Michele Goetz serves enterprise and data architect professionals. Ms. Goetz is a leading expert on data quality, data integration, metadata management, MDM, and data governance. She helps enterprises leverage data assets more effectively by improving the availability and accuracy of the information that businesses use in processes and analytics. Prior to joining Forrester, Ms. Goetz managed the business intelligence and data management programs at PTC. During her tenure, she developed and led the global consolidation of customer data across multiple CRM platforms to support a single view of the customer and manage enterprise-wide data quality. In addition, she established data governance and data quality teams and programs to support a center of excellence for data management. Ms. Goetz also held an executive position at Trillium Software, a provider of data quality solutions and services, introducing thought leadership and recommendations on how organizations can improve data quality and governance programs. She holds an undergraduate degree in mathematics, with a minor in computer science, from Framingham State College.
Recent Stories From this Author
Creating the Data Governance Killer App
October 20, 2014 We have to strike the right balance between automation, manual governance, and scale
Disruption Coming For MDM - The Hub of Context
October 9, 2014 How do you apply master data management within the new data ecosystem?
Quality, Trusted, Fit for Purpose Data?
September 9, 2014 Often lagging in priorities when it comes to data strategy, it appears that data quality is coming back in favor
Are Data Governance Tools Ready for Data Governance?
June 26, 2014 Key takeaways from the new Forrester Wave about Data Governance Tools
Can You Afford To Ignore The Artificial Intelligence Wave?
June 16, 2014 As intelligence becomes a cognitive engagement, AI is elevated from strictly an analytic capability
PIM: MDM on Business Terms
June 10, 2014 PIM is not always a well understood master data solution option for enterprise architects
MDM: Highly Recommended, Still Misunderstood
June 5, 2014 MDM is not a check box in your data strategy. Master data management is a data strategy, and not just for traditional...
Artificial Intelligence Needs More Than A Name, It Needs Personality
May 22, 2014 IBM's acquisition of Cognea reinforces that voice is not enough for artificial intelligence. You need personality.
Welcome to the Future of Data Management
May 12, 2014 Always refer to the four principles of data management and you will be prepared for the future of data management.
Big Data Quality: Certify or Govern?
April 17, 2014 Scale now forces us to rethink what we govern, how we govern, and yes, if we govern.
Why Every Data Architect Should Be An Analyst First
April 8, 2014 Data architects canít only concern themselves with understanding the technologies that are available
Data Governance: Did We Make The Right Choices?
March 10, 2014 We often emphsize how data is supplied, but how it performs in it's consumed state is fogotten
Top 4 Things to Keep In Mind When Evaluating MDM Vendors
February 6, 2014 How do you make a decision between the vendors? Tips based on research from Forrester
What You Really Need to Know about Artificial Intelligence
January 30, 2014 Is AI just a new fancy marketing term for predictive analytics?
7 Deadly Sins of Data Management Investment and Planning
January 15, 2014 When it comes to data investment, data management is still positioning the wrong value
Can Machines be our Friend? IBM Watson Thinks So
January 13, 2014 Cognitive computing has the potential to address important problems that are unmet with advanced analytics solutions
Big Data Governance - Protect And Serve Are Equals
September 11, 2013 What does data governance look like by firms embarking/executing on big data?
Are Data Quality and Data Science Polar Opposites?
August 6, 2013 Big data gurus say that data quality isn't important, but business stakeholders still complain about poor data quality
Data Science and "Closed-Loop" Analytics Changes Master Data Strategy
July 28, 2013 Context is the key in connecting patterns to master data
From Data Couch Potato to Enterprise Flexibility
July 3, 2013 With a cross-fit plan, purpose-built systems can have extended use
Shifting to an MDM Golden Profile
April 29, 2013 It isnt good enough for master data management to be siloed as an integration tool
Is Big Data Better Outside of IT?
April 23, 2013 Strategic value of data splits business and IT on management
Without Data Management Standards - Anarchy!
April 3, 2013 The premise that you need standards to have consensus and consistency is not the argument
Is Your Big Data Stuck in the Pilot Stage?
March 27, 2013 For many data management pioneers, the sandbox becomes a sand trap
Data Management Standards are a Barrier
March 7, 2013 Enterprise architects find that standards limit data, innovation
The MDM Metrics That Matter
February 22, 2013 The questions to match daily master data management with business cases
Data Quality Solutions: Getting IT and Business on the Same Page
February 11, 2013 How do you bridge the vendor divide?
Back Where We Started with Data Quality?
January 29, 2013 With other trends at the door, 2013 may be crucial for clean enterprise info
The Role of Ethics in Data Governance
January 25, 2013 Companies operate in a more externally connected fashion, and two recent stories touch on changes to data responsibility
Ask This and Kiss Your Data Strategy Goodbye
January 11, 2013 Here are ways to frame data management for business
MDM: Its Not about One Version of the Truth
October 26, 2012 Rather than an integration tool, MDM is a strategy for appropriate data
Why Doesnt Governance Lead to Data Freedom?
October 5, 2012 Rules and process should evolve with new business controls
Fast Decisions Trump Perfect Data
September 20, 2012 In part 3 of a series, a reboot on data purging and quality
Big Data Quality: Persistence vs. Disposability
September 7, 2012 In part 2 of series, a look at data profiling and standards
MDM in a Big Data World
August 22, 2012 In part 1 of data quality reboot series, a challenge to the master data model