Before considering the cost of non-data quality you must know what the business expectations for data quality, how their absence can affect the business and, a little more complex, how to relate each quality issue to a specific obstacle within the organization. The data quality It has many benefits for the company, but to be able to enjoy them you have to be able:
The cost of poor data quality
The impact of a poor data quality policy reverts to different areas within any organization:
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Financial: reflected in the increase in operating cost, decrease in income, waste of possibilities, decrease or delays in cash flow and increase in fines, similar penalties or charges.
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Trust and satisfaction: in bonding with customers, workers and suppliers, decreased trust projected by the organization, decreased reliability of forecasts, inconsistent operational and executive reports, as well as Decision making out of time and / the lack of accuracy.
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Productivity: increased workload, decreased performance, increase Processing time, decrease in the final quality of the product.
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Risk: in connection with the credit evaluation, investments and competition.
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Compliance with legal obligations, industrial expectations or private policies.
The way to act to prevent the occurrence of situations of this type needs hurry, rigor and continuity. It must be linked to a strategy and be supported by a policy that sets out how:
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Check the types of risk and costs related to the use of the information in the company.
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Specifies the expectations regarding data quality.
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Develop the necessary procedures and tools to determine the end of data quality in the organizational environment.
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Establish the data validity restrictions.
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Measure data quality.
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Determine a system for monitoring and controlling quality problems.
Photo credits: “Quality puzzle showing excellent services” by Stuart Miles
How to avoid the costs of poor data quality
The data quality issues They must be approached from the root since their consequences can have effects of great magnitude. No one is free to meet such a surprise. Some examples of unfortunate expenses caused by an inappropriate policy of this type are:
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50 millions of mexican dollars: owed by a car dealer for an error in printing coupons for a cash prize drawing of 1,000 Dollars. It is the difference between printing a single winning coupon or printing the 50.000 as lucky recipients of this award.
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Half a million US dollars: that he New York City Transportation Had to pay to meet the payment of 160.000 pay-per-ride cards with a typo.
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1.962 millions of dollars: was the cost of the error in one of the programming codes of the Mariner I spaceship, what caused its destruction.
The other side of the coin is the one shown by an investigation carried out in 2010 by Harvard University. In this study they state that Google achieves an estimated profit of $ 497 million every year thanks to typos of popular website names that lead users' search authors to sites with typographical errors, where Google ads conveniently proliferate.
Photo credits: “Dollar” de dream designs
Despite this, this is an exception, so it is interesting to invest in processes, techniques, algorithms and operations that contribute to improve data quality to save costs lead, make the most of processes, minimize response times, fine-tune decision-making and actions, improve service and corporate image and enhance marketing actions. How?
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Avoiding duplication: arise when the same or equivalent information is presented in the same table on more than one occasion.
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Ensure data consistency: to avoid the existence of contradictory information.
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Searching the I complete it: so it will be necessary to check that there are no blank or filled fields by default.
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Normalize files: to ensure the data compliance.
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Watching over him data accuracy: compare them with a reference source and apply control measures.
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Guarantee the reliability and integrity of the data.: ensure that all relevant information in a record is present and in a format that allows its use.
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