue by growing the business in some way, saving money by reducing costs, or reducing risks and meeting regulatory driven compliance measures.. Included at the end of this paper is a list of projects where some organizations have been able to realize a return on their investments, to help generate ideas about where to look within your own organization. Funding Enterprise Data Quality and Ongoing Governance Organizations thinking about Master Data Management and Data The first project will feed your return on investment which will drive subsequent growth. Governance strategies already understand the enterprise concerns that accompany such large initiatives. Data quality efforts likewise, cannot exist within a project vacuum, and must support short-term business goals while delivering quick win results in order to provide true value to the business. Thus, most enterprise data quality efforts start out as a single project or focused effort within a single application (albeit an enterprise application at times) with the understanding that the solution must grow and extend over time to support multiple applications, service oriented architectures, multiple processes, and eventually, a culture shift that permeates an organization so that data quality concerns are embedded within every new project or systems effort. The first project will feed return on investment which will drive subsequent growth. Through harnessing some significant baseline statistics during the first project, you create the opportunity to develop a business case with a proven ROI, to use at some point in the future. Process to Quantify the Impact of Data Quality The key to establishing a quantified ROI for data quality efforts is to understand the relationship between data and processes, and the relationship between those processes and financial results. The value of particular data is tied to its application and use in a business environment. All rights reserved Page 3 of 14

4 As an example, data is tied to defined business processes, i.e. new order entries must contain name, address, credit card info etc. When the actual information acquired does not meet the needs the data is expected to fulfill, then operations and downstream analysis are negatively impacted and there is a cost associated with those challenges, i.e when invalid addresses are input to an application, the business may not be able to ship to the address, bill to the address, or in more complex terms, understand which regions are purchasing products. These challenges have hard costs associated with them. Beyond establishing and understanding of the relationship between data and business processes, the next matter requires defining data metrics that can be related to business metrics (operational and financial) and then finally, executing a disciplined process to collect the necessary numbers to quantify your business case. Process Overview The basic steps for developing a tangible ROI for a data quality program are: 1. Define a well-scoped project or proof of value as the first initiative. Choose the initial project so that you can deliver measurable business returns with minimal infrastructure investments to create a maximized ROI. 2. Build a business case for the initial investment. Define data quality metrics and relate these to business initiatives. 3. Capture a documented baseline and measure improvements as a result of implementing the data quality process for the defined time period. Calculate return based on metrics and the business impact previously defined. 4. Use the demonstrated ROI as a tangible benefit to drive further investment in infrastructure and resource allocation. 5. Market your success internally. Create visibility of your welldocumented success to help elevate awareness among senior management and decision makers who may ultimately be responsible for assigning priorities and resources for future initiatives. All rights reserved Page 4 of 14

5 Start with Smart Scope The scope of the initial effort should be targeted at business projects that have readily definable pain points, are recognized within the organization as processes or business applications that are in need of improvement or alteration, and lastly are within the an area of your control. Gain support of the business community by delivering value and demonstrate what is possible on a larger scale, if supported by additional infrastructure, resources, and process changes. When scoping, consider timelines, data scope, and staff investments. Make sure you have a thorough understanding of the true condition of the data prior to kicking off improvement efforts and focus your time and resources working on areas that the business most cares about. Fuel longer term solutions and infrastructure investments with the results of initial projects or proofs of value. If possible, invest in infrastructure incrementally, to spread the costs across multiple projects and thus improve returns for any single project. Relate the Financial Impact of Data Quality to Business Initiatives Prior to securing funding for any major initiative, it is essential to Strategic data usually serves in more than one application throughout its lifecycle. understand the impact of data quality efforts upon the business. While this may seem like a daunting task, there are a few steps that can dramatically simplify the process. To understand the financial impact of data quality upon business initiatives, work with both line of business management and the accounting or finance department. Both of these groups have detailed knowledge about how the business is measured and where money is spent. Each can help you understand what metrics currently exist that you can leverage to exploit the potential impact of data quality, for example: average campaign response lift, quarterly cost of third party data appends, average call time at call center, total amount of bad debt per quarter. These metrics can be measured for specific time frames and compared for before-and-after-data-quality-process cost savings or return on investment. To establish these associations, trace either anticipated areas of data improvement or established data quality technologies/processes to the systems and applications where that information is used. Keep in mind, strategic data usually serves in more than one application throughout its lifecycle, and you may find yourself talking to multiple groups about the All rights reserved Page 5 of 14

6 same data as it moves throughout your organizations and serves multiple purposes. Work with the users of those systems to detail the business functions they perform and their specific reliance upon the data. End-user management can further help you understand the costs associated with their functions. You can then establish a relationship between specific business functions and required data. Thus, you can quantify the financial impact of the data quality challenges that you uncover, address, and improve as part of your data quality initiative. Defining data quality metrics Data discovery and profiling are useful tools to perform some preliminary data diagnostics and are helpful in identifying specific areas which may need improvement. It is almost always beneficial to display the quality and condition of the data to business managers, who quite often have no idea the state or quality of the information they rely upon. This kind of insight may provide you with the business champion you may need to secure future funds. Data quality metrics can be simple metrics that look only at a single column, often times a column output from a data quality process that includes auditing capabilities. Alternatively, they may be more complex and require measurement across a number of different data elements. Likewise some may include logic or incorporate a filter mechanism (a.k.a. where clause ). Figure 1 on the following page includes examples of each of these types of metrics. All rights reserved Page 6 of 14

7 Figure 1 Metrics may include measurements of both high-level data centric business rules and specific rules that apply to a particular system, application, or data subject area (across multiple systems). Data quality metrics give you the building blocks by which it is possible to measure business impact. By themselves, they offer a less compelling story. However, these metrics represent undeniable, data-driven facts. Relate data quality metrics to business initiatives Business initiatives are generally associated with costs and revenues. Traditionally data quality initiatives have only been associated with costs, but recent studies have clearly demonstrated enormous costs savings and revenue enhancements that have been realized with a well thought out and executed data quality program. To develop an understanding of the financial impact of data quality on your organization, you must relate it to business functions. All rights reserved Page 7 of 14

8 Figure 2 Measuring Business Impact In order to inarguably demonstrate positive business impact, it is highly recommended to step through a formal measurement process. This does not mean that the process has to be complex, but it should include several key steps: Create a clear definition of metrics and the relationship to business impact Produce a baseline Use the same metrics at pre-determined intervals or milestones to measure change from baseline Sustaining positive influence over time through ongoing monitoring A formal process for measurement is necessary in order to demonstrate tangible business benefits. As discussed earlier, define data quality metrics and work with the business to link the metrics to measurable business results or other metrics in use. All rights reserved Page 8 of 14

9 Having defined specific metrics to use to quantify business impact, document a baseline against which you can measure improvement over time. Establish clear parameters Draw clear parameters around the data you are measuring. This creates the scope that can be measured repeatedly and therefore adds validity to your measurement. Pre-define specific timeframes for measurement; this may be in terms of days, weeks, months, quarters, etc. Basically, your formal plan for measurement includes an understanding of what you are measuring (metrics), when measurement will occur (milestones), and why (relationship to business/ impact on the business). Proactively Communicate Once you have a formal plan for measurement, communicate the plan back to the line of business (or other) sponsors as well as finance. There may be some necessary iteration based on their feedback as well as feasibility of future measurement. Working collaboratively with the business units and finance will strengthen your approach and will help you gain credibility within the organization regarding your ultimate goals and measurements. Document Baseline Define Success Just prior to implementing and appropriately tuning your data quality processes, take baseline measurements, or make a record of available metrics (metrics, any calculations, and the values measured for each) to which you will compare your post-cleansing evaluations. Post-cleansing may be captured directly after a batch process or after a stated period of time during which incremental cleansing occurs. It has proven very helpful to define success thresholds for metrics upfront. This gives the project team a target goal that can be achieved, and thus efforts deemed a success. Without a specific target, data may be improved, but it is difficult to determine whether it was improved ENOUGH. All rights reserved Page 9 of 14

10 To illustrate this point with a very basic example: Metric: Number of duplicates, Timelines: Prior and post a pr ing data quality cleansing exercise Business impact: Number of duplicates relates to Cost of Returned mail for given campaign Success threshold: Reduce Number of Duplicates by 15% Measure Improvements from Baseline Capture improvements using the same metrics, data scope, and communicated measurement strategy. This may be a one-time operation or something that is measured monthly, weekly, daily, etc., depending on your objectives. Drive Future Investment with ROI If this is the first time you have calculated a Return on Investment within your current organization, sit down with your Controller and have a discussion to hear their views regarding what costs they expect to be included in your calculations. There are nuances about how costs are absorbed within different organizations, and speaking first with finance/accounting resources can be an extremely useful tactic to reduce iterations surrounding your justifications. Once you know what cost expectations exist for inclusion, these costs are generally easy to track down and factor easily into the equation. You have the numbers you need to develop a formal business case or ROI. Having worked with the business to understand how data impacts their functions, you have a common understanding of how your established data quality metrics financially impact the business and relate to their business metrics. Building out these ROI numbers will help you demonstrate the true value of your data quality efforts to the organization, and will provide unquestionable evidence as you request future support. By structuring your data quality investments so that you deliver value during the initial project, you create the opportunity to drive further investments in the future with support from the business. All rights reserved Page 10 of 14

11 Add in more content about using past ROI to drive future projects and invest in infrastructure over time. As mentioned earlier, wherever possible, invest in infrastructure incrementally and spread the costs across multiple projects to improve returns, especially for initial efforts. Communicate Successes Build upon your success and increase awareness within your organization by championing your value. As much as you feel that you have already communicated with everyone, chances are, they either did not hear you the first time, or they simply do not remember. If you are trying to grow your solution or department or efforts to provide additional value, you must continue to remind people of the demonstrated value that you have delivered. If in fact, you have delivered value, securing sponsorship for your next steps will not be difficult. Technology Facilitates Process Technology can assist the ongoing process of measuring and communicating data quality metrics as part of projects and as part of data governance initiatives. Data Discovery and profiling tools are especially helpful and can be used throughout the process. Firstly, they are useful in performing perfunctory risk analysis. Prior to embarking upon a data quality improvement project, understand the current state of the data in order to ensure that you can be successful. This requires data assessment, sometimes across multiple sources. These results need be shared with the business community to understand their priorities and set direction. Data discovery tools optimized for business collaboration are most efficient because they allow business users to directly understand the current state of the data. The business can then easily and knowledgably direct which data anomalies are truly problematic and require resolution, and which data anomalies are not show stoppers and therefore a lower priority. Further use your data discovery tool for measurement and metric management: define specific business rules and data metrics within the tool, compare the same data scope (e.g., file, system extract, filtered dataset, etc.) pre and post cleansing, and manage auditable data snapshots and metric results. All rights reserved Page 11 of 14

12 Data Discovery tools most easily allow a business user to evaluate the differences between data sets over time and across systems. This quantifies the lift you are able to achieve through data quality cleansing, and against your data quality metrics. This is the simplest way to get before and after Actuals that you can later use to substantiate your data quality return on investment or business case. As your initiative grows, utilizing tools and a repeatable process to manage all the information you must collect and be able to call upon to substantiate your business case results will allow you to scale more quickly over time. Where Can I Look for Returns? Organizations have found eye opening returns related to data quality efforts in a number of areas. Though each organization is unique, below are some areas to investigate for ROI returns, to help you start your process. Procurement cost avoidance Supply chain optimization Customer experience and loyalty Revenue assurance Productivity gains Sales and marketing effectiveness Reduced employee turnover Compliance & risk management The chart on the following page, Figure 3, enumerates business challenges where data quality has been specifically related to results within the Trillium Software customer base. All rights reserved Page 12 of 14

13 Figure 3 This may be a helpful tool to guide you where to look as you begin to qualify and quantify your own data quality ROI. For further information about developing a data quality ROI, feel free to contact Sarah Kohler at Trillium Software: sarah_kohler@trilliumsoftware.com. All rights reserved Page 13 of 14

14 About Trillium Software Harte-Hanks Trillium Software has been selected by companies worldwide, both large and small, to improve their operational and analytic business decisions through accurate and timely information. Trillium Software offers an integrated suite of Total Data Quality software and services architected to discover and correct today s data quality problems and establish a platform prepared for tomorrow s yet unknown data challenges. The Trillium Software System is recognized as critical to the success of customer relationship management, master data management, customer data integration, data warehouse, business intelligence, enterprise resource planning, supply chain management, e-business, and other enterprise applications, and data integration, data migration, data stewardship, and data governance initiatives. The Trillium Software System is comprised of: TS Discovery provides data investigation, discovery, and profiling in support of quality, migration, integration, stewardship, and governance initiatives. Fully integrated with TS Quality and TS Insight, TS Discovery includes a broad range of features that expand upon basic profiling, including automated monitoring, business-rule validation, and trend analysis. TS Quality provides cleansing, standardization, matching, address validation, location enrichment, and linking functions for global customer data and operational business data, in any and all systems and applications. Regardless of data source or structure, TS Quality ensures that data adheres to established standards that are adaptable to fit each organization s specific needs. Both single- and double-byte data are processed in local languages to provide a unique and centralized view of customers, products and services. TS Enrichment provides additional data enhancement services to complement, supplement, and amplify data available in-house. Choose from over 5000 third-party data sources, and administer enrichment through a single vendor. TS Insight provides data quality dashboards, scorecards, and trending reports and analysis through a web browser based solution. Users log on to their customized homepage and immediately access a graphical view of data quality results, monitored over time. Usage Notice Permission to use this document is granted, provided that: (1) The copyright notice 2007 by Harte-Hanks Trillium Software, appears in all copies, along with this permission notice. (2) Use of this document is only for informational and noncommercial or personal use and does not include copying or posting the document on any network computer or broadcasting the document through any medium. (3) The document is not modified from the original version. It is illegal to reproduce, distribute, or broadcast this document in any context without express written permission from Trillium Software. Use for any other purpose is expressly prohibited by law, and may result in severe civil and criminal penalties. Violators will be prosecuted to the maximum extent possible. This document and related graphics might include technical inaccuracies or typographical errors and is subject to change at any time by Trillium Software. Trillium Software does not guarantee the suitability of the information contained in this document, which is provided "as is" without warranty of any kind. Trillium Software hereby disclaims all warranties and conditions with regard to this information, including warranties and conditions of merchantability, whether express, implied, or statutory, fitness for a particular purpose, title and noninfringement. In no event shall Trillium Software. and/or its respective suppliers be liable for any special, indirect, or consequential damages or any damages whatsoever resulting from loss of use, data, or profits, whether in an action of contract, negligence, or other tortious action, arising out of or in connection with the use or performance of information available from this white paper. All rights reserved Page 14 of 14