The four (4) key data quality (DQ) dimensions are completeness, consistency, conformity, and consolidation (uniqueness). Data quality can be improved via cleansing (normalization, standardization), classification/coding, creation (enrichment, enhancement, construction), and consolidation (deduplication). Once you get data clean, you need to keep it clean. Perfect data equals perfect business (assuming people, process, and technology are not potential sources of failure).
A common measurement of DQ is to assess the proportion of data sets where the data is “clean” on a dimension, based on the standards being used to define “clean.” The definition of “clean” may vary across industries, across companies, and data types (finance, vendor, customer, material, assets, …).
The perfect data score is 100%. As the number of dirty records goes up, the score decreases. Bad data causes poor business process execution. As the DQ score decreases, the more likely it may impact your business.
The value of data that unlocks your business potential can be thought of using the following formula:
Volume of Data x Data Quality x Velocity (Usage) x Average Transaction Value
Where:
Do you know your data quality dimensions? Are they measured consistently? How are they measured? Are they monitored over time? Do standards, rules, and workflows enforce them? What is your overall total score for each material type, or business object, or data domain in your business? Examples are Materials, Vendors, Customers, Equipment, BOMs, Work Centers, Article Master, Financials, and so on. How does data quality impact your business locally and globally? How does it affect your constituents (partners, suppliers, customers)? Is data an asset or a liability?
One interpretation of the overall data quality score is to multiply the four (4) key data quality dimensions because any business transaction using that data set has the probability of not being impacted by bad data. If all four dimensions are perfect (100%) then 1 x 1 x 1 x 1 = 100% not impacted by bad data. If you have business execution problems, at least, data is not a source of failure.
The reason to have this knowledge is to reduce the chance of increasing costs of doing business by improving data quality. Three examples of Overall Data Quality (ODQ) are:
If my business had a 62% chance of being impacted, randomly, I would never sleep at night. One never knows when the impact happens, how severe that impact might be, or how much it is going to cost. Sometimes, in worst-case scenarios, it is life and limb – we never want that to happen.
It would be good to know if your data quality reports indicate how many records have all four error types, or three error types, or two error types, or just one error type, and of course, how many records have ZERO errors (excellence in data, perfect data). You want excellence across all your data. Why not eliminate it as a potential source of business execution failures?
It would be good to have an ODQ program with governance to help assure data excellence so that you could improve data quality and have a way to monitor and measure that improvement, as well as assess the reduction in business impacts over time. That means having a way to measure ROI of data as-is (now), and to-be (future), repeatedly, in a systematic way.
The point is, when a transaction is executed with bad data, one of the following may occur, which costs you money and time. Depending on the severity of the kind of incident, such as HSE, the occurrence could require a lot more than just money and time:
This is the importance of Data Quality Monitoring and Remediation (uDQR) (a Utopia addon for SAP MDG) and a Data Health Assessment (DHA) (a Utopia service); to determine if you have bad data and to show bad data’s propensity to create costs, to destroy value, to make work difficult.
Utopia uses DHA combined with Strategic Consulting initially to help you lay out a plan to start avoiding those costs by jointly laying out a plan to get the data clean and keep it clean. We want to help you create value and help avoid costs. We can help you calculate the value too. With perfect data, which is perfectly possible, eliminate data as a potential source of business execution failure!
Look for another blog post about the cost of the hidden content factory in your company.
Contact the Utopia Strategic Value Management (SVM) team at SVM@UtopiaInc.com to learn more. Thank you.