Believe me, it’s not your hard or soft skills. It goes way deeper into history…
The current state of data can be attributed to a complex and extensive historical background.
Did you know that…
- 92% of organizations reported that they have experienced negative effects from incorrect or incomplete data. This means wasted resources, increased customer dissatisfaction, lost business opportunities… (Source: Experian)
- Poor data quality is estimated to cost businesses an average of an astonishing $15 MILLION annually. This cost includes lost revenue, decreased productivity, and additional expenses for data cleansing and remediation. (Source: Gartner)
- IBM estimates that poor data quality costs the US economy over $3 TRILLION per year. (Source: IBM)
- The Harvard Business Review found that companies that prioritize data quality have seen an increase in revenue of up to 70%. Direct correlation between high-quality data and financial performance. (Source: Harvard Business Review)
- 64% of senior executives believed that data errors have prevented them from fully utilizing analytics tools and achieving their desired outcomes. This highlights once again the importance of data quality for unlocking the full potential of analytics and machine learning. (Source: Deloitte)
So, now, three questions (should) arise…
- How did we land in the ocean of low-quality data?
- What does it mean for a data scientist/analyst/engineer?
- And what it has to do with the title of the article?!
Yeah, you got it right. This article is about the importance of data quality & how to increase it.
Let me explain step by step…
1. How Did We Land in the Ocean of Low-Quality Data?
Data is in a very weird place right now.