building a tangible roi for data...

Building a Tangible ROI for Data Quality

Harte-Hanks Trillium Software www.trilliumsoftware.com

Corporate Headquarters

+ 1 (978) 436-8900 [email protected]

How Do I Create a Data Quality ROI? Organizations are cost-conscious… nothing new there. Prior to spending

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

It is imperative to

understand the

relationship between data

and processes, and the

relationship between

those processes and

financial results.

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Copyright © 2007 Harte-Hanks Trillium Software All rights reserved of 14

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

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.

The first project will feed

your return on investment

which will drive

subsequent growth.

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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 well-

documented success to help elevate awareness among senior

management and decision makers who may ultimately be responsible

for assigning priorities and resources for future initiatives.

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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

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

Strategic data usually

serves in more than one

application throughout its

lifecycle.

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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.

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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.

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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.

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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 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.

Define Success 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.

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To illustrate this point with a very basic example:

− Metric: ‘Number of duplicates’,

− Timelines: Prior and post a pre-mailing 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.

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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.

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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.

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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: [email protected].

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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.

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