Tag Archive for Data Quality

Three Steps to High Quality Master Data

Data quality is critical to business, because poor business data leads to poor operations and poor management decisions. For any business to succeed, especially now in this digital-first era, data is “the air your business needs to breathe”.  If leadership at your organization is starting to consider what digital transformation means to your business or industry – and how your business needs to evolve to thrive in these changing times, they will likely assess the current business and technology state. One of the most common outcomes management may observe is that the business systems are “outdated” and “need to be replaced”. As a result, many businesses resolve to replace legacy systems with modern business systems as part of their digital transformation strategy.

Digital Transformation Starts with Data

More than likely, those legacy systems did a terrible job with your business data. They often permitted numerous, incomplete master data records to be entered into the system. Now, you have customer records which aren’t really customers. The “Bill To’s” are “Sold-To’s”, the “Sold-To’s” are “Ship-To’s”, and the data won’t tell you which is which. You might even have international customers with all of their pertinent information in the NOTES section. Each system which shares customer master data with other systems contains just a small piece of the customer, not the complete record.

This may have been the way things were “always done” or departments made due with the systems available, but now it’s a much larger problem, because in order to transform itself, a business must leverage its data assets. It’s a significant problem when you consider all the data your legacy systems maintain. Parts, assets, locations, vendors, material, GL accounts: each suffer from different, slightly nuanced data quality problems. Now it hits you: your legacy systems have resulted in legacy data.  And as the old saying goes – “garbage in, garbage out.” In order to modernize your systems, you must first get a handle on data and your data practices.

Data Quality Processes

The data modernization process should begin with Master Data Management (MDM), because MDM can be an effective data quality improvement tool to launch your business’ Digital Transformation journey. Here’s how a data quality process works in MDM.

Data Validation – Master Data Management systems provide the ability to define data quality rules for the master data. You’ll want these rules to be robust — checking for completeness and accuracy. Once defined and applied, these rules highlight the gaps you have in your source data and anticipate problems which will present themselves when that master data is loaded into your shiny new modern business applications.

Data Standardization – Master Data thrives in a standardized world. Whether it is address standardization, ISO standardization, UPC standardization, DUNS standardization, standards assist greatly with the final step in the process.

Matching and Survivorship – If you have master data residing in more than one system, then your data quality process must consider the creation of a “golden record”. The golden record is the best, single representation of the master data, and it must be arrived at by matching similar records from heterogeneous systems and grouping them into clusters. Once these clusters are formed, a golden record emerges which contains the “survivors” from the source data. For example, the data from a CRM system may be the most authoritative source for location information, because service personnel are working in CRM regularly, but the AR system may have the best DUNS credit rating information.

Modernize Your Data and Modernize Your Business

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These three data quality processes result in a radical transformation in the quality of master data, laying the foundation for critical steps which follow. Whether or not your digital transformation involves system modernization, your journey requires clean, usable data. Digital transformation can improve your ability to engage with customers, but only if you have a complete view of who your customers are. Digital transformation can empower your employees, but only if your employees have accurate information about the core assets of the business. Digital transformation can help optimize operations, but only if management has can make informed data driven decisions. Finally, digital transformation can drive product innovation, but only if you know what your products can and cannot currently do.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics. 

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Formulate Essential Data Governance Practices

The creation of a  Data Governance function at your organization is a critical success factor in implementing Master Data Management.  Just like any machine on a factory floor, Master Data is an Asset.  An Asset implies ownership, maintenance and value creation: so too with Master Data.  To borrow an analogy from the Manufacturing world, Transactional data is the Widget, and Master data is one of the machines that makes the Widget.  It is part of your organization’s Value Chain.

Unfortunately, firms starting on the road to MDM fall peril to one of two pitfalls on either extreme of the Data Governance mandate.  The first pitfall is to treat MDM as a one-time project, not a program.  Projects have an end-date, Programs are continual.  Have you ever heard of an Asset Maintenance Project?  That’s a recipe for crisis.  Firms which maintain their assets as a Program do far better.

The second pitfall is ask the Data Governance team to do too much, too fast.  Governance cannot do much without some asset to govern.  Have you ever heard of a Machinery Maintenance Program instituted before you figured out what type of machinery you needed, what the output requirements were, or before you made the capital purchase?  I haven’t either.  First, you acquire the capital.  You do so with the expectation that you will maintain it.  Then you put it into production. Then you formulate the maintenance schedule and execute that plan.

In order to successfully stand up a Data Governance function for your Master Data Program, you’ll need to understand these essential roles in Data Governance: Executive Steering, Data Owners, Data Stewards. 

Follow these Do’s and Don’ts:

Do establish an Executive Steering Committee for all Master Data practices in your enterprise, focused upon strategic requirements, metrics and accountability.

Do establish Data Quality Metrics.   Tie them to strategic drivers of the business. Review them regularly.  Your MDM toolset should provide analytics or dashboards to provide this view.

Don’t ask the Steering Committee to own the data model or processes – that is the Data Ownership role.

Do establish a Data Ownership group for each domain of Master Data.  Ownership teams are typically cross-functional, not simply the AR manager for Customer Master Data, or the HR manager for Employee Master Data.  As you evolve down the maturity path, you will find that master data has a broad set of stakeholders – Do be ready to be inclusive.

Do establish regular status meetings where Data Ownership meets with the Executive Steering Committee to review priorities and issues.

Don’t require that Data Owners “handle the data”.  That is the Data Stewardship role.

Do formalize a Data Stewardship team for each domain of Master Data.  Data Stewards are “data people” – business people who live in the data, but with no technical skills required, per se (though technology people can contribute to a Data Stewardship team).

Don’t restrict Data Stewards to just  the people who report to the Data Owner – think cross-functional!

Do anticipate conflicts – Data Owners should have some political skills.  The reality is that Master Data is valuable to a broad set of constituencies within an enterprise.  Be practical as it relates to one faction’s “Wish List” and keep moving the ball forward.

Without a Data Governance function, MDM tends to be a one-time project (“Clean it Once”) and fails to deliver real value.  Without a clear vision of how Data Governance support MDM, it can hold things up. A rational Data Governance function does not need to hold up the execution of a Master Data project – it supports it. Keep Data Governance strategic, cross functional, and flexible. Then, let the MDM technology team deliver the tools.