In the age of “alternative facts,” understanding the difference between what’s real and what’s imagined to be real is more imperative than ever. Inaccurate information, whether it’s intentionally made up or an error in data entry, skews the knowledge base and can drive decision-making toward disastrous paths. That is why insisting on using only accurate data – maintaining the integrity of the data – is as critical as obtaining it in the first place.
Accompanying the increasing attention paid to “Big Data” is an inference that all data arrives at the database clear, clean and ready to integrate. Research reveals, however, that data is fragile and its integrity is easily compromised, reducing or eliminating its value and potentially damaging the database. Companies that maintain appropriate controls over their data structure – keeping it traceable, searchable and recoverable – reduce their risks of a breach and subsequent losses.
Information failure at any stage creates extensive damage that is expensive to repair. IBM estimates that the total cost of data failure to American businesses is over $3.1 trillion annually. These losses create waste, decrease productivity and erode competitiveness.
While Big Data can easily be mistaken for a panacea, some sources estimate that as much as 32 percent of all U.S. corporate data is inaccurate, skewing production numbers and sales and ultimately reducing revenue. Companies that invest in data quality management systems are also investing in increasing their income and maintaining their market share.
In the health care industry, decisions based on inaccurate data can have a severe effect on human welfare, including decreasing quality of care and jeopardizing lives. The mandate to utilize only electronic health records (EHR) has exacerbated the problem; because there is no formal regulatory requirement to ensure the accuracy of the data contained in EHRs, the adoption of those systems has failed to achieve the improved health care outcomes the EHRs once promised.
Instead, some industry experts suggest that the EHR mandate has led to the entry of ever-increasing quantities of bad data, which taints the entire database. The consequent quagmire cost one woman her life when her EHR failed to note her abnormal pap smear results, and she died of cancer. Another family lost their child when a handwritten prescription entered erroneously into the system caused the toddler to receive a massive drug overdose.
In the financial world, lack of integrity in databanks can, and has, cost people their businesses and livelihoods. Banks carry immense volumes of data relating to their clients, loans, creditors, debtors, vendors, regulations and myriad other influences. When bad data skews those databanks, customers lose their economic foundations.
In 2001, the Enron scandal demonstrated how intentionally misrepresenting corporate financial statements can shutter a company and its affiliates. Both that company and its accounting firm, Arthur Anderson, collapsed because they deliberately manipulated data to steal from customers and investors. And at the G20 Summit held after the 2008 global financial meltdown, there was international agreement that, particularly in the financial markets, increased transparency and heightened risk mitigation around the digital world must occur across all sectors if the world were going to avoid another financial crash.
To many people, the words “politics” and “integrity” do not belong in the same sentence. Adding “data” to the mix multiplies the challenge. Investigations into the recent U.S. election reveal that gerrymandering – manipulating political data to further political goals – would have elected the Democratic candidate if the votes from only four counties shifted from one state to its neighbor. Others suggest that the winning candidate used “manipulated data” on Facebook to suppress the votes of millions of voters.
A 2011 report showed that up to 40 percent of business value is never achieved because of insufficient or inaccurate information; poor data quality from both the planning and execution phases plays a large part in that failure. Not only does the information gap reduce revenue, but it also decreases operational efficiencies and increases risks. Maintaining a high level of data quality is both a protective and proactive factor for any enterprise, and the adoption of an information integrity plan is the first step toward achieving integrity in your data.
At Data Integrity Solutions, our experts offer strategic consulting services so your enterprise can gain critical insights into where it is losing value because of failures in data quality. We also believe that in light of revelations that bad existing data taints incoming clean data, adding data accuracy assurance programming cannot happen fast enough in most organizations.
In my forthcoming book, “Turning Data into Dollars and Sense,” I explain how the swift rise of technology data did not equal a rise in technology wisdom, and how you can use technological insights to capture enterprise values that are currently lost due to the impact of poor data quality. For information on how to create and adopt your information integrity imperative plan, call us today or email at email@example.com.
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