Enterprise Master Data Management: An SOA Approach to Managing Core Information

This is a great book that merges two hot topics in data management today: Service Oriented Architecture and Master Data Management. We highly recommend looking into this book as it can certainly bring value to your organization. Check it out here.

Customer Data Integration: Reaching a Single Version of the Truth

Often times MDM is brought into an organization because of a data warehousing project or to assist with identity resolution type work. This book provides great insight into how to reach that single version of the truth. Get more info.

Data Driven: Profiting from Your Most Important Business Asset

This is a fantastic book that digs deep into the value of Master Data Management and the value that it can bring to your organization. Perfect whether you are pitching MDM or trying to sell its value. Read more about it.

Grammar and Data Management

 

One of the largest challenges for analysts that are developing data management plans are ones that involve organizing and making sense out of data that allows for free form text. Any time users are able to enter information into systems with free form text they inherit mistakes that are natural to human input. Typos, spelling mistakes, and grammatical errors can be found throughout open ended data.

These challenges can make it difficult to organize and account for all variations of data provided by users. A simple typo can change someone talking about a “Tree” to being entered into the data entry fields as “Three”. This typo of situation can only be minimized by developing systems that have a grammar check, spell check, and format check built into the data entry fields. With these elements in place users will be able to review and check their work before submitting to larger databases of information.

The first thing a data management analyst should investigate when starting a project that includes data with open ended input is to see what checks and safety mechanisms are in place to account for possible errors. If there are not sufficient checks in place than it is the responsibility of a data manager to coordinate with project and system developer leads to push for the implementation of these checks.

If adding these checks are not possible an analyst should prepare a risk document that walks stake holders through the possible errors and impact the lack of these checks may have on their overall system. This maybe a difficult document to complete but should include the fields where data may have possible errors, examples of what these errors may be, and possible solutions to identify these errors when including the data in a higher enterprise data management plan.

Data Management plans should clearly identify all systems that have risks of including data with grammar, spelling, and typing mistakes. If this is not included in the overall data management plan than long term and costly errors can be made by systems and decision makers.

How does your organization account for these type of data management trials?