5 Tips To Guarantee the Success of a Master Data Project - Customers, Products, Vendors, Assets, etc


Getting a single 360-degee view of customers, vendors, products, employees, and other data is very important to organizations. Sales and marketing staff need the latest 360-degree master view of customers, so that they are able to assess the customer's buying habits and target them for new campaigns, while the procurement and accounts payable departments care about a single view of vendor data, and human resources and payroll require a master copy of an employee master. This sounds straightforward, but it isn't.

In this article, I will provide 5 important tips that will guarantee the success of a master data management project.

10 Pain Points Based on a Survey of Marketing Professionals

A recent survey of marketing professionals in the United Kingdom identified the 10 pain points with the highest migraine ratings:

  1. 54% said that 'IT and web development teams are a major bottleneck.'

  2. 47% said that ‘There is no time to test and optimize campaigns.’

  3. 42% said ‘I’ve always wanted a single customer view, but haven’t been given the time, budget or IT resources to build one.’

  4. 41% said ‘I can’t keep track of customers across different channels and on different devices.’

  5. 41% said ‘I don’t have enough budget / my budget is decreasing.’

  6. 40% said ‘I’m struggling with multiple data sources.’

  7. 40% said ‘I have trouble defining attribution and assessing the touchpoints required to convert a customer.’

  8. 39% said ‘I struggle to prove the ROI of marketing activities.’

  9. 33% said ‘I’m in a battle to keep up with marketing technology.’

  10. 30% said that ‘Finding marketers with the right skills is a nightmare.’

Some MDM background

For marketing professionals getting a single view of the customer is essential for success. The same is true of other departments such as procurement, account payable, sales, and human resources that are dependent on vendor, employee, product, asset, and other data.

Given the complexity of Master Data Management (MDM) implementations, many master data projects either don't get off the ground due to analysis/paralysis, disagreements between the business and IT stakeholders on which domain to master first, immature of non-existent data governance, skilled resources or fail to deliver the expected results. Mastering data by its very nature is a complex task since it requires creating a single copy of an entity (e.g., customer) from numerous incomplete or poor quality copies.

The Six-Step Data Mastering Process

The six steps involved in mastering data for a particular domain (e.g., customer, product, vendor, etc.) are (1) inventory all data sources and analyze the quality of data that needs to be mastered and model it, (2) acquire data from internal and external sources, (3) standardize and normalize the data, (4) match and merge the data using pre-defined business rules, (5) persist the mastered records, and (6) provide API's or an interface for consumers to access and a mechanism for certain applications to update the master records.

The diagram above provides a graphical representation of the components of an enterprise MDM project.

5 Tips To Guarantee the Success of a Master Data Project

The probability of success of a master data management project will increase significantly if organizations implement the five tips I share below. They are based on experience that I've gained while planning for, developing strategies, architecting, and leading multi-domain MDM projects.

Tip #1: Get data governance right before starting MDM

Data governance is a pre-requisite for the success of an MDM project. The reasons why - need subject matter experts from the business organization to define the data quality requirements, address questions related to business processes and their usage of data, and resolve issues related to the data being mastered. Implementing a governance regime will ensure subject matter experts are on board and willing and able to help during the entire duration of the project.

Tip #2: Do the due diligence on your data ecosystem

The data being mastered will typically be acquired or obtained via multiple channels - online forms, offline data interfaces, or API's. Copies of this data get persisted in numerous data stores across multiple departments. The copies typically are at different levels of completeness and quality. It is imperative that the project team inventory all data sources that store the data to be mastered and profile the data in each of the sources to assess its quality. This level of due diligence will help determine the amount of data cleansing, standardization, and normalization that will be required and this data will help plan better.

Tip #3: Master the Simplest Domain First

I've been involved in numerous MDM projects and am also stumped by this. Inadvertently each department has it's favorite domain and a vested interest in ensuring that its favorite domain gets mastered first. What people forget is that the prioritization of domains should be based on the results of the due diligence performed (Tip #2) and a good rule of thumb is to master the simplest domain first.

Let me share a real life example to emphasize this point. In 2014, my firm was hired to develop the MDM strategy and roadmap, recommend the sequence in which the domains should be mastered, and evaluate the best MDM product for a global Fortune 100 firm in the hospitality vertical. We spent six months doing the due diligence, assessing the maturity of the client's data governance and quality programs and analyzing the complexity of its data ecosystem. Based on the this and our experience in implementing MDM projects, we recommended that the client start by mastering property data since it was the simplest and would give us a quick win. Mastering property data would also enable us to mature the client's not so mature data governance and quality programs. This would be followed by mastering vendor data and then customers, given their complexity.

The business and IT stakeholders at their global headquarters were kept abreast of the results of our due diligence and recommendations. We assumed that they'd obtained buy-in from their peers. But the sales and marketing team threw a major roadblock just when we were getting ready to finalize the plans and kick-off the project. They felt that customer data was most important and should be mastered first, instead of property data. When the business stakeholders didn't agree with our recommendations, we decided to roll off the project rather than jeopardize our reputation for success. Three years have gone by and the client's MDM project is still not off the ground.

Tip #4: Conduct a thorough product evaluation

There are numerous MDM products on the market and each of these products has its strengths and weaknesses. I've outlined the evaluation criteria and offer lots of tips in my article titled "Everything You Need to Know to Evaluate and Select an MDM Tool".

Here's a list of some of the leading MDM vendors:

  • Talend (Open Source)

  • Orchestra Networks

  • Informatica MDM

  • SAP

  • Stibo Systems

  • IBM

  • Riversand

  • Oracle

  • Enterworks

  • TIBCO Software

  • Reltio

Tip #5: Experience Matters

To ensure the success of your MDM project utilize internal resources and external firms that have the expertise in data integration, data quality, data governance, and data distribution and prior multi-domain MDM implementation experience.

Conclusion

MDM projects are complex and require buy-in and engagement from stakeholders from business, operations, and technology departments and cross-functional engagement. Organizations that wish to succeed at their implementation will be best served to focus on the 5 important tips I share in the article.

Go forth and conquer!

About Jay Zaidi:

As the Founder and Managing Partner of AlyData, my firm and I help leaders derive tangible business value from their data and information assets — to power sales, marketing, innovation, product development, and risk management. Our clients include financial services, healthcare, biotech firms and federal agencies. I’ve led strategic data and analytics engagements at Fannie Mae, Citibank, Hilton Hotels, The DOW Chemical Company, Ohio Edison, Illinois Health and Science, IBA Molecular, and The Consumer Finance Protection Bureau. To learn more or get in touch, visit http://www.alydata.com.

Or follow him on Twitter: @jayzaidi

#masterdatamanagement #mdm #datagovernance #dataquality #customerdata #vendordata #assetdata #vendorsdata #employeedata

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