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Data Quality Essentials For any Data-Intensive Project

Summary: Projects that involve data: CRM projects, MDM initiatives, ERP implementations, Business Intelligence and data warehouse projects, data governance programs, migrations, consolidations, harmonizations… all offer the opportunity to improve data quality. This paper is a phase by phase guide that identifies for both business team members and IT resources, what tasks should be incorporated into a project plan for each team function, as relates to data quality. This provides a roadmap for optimal effectiveness and coordination. These data quality essentials are based upon best practices collected from experiences on thousands of data management projects and successes over the past 30 years.

Harte-Hanks Trillium Software www.trilliumsoftware.com

Corporate Headquarters

+ 1 (978) 436-8900 [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 data quality suite that delivers complete end-to-end data quality life cycle management. 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. Designed for collaboration and information sharing, the Trillium Software System lets businesses individually define what data quality means to their organizations. The Trillium Software System is comprised of:

TS Discovery - Collaborate across business and IT resources to assess large volumes of data within and across systems. Robust data profiling capabilities allow users to understand data domains, formats, patterns, and relationships as they exist within the data itself, as well as to see whether data conforms to specific business rules and defined data standards. Ongoing monitoring assesses data to ensure that high quality is maintained at all times. TS Quality - Cleanses, standardizes, and matches any data: name and address data; product data; asset, material, and location data; transactions; etc. World-class global capabilities and automated, rules-based intelligence give organizations a simple but complete solution to handle massive volumes of data—out of the box. Organizations can further customize rules and adapt to meet changing business needs. TS Enrichment - Complements, supplements, and amplifies data by drawing on over 5000 third-party sources. This service provides a fully automated process for appending third-party data seamlessly for storage and distribution. TS Insight - Monitor business rules and data quality metrics graphically through a customizable Data Quality Dashboard. Use scorecards and trending information to communicate data initiatives, results, and goals. TS Insight shows which data sources have data quality issues and which do not meet minimum corporate threshold and acceptability levels, helping you forecast and allocate the right resources to improve and optimize the business processes that impact them.

Usage Notice Permission to use this document is granted, provided that: (1) The copyright notice “©2008 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.

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Project Manager’s Guide to Data Quality

Incorporating an effective data quality solution into your project requires a number of additional activities throughout the lifecycle of your project plan. Some tasks are more suited for business user resources to take the lead on while other tasks are primarily technical activities.

This white paper introduces some of the techniques used by successful companies to plan and successfully implement data quality processes as part of an initiative. While technology greatly facilitates and automates data quality management, it should be applied in accordance with a measurable, objective methodology to assure success and a high ROI for the project. As you’ll see in the pages to follow, process, people, and business expertise are major components in achieving an improvement in data quality, leaving technology as a way to automate and improve processes.

This paper is aimed squarely at project managers and describes the step-by-step process for implementing data quality as part of a project. Specifically, this white paper highlights:

• The importance and ways to best involve business users in the project to ensure their needs are met

• Ways to accomplish a specific limited-scope project, while considering the big picture and the going concern of data quality within your organization

• How to incorporate technology throughout a project to expedite data quality initiatives

The processes are best outlined in the following chart:


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Most projects have six phases. They are:

1. Project Preparation – defining a team, business objectives and assessing the risks involved in project completion for realistic project planning.

2. Blueprint – writing the plan and detailed designs to meet project requirements, while mitigating the risks discovered in phase one

3. Implement – executing the action plan for establishing new processes and using new technologies.

4. Rollout Preparation – getting the organization ready for the new improvements

5. Go Live – transition time when the new processes and/or technologies are first being utilized and being ready to resolve any issues that arise

6. Maintain – tuning the processes and technologies and getting ready for the next project. The process is almost never a one-and-done, rather it is iterative

Each phase requires that both business people and technical resources work together to accomplish the project effectively and efficiently.


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Phase One: Project Preparation

In this phase, you will evaluate what resources and time are needed to execute the project and what issues, roadblocks and risks will need to be overcome. Start by setting up your team, defining the scope, expectations, and deliverables of the project, and conducting an analysis of the current state of your data.

Define Project Team and Roles Ultimately, data and its level of quality must be supported by many people in the company, not just IT. Involving subject matter experts from affected business areas is an absolutely essential step necessary for success because successful interpretation and treatment of data is derived from both proper ‘Syntax’ and ‘Context’.

Syntax— IT is generally very capable of conforming data to proper syntax with relative ease. An example of this might be that telephone numbers should all appear in the same format in a database.

Context— Business users are generally the best source of information regarding context, or the meaning behind the data. An example of this might be the repeated occurrence of an undocumented comment or code embedded in a name and address field.

Each of the types of team members has clearly defined roles for making the initiative a success and must be accountable for his/her part. Here is how roles and responsibilities are typically defined with regards to the data quality initiative elements of a project.

Role Responsibility

Executive Leaders (CIO, CFO, VP)

Publicly endorse the data quality initiative. Foster support. Secure funding for project. Resolve issues and remove roadblocks.

Line-Of-Business Managers

Champion the cause. Interface between IT and business. Work with the executive leaders to understand business objectives, remove barriers and political opposition, and influence political change and cooperation between lines of business. Articulate the data quality problem in terms of business value and affect change to business users.

Data Stewards Understand technology available to meet business objectives. Define what is possible and what is not. Develop a deep understanding of available data assets, usage, and issues. Drive specific requirements, provide feedback, participate in UAT activities.

Information Professionals

Implement business rules for cleansing, standardizing, and de-duplicating the data, support data stewards, run day-to-day operations.


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The right technology will help keep team members engaged and communicating. A data analysis environment with a central repository provides the right architecture for multi-role, multi-member projects where resources need to interact and communicate about source data and target environment designs.

This architecture provides an infrastructure that allows a common understanding about the issues around the quality of data, recommendations for use of data, and what transformations may need to take place as data is migrated.

Identify Short and Long-term Business Objectives During project planning, business objectives are defined. As part of this task, both short- and long-term data quality goals should be identified. Short term objectives usually relate directly to the project and activities related to data movement and manipulation. Longer term goals usually take into consideration how the work being done on the immediate project can be leveraged by the organization and extended for further value.

In the short term, begin improving data by starting small and keeping the scope well-defined. In the long term, keep in mind that if all goes well, you will have success, and you will be asked to replicate this success across the company.

Scope Scoping draws clear parameters around the data you are capturing, moving, cleansing, standardizing, linking, and enriching, and its use. Each requirement must be assessed to determine whether or not the data involved in this project can or will meet the requirement to the satisfaction of the business. There are several basic questions to answer:

1. Does the suggested data exist within the organization?

2. What source or sources contain this data?

3. What is the level of quality within each source, for this information?

4. What cleansing, standardization, or de-duplication is necessary to meet the requirement?

5. What problems or anomalies must be addressed as part of this project?

In a data migration, for example, you might be looking for certain key elements to appear in the target data model. You may first need to confirm that the anticipated target data physically exists within source systems and may next need to determine the best source for the data, or most trusted source. If taking data from multiple sources, you may have to establish a set of standards that all source systems conform to in order to produce a consistent representation of that data in the new target system.

Understanding the scope of the project early is key to its successful and timely delivery. Be sure to categorize the need-to-have data and the nice-to-have data. Be prepared to drop off the nice-to-haves if time becomes short or if the effort of moving, cleansing, standardizing, etc. outweighs the anticipated business benefit.

There are ways to limit scope. For example, if you’re integrating multiple data sources, will it be one large movement of data or several smaller movements? Will the data need to be the entire database, or is 6 months enough? Working through these issues with the business team and IT will keep the project on-time and on target, and will help manage expectations during the project lifecycle so there are no surprises as the project nears a close.


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Analyze Current Technology Early in the process, it is a good idea to take inventory of the current data quality technology in place. Perform interviews of the key technologists and determine what is working and what is not meeting user expectations.

If a process or technology exists that meets user expectations, can it be leveraged within the new solution? If so, can it also be leveraged for other solutions in accordance with long-term business objectives? If not, is it a good source of standards or logic, which can be designed into a new data quality solution that offers more options for future growth?

Often solutions to data quality problems might be point solutions, without regard to the entire enterprise. The key is to develop a solution that can serve the needs of the entire organization.

Assess Data Risks Does your source data actually support the business objectives? During a data risk assessment, it is crucial to ensure that the available data satisfactorily meets business requirements. Much of the legwork for this analysis is performed by IT through mapping-data-to-requirements exercises and performing extensive data investigation of source systems. Should questions arise, key business stakeholders should immediately be involved to ensure the project is ultimately successful in delivering what the business expects.

If data does not meet expectations, what are the root causes of these gaps and how must they be addressed before proceeding with your project? Does project scope need to be revisited or do isolated requirements need to be classified as high risk?

Use a data discovery process on the source data to determine if the data is viable. If the data cannot support key business requirements, the project is at a high risk of failure despite investments of time and money. Thus, before committing to development, first assess data to ensure that the project can ultimately meet user expectations.

Data discovery is the process of discovering the unknown about your data by bringing together IT and business team members, familiar with the meaning of the data content and how the data is to be used. This team will address issues that arise early in the project lifecycle and create workable solutions. For example, you may not want to incorporate specific data elements into your CRM system if there is a high degree of null values, since the data will not meet your business needs.


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Phase 2: Making the Blueprint

In this phase, you’ll assess data quality issues in detail and begin to build a plan for improving data as part of your overall project. Together, key players from IT and the business define corporate standards for data, and a baseline measurement of your current state of data quality is documented. The baseline will serve two purposes. It will help to enlist executive support by showing the business impact of poor data quality. Secondly, it allows you to tangibly show the improvement in the data at named milestones after the new system or solution is in production.

Define Success Metrics Most organizations are cost-conscious, so it is necessary to produce a business case or cost justification for a new initiative. Even if not required, it is recommended that you quantify the impact of data quality processing, as a methodical way in which to measure the impact of your efforts and the value you are providing to your organization. Though frequently overlooked during project execution, these numbers are necessary to drive future investments and promotion within an organization.

Data Quality Metrics and Business Impact Define the data quality metrics which you will track. This may include both high level data-centric rules and specific rules that apply to a particular system or application, such as:

Metric Business Impact Number of records with changes to address data fields for compliance with USPS standards Number of duplicate records identified Number of processed records with incomplete mailing address but with valid phone numbers or emails

Affects the ability to complete marketing programs that are on budget and successful. Affects billing effectiveness.

Number of records with duplicate primary keys

Unique keys must be generated by IT, thus causing delays in the project if unexpected.

Blank values for critical fields of data such as quantity per box in supply chain data or shipping dimensions

Does the customer get the right quantity of items ordered? Can the customer logistically handle the package they receive?

Adherence to standards such as the metric or English systems of

Do the same exact or similar parts exist in the supply chain, but under different measurement


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Metric Business Impact measurement

systems?

Total dollar value of bills with no invoices Total dollar value of invoices with no bills

Is the billing system in compliance with regulations? Is revenue being reported properly? Are orders being fulfilled without a purchase order and invoice?

Technology can play a significant role in uncovering data conditions such as those listed above and establish a recorded baseline of these conditions. Not only will technology help you organize and document results, but it can further be used to manage conditions going forward. Automated data profiling analysis and exception reporting along with drill-down functionality gives you results and the tools to involve non-technical users in the analysis of these results. You can set conditions, such as those listed above, and understand immediately to what degree the metrics are met.

Formulate Communication Strategy A communication strategy should also be put in place at this time. Key business users have defined metrics and identified how those metrics can be related to the business to quantitatively demonstrate value, but how will the organization hear about the upcoming results?

Moreover, do other members of the business community agree with the relationships that have been identified between data, its quality, and the impact of that data on the business? For an effective and useful ROI, it is essential to establish buy-in as part of an early communication plan task, and follow up with updated metrics at pre-determined milestones.

Define Standards Project team members representing the business play a key role in standards definition. The team members involved in this step should be a fair representation of the ultimate user audience. For example, if the end user audience will include sales and marketing and potentially shipping, someone from each of the named departments should be involved in defining system standards. Also, a representative from each of the company's departments should act as a data steward to make sure data adheres to the defined standards in the new system, if not ALSO in the source systems.

With every business, there are certain standards that can be applied to every piece of data. For example, a name and address should almost always conform to postal standards. E-mail addresses conform to a certain shape with a user name, internet domain and an “@” sign in the middle. However, there may be data for which your team needs to define a new standard. This is typically a part number, item description, supply chain data, and other non-address data. For this, you need to set the definition with the business team. As part of the process, explore the current data, decide what special data exists in your required fields, and establish system standards that can then be automated and monitored for compliance.

Create Executive Buy-in While the technical team members are busy with technical designs for the new system or solution, business team members are ensuring that the efforts have positive impact on the


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business. Most project managers find it helpful at this point to seek out the endorsement of a ranking executive. Using the data quality metrics and business impact generated in the previous step, keeping the executives in the loop about your initiative will help you maintain your endorsement of the data quality initiative, foster support, and secure funding for future projects or additional resources. If there are any internal political challenges, executives can help resolve issues and remove roadblocks. If they are already well informed of your efforts, status, and potential positive impact, it will be much easier to invoke their support.

Access Data At this point in design, it is necessary to take a deep dive into data extracts, representative of the actual data that will be used as part of the production system. The purpose here is to understand what mappings, transformations, processing, cleansing, etc. must be established to create and maintain data that meets the needs and standards of the new system or solution.

IT resources are generally responsible for defining data extracts and gaining appropriate access to source systems. This data can then be shared with other team members to support detailed design tasks.

Analyze Source Data The same principles and benefits of a collaborative approach between IT and business team members (described earlier for risk mitigation analysis) are relevant for the in-depth source system analysis necessary for effective and efficient detailed design. Most often, a collaborative effort is hindered by the fact that it becomes time consuming and inefficient to involve business users in the process to clarify data questions because they lack the technical skills necessary to self-sufficiently access data, investigate anomalies, and thus offer insights to drive design. Advanced data discovery tools eliminate this challenge if they offer an intuitive interface through which business users can do all the tasks named above, independent of IT, but for the purpose of collaboration with IT.

It is very feasible to leverage the same technology used for risk mitigation and data metric definition to now help with this up-front analysis of the data. Additional functionality, available within a data discovery tool, presents users with statistics, results, and a window into the data, so that information can be easily digested and navigated by IT and business users alike. A special purpose data browser makes it possible for users to identify and review issues in the data, collaborate and reach consensus about what should be done.

Use technology to facilitate source system analysis. Employ advanced profiling and data discovery functions for comprehensive column and attribute analysis. Identify potential problems within structured data fields such as dates, postal codes, product codes, customer codes, addresses or any attributes that should conform to a particular format and structure. Configure custom data quality rules, and flag any attributes that do not conform.

When you’re done with the analysis, you will have a very good idea of the challenges you face in integrating data and the information necessary to develop designs that address the challenges proactively. At this time it is also a good idea to revisit the project plan and confirm that appropriate time and resources have been allocated to deal with any data issues that have been uncovered.


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Capture a Baseline Business team members have defined the data quality metrics and business impact in a previous step. Now is the time to take a baseline measurement. As part of the source system analysis, a baseline of each source system should be captured and stored as well as how multiple systems conform to expected metrics or business rules. In some cases, it will make sense to look not only at each source system in isolation, but across systems.

Data Architecture & Schema/Data Model As the data model is being developed, a crucial step often overlooked is confirming that the source data supports the anticipated data model design. The best way to have confidence that this is the case is to reverse engineer the data and understand the relationships that naturally exist within the data. This should occur independently of metadata and system documentation, and should be a complete reflection of the data itself. Here again is an opportunity to leverage the technology that the team has been using and is comfortable with: a data discovery tool will already contain all the source information required for this analysis and should contain the functionality to display a data model or schema that represents the native state of the data itself. These schematics can then easily be compared and cross-referenced to intended data model plans by the data modeling team, saving them potentially weeks of manual efforts and missed exceptions.

Data Architecture & Platforms There are any number of different data quality solutions that may be built as part of your overall project. The biggest consideration for a non-custom or in-house data quality solution is whether or not the technology you are acquiring supports process execution on all platforms of the source and target systems within your given project.

To take things a step further and offer more long-term value however, as the designs are being set, and if technology investments are being made, revisit the long term business objectives outlined during the Blueprint phase. Evaluate vendor tools using your longer term vision and ensure that you have options for future connectivity requirements. Provide your organization with the flexibility to extend the data quality processes you design for your immediate project to other systems, in different environments, and on other platforms that exist within your technical enterprise infrastructure.

Develop Test Case Scenarios As you examine your data, you will uncover patterns and common occurrences in the data that require resolution. For example, names may appear in your CRM sources in any one of the following formats:

Smith, John John Smith Smith/John John and Jan Smith

It’s up to you and your business team members to decide how to standardize each of these name formats for optimal efficiency in the target systems. Should ‘John and Jan Smith’ be linked, but separate records in your master file or remain as a single entry?

Set up a test file or database of records that present these common data situations for QA purposes during this stage of the project. A quality assurance (QA) task will be completed prior to going live with new data. This test case scenario definition effectively begins to build a list of data quality anomalies, which you can leverage to build and test business rules and quality processes. Some of the business rules and test cases will come standard with the cleansing process of packaged data quality solutions. These should be highly


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tunable to meet your organization’s specific needs. Others, you can begin to build, based on your needs.

Define Exceptions Process In a data quality process, an exception occurs when a piece of data cannot be interpreted by the business rules and process engine your team has defined, i.e. an address does not contain enough information to be verified with the USPS standardization business rules.

When a data quality exception occurs, the data steward must resolve the exception and decide whether the anomaly is an unusual occurrence, or whether new rules should become part of the data quality process. Your project should define a clear way to handle exceptions, including automated distribution (of error records) where possible, areas of responsibility for correcting, and a method to report anomalies back to the source.


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Phase 3: Implement

When all the planning is done, it’s time to begin to put the technology in place to improve data using automation wherever possible. For the technology resources implementation tasks, we recommend “Trillium Software Data Quality Methodology”, a white paper detailing how to standardize, enrich, and match data, and how to fine-tune the business rules to optimize data. Although this is the most technical of the plan’s phases, business users still play an important role in this phase.

Create User Acceptance Test Plan As team members create User Acceptance Test (UAT) plans, additional considerations should be incorporated that investigate and display the results of data quality processes that have been built into the new system or solution. As a result, UAT should include not only testing of new functionality and/or reports, but should also be prepared to include data quality test case scenarios. Test data should include both good and problematic input data so that a wider business audience (UAT resources) is forced to confirm that the data quality processes are producing desirable results.

Create Data Quality Processes During the implementation phase, the technical team puts together the data quality process defined and designed during the Blueprint phase. This usually includes cleansing, standardization, enrichment, and matching/linking processing. For more details and best practices on data quality process creation, refer to “Trillium Software Data Quality Methodology”, another white paper available through the resource library reached via www.trilliumsoftware.com.

QA Initial Results Results. The most important part of the data quality process should be that business users are happy with the results. As you begin to implement new data quality process designs, project managers should have business users run sample data through the data quality processes to ensure results meet their expectations. Business users can compare results before and after processing with the same data discovery tool that they have been using all along. Coarse-tune processes using sample data, then switch over to a complete data set for formal QA.

Once results have been verified, it’s time to load sample data into the target applications and begin testing it more thoroughly. By taking the extra step with the business during the QA cycle, you’re much more likely to be successful the first time you load data and will avoid loading and reloading data repeatedly.

Validate Rules In phase one and two, you’ve both determined what you have and what you need. Rules are developed in an iterative analytic process. This requires access to knowledge about intended meaning of the data. Business users and data analysts should work together on this process, applying the same technology and process described for analyzing source data, if additional questions come up. Give business users an opportunity to set up test data scenarios and allow them to review the results after the cleansing process.


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This is also your opportunity to review and add your specific terminology, e.g., industry-specific terms, company-specific definitions and regional colloquialisms) not initially part of the standardization terminology. This is a chance also to determine whether they will require geography-specific standardization.

Tune Business Rules and Standards You may find that some of the initial data quality process design does not meet expectations or act as expected. Business team members should be able to interact directly with a rules-based engine and tune the rules to produce results more in line with expectations. This requires an intuitive interface and tuning tools to be built into any products that are purchased to facilitate data quality processing.

Involving business team members directly with the tuning process ensures that the rules exactly meet their needs and removes the risk of failed expectations late in the game.

Integrate Data Quality Processes with Applications and Services Once data quality processes are designed and tested, and business team members are well underway validating and tuning rules, the processes are ready for integration into one or more applications and/or services. If architected with an eye to the future, data quality processes should be reusable across multiple systems, platforms, and applications. Vendor products should likewise support the principles of reuse across the enterprise, to support growth over time as well as providing flexibility of deployment options within your project.

Although your project’s solution may not need the capability to expand and grow over time, having the option of extending to any and all applications, even those that may come to your company through mergers and acquisitions is a feasible best practice that should be seriously considered. This also includes the ability to carry the rules from one application to the next.

This will likewise meet any need to expand from a scheduled batch process to a multi-use real-time process wherever and whenever you need it within enterprise systems.


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Phase 4: Rollout Preparation

During this phase of a project plan, business users and IT must determine how and when the development environment is migrated to production. Before that however, UAT must be completed and users must be trained on the changes they will encounter when using the new system or solution. The Help Desk must also be properly prepared and able to answer any questions that arise as a result of the new technology or changes to existing technology.

Execute User Acceptance Testing Plan (UAT) The User Acceptance Test plan should include a record of the business users’ sign-off of the documented scenarios and the data quality processes that influence automated changes. Some different types of UAT strategies include:

• New System Test—application to be tested is entirely new (not an enhancement or system upgrade).

• Regression Test—amount of change in an existing application requires a full system retest.

• Limited Test—amount of change in an existing application requires only change-specific testing.

Data discovery tools can here again help aid the project during the UAT process by giving both business users and technical users a view into the data. Teams can collaborate and view the results of any data quality process, before and after the process is run.

It’s valuable to test inside the target application, too. Things to test include:

• particularly important when using a real time interface into the data

• systems and applications that interface with the ones included in your project

used throughout the project, or otherwise, to quickly address any questions that arise.

r

they need to use the new solution, to facilitate end-user adoption. Make them aware of:

• or pop-ups requesting validation of automated cleansing and

• The positive impact and business benefits of new, cleaner data

All forms—quality tool

• All reports—ensures the results from the reports are as expected Test scenarios—test the results of the data quality process’s impact on

Throughout your UAT, make sure your business users have easy access to the data, whether it be through tools and technologies

User Training/Help Desk Training Users must be made aware of new applications going online and the Help Desk should know who to call to escalate any technical issues. Effective user training is a critical factofor a successful implementation. Here, the goal is simple: give your users the skills and confidence

• Any new required fields or formats as they enter data into the system Any new screens matching of data


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• The involvement of both business users and IT users in the process of creating high quality data.

Production System Cutover Plan As the system is rolled out to end users, the operations and support team should have all the tools, processes, and knowledge to support them. A plan for transitioning from the project team to the operations and support team is crucial.

Most product managers will create a schedule and plan to engage the new system with newly cleansed data. The migration to production generally occurs during an off-peak time. The decisions you need to make include training, if/how to phase the rollout, expertise needed when the cutover occurs, whether to run multiple systems (old and new) in tandem, and if so, for how long, whether to hire additional resources (e.g., consultants or contractors) to assist, and any additional security considerations.

Successfully Complete Initial Cleanse/Load For many projects, the first step of going live involves an initial load or an initial cleanse process. Data is rarely loaded without encountering errors during the extraction, transforming, and loading of data. The errors generally fall into these categories.

• Incomplete errors –consists of missing records or missing fields. What is not being loaded and what should happen to those records or fields absent of data?

• Syntax – relates to data formatting and how data is represented. Is data the right shape? Does the data fall within value range?

• Semantics – conveys what data means. Is there hidden value in unstructured data? Are there names in address fields, despite compliance with correct data shape? Are there slightly different duplicate records?

If you have executed the tasks communicated so far, you have significantly reduced the likelihood of any of the above-mentioned issues from occurring on your project. By taking the time upfront to thoroughly investigate source system data, incorporate necessary processing into your designs, and perform UAT that includes anticipated problematic data conditions, you have proactively addressed the issues that cause most project teams severe headaches late in the game.

Should something unexpected occur and require attention, you already have the resources and infrastructure in place to quickly react: your team of both IT and business users is already familiar with the project, the data, and any technology you have been using (i.e., your data discovery tool) and can swiftly look at the data and assess the problem for a quick resolution.


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Phase 5: Go Live

Congratulations – you are going live! During this phase your team will turn on the switch and your new data quality processes will begin to provide immediate benefits to your organization. The fruits of your labor will begin to be realized.

SWOT Team At this stage, it’s a good idea to have in place a cross-functional SWOT (Strengths, Weaknesses, Opportunities, Threats) team including – business analysts or departmental resources familiar with business processes, performance engineers, data architects, field technicians, and contacts from any vendors, to be available on an emergency basis to provide rapid problem resolution.

Teams may adopt different processes to help them understand the problem presented and to design a response. Practitioners using problem-solving processes believe that it is important to analyze a problem thoroughly to understand it and design interventions that have a high probability of working.

The intent is to intervene early after a problem is identified and to provide ways by which that problem may be alleviated and the corporation can achieve success.

Teams should meet to complete a post mortem, discussing how the project went and how to further improve on data quality during the next round.

Problem Resolution All support organizations have some form of processes and procedures in place for helping to resolve user and system-generated queries, issues, or problems in a consistent manner. In some organizations these processes are very structured; in others they are more informal.

In addition to efficient processes, it is also very important that the support team have well defined roles and responsibilities to reduce response time to customer needs. Here is an example of an escalation hierarchy, along with the individuals who perform these tasks, for a fairly large implementation. For this example, when a problem is identified, it is escalated as follows.

Tier 1: Help Desk - Help Desk technicians provide first-line support to the user community and perform any additional training and remote operations to resolve issues. If the help desk is unable to resolve the issue, it is escalated to Tier 2.

Tier 2: Informational Professionals - Information Professionals are typically more aware of the data aspect of the operation than the Help Desk. With the aid of a data discovery tool and access to the end-user application, they troubleshoot the issue. If the information professionals are unable to resolve the issue, they escalate it to the Data Stewards at Tier 3.


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Tier 3: Data Stewards - Depending on the nature of the problem, Information Professionals can contact Data Stewards, who tend to have an enterprise view of a data subject area, as opposed to knowledge of data and processes within a given application only. Although most issues are resolved at the first three support tiers, in rare cases, issues can get escalated to Tier 4.

Tier 4: Project Managers - An issue usually reaches this level if an architectural change is required to resolve it. The project managers will have to analyze the situation and take the appropriate actions to resolve it.

The hierarchy just described is merely one example of a support and escalation hierarchy. No matter what type of support hierarchy you have, it is crucial that each group within it understand its role and responsibilities. Moreover, the team must be able to quickly resolve or escalate any issues that arise.

Post Mortem Re-run your baseline processes and collect updated results for a quantified measurement of your impact. Gather up your metrics, your support log, your exceptions processing log, and other relevant documentation. Call a meeting to:

• Ensure that the project met the business objectives

• Ensure that the project met the outlined success criteria

• List the lessons learned during the project; use it as input to improve future project delivery

• Conduct performance reviews for team members

Perform Ongoing Data Quality Processing Now that your system is live, you not only have cleansed historical data loaded into the system or solution, but your ongoing data quality processes should be keeping new incoming data free of the problems identified and prepared for.

Define Monitoring Processes Given that all systems and processes are assumed to be operating well, it is now time to ensure that appropriate monitoring processes are in place. Regularly scheduled data audits are a great way to ensure that data continues to meet expectations, and highlights any areas of quality that have slipped or new problem areas that have become evident.

To facilitate this process, many organizations leverage the technology used for risk assessment, baseline measurements, source system analysis and design, and user acceptance testing. For example, a data discovery tool in which you have trained business users to use to investigate data, collaborate with IT, and measure data metrics, as well as all the knowledge capital built into the environment is easily adapted to perform scheduled audits and ongoing monitoring. Email workflows can be defined to alert key players when problems arise or when problems exceed or fall below defined thresholds.

Monitoring ensures that you continue to meet or even exceed user expectations over time so that your data assets become a trusted source, actively USED by the business.


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Phase 6: Maintain

In most religions of the world, there is a day to reflect on the good work you’ve done, admit your shortcomings, and set a plan in place to improve. Phase six is that day for those who believe in data quality. It is also a time of joy, however. In this phase, the fruits of your labor will be realized and you should not be shy about telling the world what you have accomplished.

Announce Successes One of the keys to maintaining funding for your project is to internally publicize the successes you’ve had. In reality, a data quality initiative should be constantly re-sold at every opportunity, to continue to reinforce in people’s minds, the value you are introducing to your organization.

Ways to communicate your success include:

• Create a monthly data quality email update • Establish a presence on the company Web site or

intranet • Ask the sponsor(s) to send out a memo about the

project from time to time. Feed the sponsor business benefit information such as money saved on marketing mailings, improved marketing sell-through rates, improved inventory and supply chain savings, etc.

• Identify and work closely with a select group of users to help with the communication. Identify what they are contributing to the project, and publish that information.

• Recognize the customers/users of the data first and how they are benefiting from your improved data.

This is also a very good time to remind the company that data quality is everyone’s problem and ways they can help solve data quality issues.

Monitor You can keep track of data quality in a number of ways. A full analysis with your data discovery tool is one way. Each time you compare it to your baseline or the previously measured baseline, you will get a very detailed idea of how your data quality initiative is progressing.

Some tools include a way to automatically keep track of data quality, too. For example, they may include an e-mail notification feature to inform key personnel when business rules are violated such as when data values do not meet predefined requirements, threshold values are exceeded, or nulls are present where unacceptable. These powerful features prevent errors from impacting your business, should your enterprise use data sources that are prone to change.

Data stewards, system owners, and/or key business contacts can receive alerts on critical changes and errors. The tools can then allow users to call up the violation(s) and drill down on the error(s).


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Organizations with action-oriented governance programs use such features to alert key stakeholders and responsible parties of data anomalies. Each day, stewards can address the issues at hand and create prioritized tasks to resolve the issues identified.

Collect New Requirements for Next Phase With your success in hand, it’s time to begin gathering requirements for the next phase. Business users will be inspired by the new intelligence available to them and begin to ask for additional data. They may want you to expand the systems exposed to your newly developed data quality process, add additional data sources to your new system or solution, or incorporate new systems and applications if your company is in acquisition mode. If you play it right, word will get out about your successes, and your solution and/or data quality services will be in demand.

Hold a meeting or series of meetings, to gather new requirements for version 1.1 of the project.

Manage Change Requests/Exceptions A change request conveys a major change to the project/requirements of your new system. Having passed UAT, users may now see additional opportunities to improve business processes, and a way to manage these requests is necessary. Most project managers feel that every project should have some formal change request process and that every request should follow the process. A simple change request process might look something like this:

1. User submits a change request.

2. The assigned resource, perhaps the data steward, assesses the change request to see if it's worth investigating. Compare the benefits to how difficult the change is (impact). Weed out the obvious change requests with limited benefit and place them in a nice-to-have list for future reference. Assess risks in making the changes necessary.

3. The data steward and project manager should document and communicate the assessment to key stakeholders.

4. If the change sounds reasonable and has obvious benefit, ask the corporate sponsor to accept the changes in schedule, cost, quality and risk. Remain objective and let the sponsor decide if the change request has merit.

5. Communicate the schedule and status of the change request to key stakeholders.


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Trillium Software Value Proposition

We have covered a lot of ground herein, but if you distill this paper down into its essence, there are just six guiding principles to use to incorporate a successful and complete data quality strategy, regardless of whether you have a project that is related to CRM, ERP, SCM, CDI, DW, BI, MDM, SOA, data harmonization, OLTP, legacy migration, and so on. When building a solution for data quality, make sure your solution is designed for expansion over time, as your organization develops new needs and faces new challenges. The guiding principles that will best prepare you for extension and growth over time recommend a solution that is:

Principle Description

Comprehensive Deliver fit-for-purpose data for all types of data, everywhere, anytime. It includes the ability to support global business, not just information from the US and UK, but from China, Japan, Germany, Mexico, etc. Not just single byte, but double byte data. Not just name and address data, but all types of data. Important for consolidation/migration projects with international or cross-functional reach.

Intelligent Contains intelligence to identify and address problems in context so you do not have to apply heavy human resources to fix the data. Important for lowering IT costs and saving you money on human resources.

Seamless Does your solution have the capability to expand and grow over time, extending to any and all applications, even those that may come to your company through mergers and acquisitions? Important when you want to apply data quality to key enterprise applications.

Dynamic Can you quickly and precisely change the rules if you need to, to adapt to meet changing business needs? Important because business models and business processes can quickly change as technology advances.

Measurable Can you measure that your solution is working both immediately and over time? Does it produce quantifiable results that can impact business? Important as an internal self-justification of your team, a way to continue improvement, and a way to justify expenditures.

The Trillium Software System answers these challenges with a scalable, flexible framework that supports the integration of data quality processes into any system, at any time, anywhere in the world. From tactical projects to strategic practices, the Trillium Software System increases integration efficiency, lowers development costs, and provides faster return on investment (ROI) from data quality initiatives through:

• Modular software design • Universal connectivity components • Architecture-neutral core technology • Portable, reusable resources • Tunable processes • Expandable, global support


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The Trillium Software System facilitates near-instantaneous replication and portability across practically any platform or system. It lets you leverage efforts from one Trillium Software System implementation across new projects and the entire enterprise, dramatically reducing costs in multiple implementations and allowing you to easily create, propagate, and maintain an enterprise data quality standard.

Trillium Software System includes TS Discovery, a data discovery tool that you can leverage across the life of your project. It facilitates the inclusion of business users in all phases of your project to reduce risk, and better meet the demands of the business. The Trillium Software System also includes TS Quality, a rules-based engine that promotes reusable data quality processes, business user-defined and -tuned rules, and the most deployment options of any data quality product on the market. The Trillium Software System also offers TS Enrichment, a data enrichment service available for supplementing and increasing data available within source systems.


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