How Bad Data is Affecting Your Organization’s Operational Efficiency
Despite recognizing the importance of data quality, many companies still fail to implement a data quality framework that could protect them from making costly mistakes. Poor data does not just cause revenue loss – it’s the reason your company could lose employees, customers and reputation!
Most organizations today understand the importance of data and are ramping up efforts to collect more data. The problem? Organizations are finding it difficult to ensure the quality of their data.
In fact, according to KPMG’s 2016 Global CEO Outlook, ‘84% of CEOs are concerned about the quality of the data they’re basing decisions on.
Despite ‘worrying’ about data quality, most organizations still overlook the role of data quality within operational and business intelligence applications. It is only when a major initiative such as a migration or transformation project fails that the company to truly recognize and make an effort to take data quality seriously.
Here’s an example of how bad data quality can start a vicious chain of events throughout an organization.
An insurance company with nearly 54,000 employees around the globe only realized they had a problem with conflicting data in their mainframe database when they were confronted with the consequences of incorrect payments and mismatched vendor data. They realized that their current system did not have an option for standardizing payee names, meaning every time they ran a query against the main database, they would have to sort through a long list of duplicates.
Not only did this cause them significant troubles with customers, but it also maximized their operational inefficiency. Employees were put to the task of manually sorting, matching and removing redundant data. The outcome? Disgruntled customers, demotivated employees, lagging processes and a loss of yearly revenue.
Examples like these are plenty. Be it the banking, technology, healthcare, real estate, industrial or retail sector, data quality or the lack thereof can cause significant challenges in business operations.
In simple terms, data quality refers to the accuracy, completeness, timeliness and consistency of the data used in your organization. It’s important to note that the scope of data quality is not limited to just customer data – it includes product data, company data, finance data, vendor and external stakeholder data, internal operations data, just to name a few. For an organization to function optimally, it needs to keep data quality as its foundation.
While data quality can refer to multiple problems, we’ll limit the discussion to just three major type of data quality issues that directly impacts the operational efficiency of an organization.
As if revenue loss was not already devastating enough, bad data can cause your employees to lose their morale, decrease their efficiency all while generating a negative perception of your company.
After customers, employees are the most affected with bad data. Across departments, be it marketing or sales, customer relationship or lead generation; employees spend a significant amount of their time cleaning data and resolving the consequences of bad data.
Organizations that hire data scientists to make sense of their data end up spending 60% of their time cleaning and normalizing data. In the meanwhile, frontline employees like customer service reps, sales reps etc are working with corrupt, inaccurate and flawed data, dodging bullets as they come.
Dirty data can literally pull an organization down.
