I compared the “Data Management” with a story of Elephant and Seven Blind Men in my last blog post http://manageyourdata.blogspot.in/2012/09/data-management-elephant-seven-blind-men.html. I promised to start writing my views about the issues that I heard so far from my customers, consultants and product companies that I worked with.
The first issue is confusion with the whole terminology. This seems very basic thing but if all stakeholders in this value chain are not talking same language, same terms with same meaning the initiative is doomed to fail.
What were my questions to them ?
1. How does ETL works in this ?
2. What is the data profiling mechanism?
3. Does this tool normalize my data ?
4. Does this tool classify my data ?
5. Does this deduplicate ?
6. Can this gives me exceptions to rules data sets ?
7. Can I override tool with my own rules ?
8. Does this toll enrich my data ?
9. Can I specify attributes for enrichment ?
10. Can I have multilevel enrichment ?
11. Can I map different datasets to one common datasets?
12. Can this tool produces validation reports with data ?
Few of these seems typical techno-functional and not so business based questions. But when you Go to market – who is your customer? For data – mostly its IT people – who are technical. The business will surely have a different set of questions but they will map to these invariably. How ?
What a Supply Chain or Procurement business person will ask typically for ?
1. How do I cleanse my multilevel redundant BOM (Bill of Material ) Data ?
2. How do I generate required spare parts listing with good data ?
3. Can I have cleansed, good quality suppliers list ?
4. Where do I aggregate my suppliers by parents – to leverage spending patterns ?
5. How do I search my customer list and aggregate that ?
6. Can I have a classified product list – to get necessary dashboard, inventory or spend amounts ?
Have a close look and you will see most of it maps to core questions above.
So in next post – I will try to provide glossary of terminology that invariably and interchangeably used while discussing Data Management .
As usual – Please keep sending your feedback & questions.
Pmendki at gmail dot com
Twitter - @pmendki