de-risking master data management and ensuring project success

De-Risking Master Data Management and Ensuring Project Success

IntroductionAs consistent, accurate data becomes more important to companies’ core business objectives, many

organizations are implementing master data management (MDM) strategies to improve the quality and

usability of their data. However, MDM initiatives fail regularly – and for a wide range of reasons. They

frequently run over schedule and over budget, or they do not meet the quality levels required to support

the business needs after the solution has been implemented in production. Many companies fail to properly

plan for their initiatives and use generic integration and migration methodologies and technologies that

are ill-suited to the specific needs of MDM. These failures can ultimately result in damage to the company’s

brand image, decreased productivity, and lost revenue. Improving the effectiveness of MDM initiatives and

reducing the associated risk is extremely important for ensuring that the organization’s business objectives

associated with MDM can be met.

Understanding common points of failure and how to take steps to avoid them is critical to achieving future

success. By carefully applying the appropriate methodologies, aligning initiatives with business objectives,

and engaging in full-production analytics, organizations can more effectively prepare for MDM initiatives

and significantly reduce risk.

2 DE-RISKING MASTER DATA MANAGEMENT AND ENSURING PROJECT SUCCESS

The high failure rate of MDM initiatives is particularly alarming given both the significant investment associated

with these initiatives and the broad implications of their failures in a production environment. As the importance

and sheer volume of business data increases, there is also an increased likelihood and impact of potential

problems. More data means there are simply more opportunities for error. At the same time, there is greater

pressure on IT groups as data quality becomes more critical to core business functions. This makes it important

that companies seek to understand common sources of failure and take steps to mitigate risk.

Why MDM initiatives fail


Problems with generic methodologies and solutions

These frequent failures are often a result of the fact that many organizations use methodologies and technologies

that are not optimized for MDM. These methodologies were originally designed to handle a diverse range of

integration and migration projects and may be decades old. This generic approach is simply not capable of

handling today’s master data management challenges while ensuring that accuracy is kept at levels that are

acceptable for current business requirements.

Generic methodologies and technologies present numerous problems, including:

Inadequate testing

Generic methodologies use samples to test the quality of data and build the migration plan. This

provides only a partial picture of how the MDM will perform in production and doesn’t allow for the

identification of all errors or invalid conditions within the data. This can lead to costly mistakes and

an increased risk of initiative failure.

Imprecise matching

Probabilistic and deterministic matching approaches do not maintain precise control over the matching

process. They group together many different business rules related to record matching and assign

them the same matching score, which means that they have to treat all of those records with the

same score in the same manner even though there may be significant differences among the types

of matches. This creates problems by increasing the likelihood of undermatched (duplicate) records

and overmatched (improperly matched) records – potentially leading to lost data.

Inefficient exception handling

Many MDM implementations are not designed with efficiency in mind. They lack the automation and

ease of use necessary to quickly and effectively manage exceptions. Problems that are not handled

automatically by the software require valuable personnel review time to resolve them; otherwise,

required data accuracy targets will not be met. Underestimating the amount of review required

and not having efficient tools and personnel resources to handle it are two of the leading factors

contributing to time and cost overruns in MDM implementations.


Given the significant risks associated with failed MDM initiatives and the inherent problems with generic

MDM methodologies and technologies, companies could greatly benefit from an alternative approach called

“full-production analytics” that reduces project risk by virtually eliminating these issues.

Full-production analytics consists of three key components:

Reducing risk through full-production analytics

1. Proper pre-implementation planning

2. The use of appropriate technologies to execute the implementation, and

3. A full-production simulation

This provides a holistic approach to MDM initiatives, allowing for significantly reduced risk of failure.

Many of the risks associated with failed MDM initiatives can be reduced with a full-production analytics approach.


Component 1: Engage in pre-implementation planning

Capture business and technology requirements

Many companies don’t effectively plan for their MDM initiatives, often causing implementations to fail to

deliver the accuracy the organization requires and leading to a disconnect with business objectives. In order

to avoid these problems and assure business leaders that the project will meet expectations, it is important

to apply the appropriate methodologies and capture relevant requirements and data from key business and

technology figures within the company.

Business goals

It is critical that input from business leaders and end users be taken into account when planning for an

MDM implementation. This helps ensure that the project will meet the expectations and requirements

of the business and of those who will actually use the data on a daily basis.

Required quality

Gathering information about business goals provides a basis for defining the quality requirements of

the MDM implementation. It is important to remember that the benchmarks for accuracy, completeness,

and consistency must be very specific in order to ensure that predetermined business goals can be

met. The required levels of accuracy may vary significantly between operational applications, such

as sales and customer service, and analytical needs, which may require somewhat lower levels of

data accuracy.

In-depth data analysis

Setting goals is only a first step. It is also important to conduct analyses to ensure that these goals

can actually be met. This requires input from the providers of data and processes as well as an in-

depth data analysis. Doing so allows the organization to have confidence that the data will meet

predefined accuracy targets.


Component 2: Use appropriate technologies

Use data discovery tools that operate on the entire data set

As one of the first steps in deriving full-production analytics, the data discovery process must be done

across all sources of data and all relevant data fields at one time. Ensure that your data discovery

tool has this capability. Otherwise, you will not be able to discover and plan for all of the potential

issues hidden in your data.

Use knowledge-based vs. table-driven cleansing

Standard table-driven cleansing and standardization approaches rely on a lengthy iterative process

(many months or longer) to build up enough entries in the tables to accurately process the entire

data set for full-production analytics. Instead, use a knowledge-based system that already has large

knowledgebases (typically millions of entries) containing properly and improperly-spelled words,

phrases and patterns. This allows for rapid and accurate processing of the entire data set within

hours or days.

Use cognitive vs. probabilistic or deterministic matching

Deterministic and probabilistic matching and linking are generic approaches that may be appropriate

for certain tasks and types of data, but often fall short on the level of precision required for MDM

full-production analytics because they mix multiple, different business rules into one score at the

record level. Instead, use a cognitive matching/linking approach that maintains precise field-level

distinctions throughout the entire matching process.

Use specialized exception review productivity tools

To derive accurate full-production analytics, use exception review productivity tools that are specific

to the type of review being done – cleansing, individual matching, corporate matching/linking, or

customer-to-customer linking.


Component 3: Conduct a full-production simulation

One of the greatest failings of generic integration and migration methodologies is their reliance on sample-based

testing. This provides, at best, a partial view of how an MDM will perform in a production environment and can

lead to costly errors. Full-production simulation using 100% of the data drastically reduces this problem by

providing a more complete picture of MDM performance and data quality, allowing the organization to make

adjustments and refine its approach to meet business and data quality requirements. Ultimately, this allows

companies to reduce the risks associated with their MDM initiative and avoid problems before deploying to

a more high-stakes production environment.

Full-production simulations allow companies to gather data on a wide range of points, including:

• Current quality issues

• Time to full-production implementation, including 100% of required data

• Cost projections

• The resources required to implement the MDM in production and integrate all data sources

• The future state of quality that will be achieved when the MDM is rolled out in production


Full-production analytics allows companies to significantly reduce the risk associated with MDM initiatives. The

approach gives organizations the tools to identify quality issues earlier, well before the MDM is implemented

and errors have real-world implications. This provides a more solid foundation for program success, allowing

the organization to ensure that the MDM implementation will meet business objectives.

This approach provides several benefits, including:

The benefits of the full-production analytics approach

Faster implementation

Applying the appropriate methodologies and technologies and engaging in proper pre-implementation

planning allows companies to deploy MDM implementations more efficiently and finish the

implementation in far less than a single budget cycle. This can substantially reduce costs and allow

users to take advantage of the benefits of the initiative more quickly.

More accurate projections

Capturing business and technology requirements and running full-production simulations allows

companies to make more accurate projections about costs, time to deployment, and data quality.

This makes it easier to allocate resources and plan for the future effectively.

Reduced costly rework

The high failure rate of MDM initiatives means that many companies are forced to adjust or completely

rework their initial attempts. This can be incredibly costly and significantly increases the time it takes

to achieve an implementation that meets the business’s needs.


Fixed-time and fixed-cost implementation

When the team has identified objectives and built a thorough plan to deal with issues and control

factors that would normally extend a project, the time and cost of a project become much more

clearly defined. This allows an organization to require that their MDM vendor provide fixed-time

and fixed-cost implementation bids, which reduces the risk of budget overruns and compresses the

timeline of the project.

The full-production approach provides a solid foundation for MDM program success.

Better business case for MDM

Building a business case for MDM can sometimes be challenging, particularly when the required

budget and timeline are not well-defined. Full-production analytics significantly reduces the unknown

variables in MDM initiatives, making it much easier to build more accurate predictions for the required

investment and ROI. This provides business leaders with a much clearer and less risky decision to make.


MDM initiatives can allow a company to make its mission critical data significantly more accurate and consistent

across a variety of disparate sources. But implementations can often be challenging. A large portion of MDM

initiatives fail, sometimes because the accuracy of data delivered is not up to the business’s expectations, and

sometimes because the initiative runs out of time, resources, or funding. Engaging in full-production analytics

is critical to reducing the risk of failure. This involves engaging in pre-implementation planning, using MDM-

specific methodologies and technologies, and conducting a full-production simulation to test for potential

problems. This technique allows organizations to implement MDM faster, reduce overhead, mitigate risk, and

build a better business case for MDM.

Contact Innovative Systems to learn how to perform full-production analytics prior to starting your next

MDM initiative.

Conclusion


Innovative Systems has been providing software and consulting services to major companies in more

than 40 countries for over 45 years. We deliver both on-premises and cloud-based (SaaS) multi-domain

enterprise data management solutions that can be deployed for operational or decision support

requirements.

World Headquarters

790 Holiday Drive

Pittsburgh, PA 15220-8127 US

Phone: 800.622.6390

International Call: +1.412.937.9300

E-mail: [email protected]

About Innovative Systems, Inc.

www.innovativesystems.com

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