Data is everywhere and touches all aspects of our personal and business lives.
The problem is that not all data is good data. Without proper data quality management process, it can lead to poor outcomes with potentially catastrophic consequences for the population and individual businesses as well as the wider economy.
Many Organizations Fail to Manage Data Correctly
Yet, according to a survey of 1,800 senior business leaders in North America and Europe carried out by PWC and Iron Mountain in 2019, “three quarters of organizations surveyed lack the skills and technology to use their data to gain an edge on competitors. Even further, three out of four companies haven't employed a data analyst, and out of companies that do, only one quarter are using these employees competently”.
Bad Data Impacts on the Bottom Line
This would seem to suggest that many organizations still do not recognize the true value of their data and moreover they don’t realize that bad data can have a major negative impact on their business and that it can actually cost them money on a daily basis.
IBM has estimated that bad data costs the U.S. economy around $3.1 trillion dollars each year. Additional research from Experian also found that bad data has a direct impact on the bottom line of 88% of American companies, with the average company losing around 12% of its total revenue.
These staggering statistics come in spite of the increasing investment businesses are making in new business tools and AI initiatives.
The fact is that the growth in the volume of data companies are accumulating seems to be inversely proportional to its quality with business leaders appearing to prefer to rely on their intuition when it comes to decision-making.
Bad Data Equals Bad Decision-Making
Bad data invariably leads to bad decision-making that can have an impact right across the organization especially on employee efficiency, marketing, R&D and on lower productivity levels. These are typical examples of how bad data has resulted in bad outcomes for some businesses:
#1 At an energy services company, its inconsistent supplier data resulted in incorrect payments and increased costs of entering the same data multiple times.
#2 At a telecommunications company, due to poor data quality, under-billing resulted in revenue leakage of just over 3% of total revenue.
#3 At a call center, data inconsistencies led to a lack of trust in the data that negatively affected productivity. Agents were often increasing call times by needing to ask a customer to validate product, service, and customer data during an interaction.
#4 At the Department of Defense “… the inability to match payroll records to the official employment records cost millions in payroll overpayments to deserters, prisoners, and ‘ghost’ soldiers.”
These are just a few of the many cases where bad data has led to serious financial losses for the organization but bad data can also have an impact in other areas with equally serious consequences.
As we have all experienced during the recent health crisis, ensuring that hospitals have the capacity to cope with thousands of emergency cases has been a priority for politicians and healthcare managers and has relied heavily on the daily infection rate data being accurate and available.
History will judge if they got this right or not.
Data Quality Must be a Top Priority
While many business decisions do not have such serious life or death outcomes to consider, bad data for any business can mean lost revenue, reputational damage and loss of jobs, that can ultimately lead to closure.
With over 40% (according to Gartner) of businesses failing to achieve their objectives due to missing, incomplete or inaccurate data, bad data is clearly a widespread problem and business leaders have a responsibility to their stakeholders and employees to put data quality at the top of their to-do list.
