Master Data Management with Scott Taylor
For organizations, data that is critical for parties, places, and things is called master data. While conventional strategies are available for manipulating data, this episode places special emphasis on how to make the most out of your master data.
Scott Taylor, the Data Whisperer, shares his advice on aspects like:
- Why Master data Management (MDM) has become so important
- The main pillars of MDM
- Why organizations go through different implementation cycles
- Can common software be used for data governance
- Tips for successful implementation
Why MDM is crucial today than ever
Data is still not very interesting to the average Joe. It is made fancy by terms like data science, but at the core of it, it’s still columns and rows of numbers and words. That is not to say we should ignore the role of Master Data (MD).
With the proliferation of the internet, almost all companies have to deal with data they create or receive, for their operation. This reality means that this data has to be properly structured and managed to harvest insights that move the needle for the company. And for this to happen, organizations are going after skilled professionals like data scientists and engineers.
The bottom line is, companies need to dwell more on the strategic ‘Why?’ of MDM than the technical ‘How?’
Pillars of MDM
The five critical pillars for MDM are:
Let’s dive deeper into some of the details of these pillars:
An important question to ask about your critical business data is: Are there specific data points that create value for the company?
This refers to the format in which data gets entered into storage and analysis tools. We have to understand whether the structure makes sense across the organization. It also has to be easy enough to be understood by the teams working on it.
Just like an understandable structure, the processing of data across various department and tools has to be seamless
With coverage, we are asking whether the data has enough parameters to make business sense. It needs to have enough context and depth to point out both trends and outliers.
All the previous components amount to ‘cleanliness’ of the data. Quality refers to how clean the data is in terms of following the appropriate structures of input storage. As a result, analysis and information generation is easily understood and replicable across the organization.
Why organizations go through several MD implementations without success
A common need for MDM strategies in an organization revolves around maturity in creating value, improving execution around data and protection of the existing data. With the age of the internet, many organizations saw MDM as a trend to jump on. However, without adequate governance, issues of privacy, security, and scaling can crop up.
Other reasons why facilities churn through various MD implementations are:
- Not getting a broad enough view of the market. Understanding the market dynamics and future business needs is an important place to start creating a data governance structure that can endure time.
- They are not getting data under control. Data might need treatment like an independent department. It needs a structure that is easy to understand across existing teams.
- Adoption by the company team. New implementations are a cultural change as much as they are a technology change. Managers need to have implementation support to ensure people, processes, and products get sufficient support. People being the end-users need attention and buy-in to carry these MDM strategies to success.
Can software tools be used for data governance?
In simple terms, not quite. Organizational needs call for a tool that can be as dynamic as the nature of business. MD governance structures are specific in addressing the needs of a company including making consideration for the company growth.
If you do not have the subject matter expertise, consider outsourcing this work to companies that specialize in creating these MDM structures.
The main takeaway from this episode is that for successful implementation,
- Have your management onboard,
- Restrict your data to fundamentals
- Ensure you get support from the day-to-day implementers in the team
- If unsure, outsource data governance structure creation
Peter Horsburgh Links:
- Q6FSA Approach – A Data Department by Robert Vane
- Leveraging Failure Data for Better Decision Making with John Reeve
- Scott Taylor LinkedIn
- MetaMetaConsulting website
- DAMA Body of Knowledge
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