How To Reduce Bad Data and its Impact

What is
bad data?

Bad data in simple words is inaccurate data for organizations – missing information fields, wrong data, inappropriate, non-consistent and duplicate data.

The High Cost of Low quality/Bad Data

The Business

Bad data costs an organization around $15 mn per year, as per Gartner.
IBM also discovered that businesses lose $3.1 trillion annually due to poor data quality in the US.

The Industry

Healthcare industry – Bad data costs the industry a whopping $300bn each year. 15% lost revenue and 14% expenses lost to data mismanagement

The Economy

Bad data has an impact on 88% of the American companies, losses around 12% of revenue, according to Experian
Android, iOS, and Windows apps to allow users to access the reports and interact with the data

The Impact of Bad Data

Only 3% of the organization’s data meets the quality standards, as per HBR
70% of data experts believe that data quality impact AI and ML projects outcomes – longer duration, inflated costs, and financial liability
Lost opportunities, revenue loss and customer churn
Algorithmic bias – credit score models often identifying non-risk customers as risky
77% of business users lack trust in their data
50% of IT budget may be spent on rework and information scrap

Reasons behind bad data

Data transformation issues

Transforming data into another format is often a challenge. A legacy mainframe to NoSQL conversion is quite complex, and only a few intelligent tools can differentiate the commas and number formats.

Poor data governance

Most organizations consider data governance as a cost. But poor data governance leads to regulatory risk exposure and increased time spent on non-value data management tasks.

Data decay

Data is not static; it constantly changes. Email addresses, contact details, and personal information change continuously. An outdated database increases efforts in communication and collaboration.

Inconsistent data entry protocols

All organizations have duplicate data and inconsistencies while data is entered manually and formatted.

Data migration and integration

Consolidating data systems via integration or migration often led to irregular, missing values to increase inconsistencies.