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
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.
Healthcare industry – Bad data costs the industry a whopping $300bn each year. 15% lost revenue and 14% expenses lost to data mismanagement
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
How to reduce bad data?
- Transform big data into big value with Master Data Management in data lake architecture
- Bad data is not an isolated event. Analyze, identify and scrutinize patterns
- Leverage modern data management tools for visibility into the entire data lifecycle. Discard legacy systems
- Use AI and ML to identify data management problems automatically. Adopt data pipelines, warehouse, and data lake