How Do You Like Your Data – Clean or Dirty?

This week, Rochelle Hood, SSON’s new Global Head of Communities and Process, reminds us how costly faulty data can be, and shares 5 simple steps to get started on data cleansing. I routinely frequent a Tiki Grill in Florida which touts its specialty as offering most menu items Clean or Dirty. The dirty preparation style includes lots of butter or oil and plenty of extras (extra cheese, extra bacon). The "dirty" cooked food offers tempting smells and captures the heart of your taste buds; however you nearly always pay a bigger price in the long run. Working with "dirty" data can also have lasting effects and impact quality, accuracy, productivity and customer satisfaction. In the first segment of this multi-part series on Master Data Management (MDM), today we’ll explore the benefits and challenges of data cleansing. How do you know if you have dirty data? Ask yourself these questions: Have you ever entered an entry for a price quote and forgotten to delete it? When you run a sales report does the same customer appear with 5 different spellings? Do you have any blank description fields for products or generic items? Do you have old product codes that are no longer in use? Do you have the same supplier who gets paid in a 3 different methods (ACH, Wire, Check) depending on which vendor number you select? If you have answered yes to any of the above, your data probably needs to be cleansed. Many companies only approach data cleansing during major Enterprise Resource Planning (ERP) system integrations or upgrades. Data Cleansing involves the following processes: Standardization Error correction Reconciliation or matching Duplicate elimination Augmentation of the data Getting started is one of the biggest challenges. Below are five steps to initial your data cleansing approach and create your plan: Plan – define the scope of the data to be included and identify all of the sources. Prioritize the most critical data domains or data sets to be cleansed. Start small and leverage your process to gain early success. Analyze to cleanse – establish standards and complete a gap analysis to outline the inconsistencies. Implement automation – numerous services, tools and systems exist to efficiently identify and resolve data inconsistencies. When evaluating solutions remember this goal – effective data cleansing requires on-going maintenance not just yet a one time clean up. Append missing data – document sources and appoint a team to resolve missing data issues, determine root cause and implement a corrective action. Monitor and govern – implement reconciliations, audit controls and routine governance reviews to keep your data clean. (click to enlarge) Dirty data may sound interesting or adventurous – be sure to remember, cleanse now or pay later. For additional resources and information, please access these references: 1. Oracle Master Data Management White Paper – Section 15: Cleanse and Govern 2. Data Cleansing Guidelines – Sample Template 3. Transform Your Data Quality – Top Ten Tips To learn more, plan to attend the 2014 Shared Services and Outsourcing Week in Orlando (March 11-15) featuring the following Data Management Sessions: Workshop E: Master Data Management: Your Journey to Full Scale Analytics and Insights presented by Scott Burns, VP, Master Data and Reporting, Thomson Reuters Fireside Chat: Getting Big Insights from Big Data – How to Get from Master Data and Analytics to True Value; Interviewer: Lee Coulter, SVP, CEO, Shared Services, Ascension Health Plenary Session: Realize the Potential of Adopting a Formal Analytics Function to Produce Meaningful Data and Actionable Insights, presented by Scott B. Manning, Partner and Practice Lead, Corporate and Shared Services, ScottMadden Track Session: The Master Data Travel Guide – From Roadmap to Turn by Turn Directions, presented by HP [inlinead]

How Do You Like Your Data – Clean or Dirty?

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How Do You Like Your Data – Clean or Dirty?

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