Data quality is the foundation that EHS insights are built upon. When EHS data suffers from poor data quality, insights are eroded and the information that was once on a stable foundation is no longer suitable for analytics.
Many companies starting their data analytics journey make the mistake of skipping the data cleaning process all together. None of us want to see how the sausage is made, we just want the bratwurst to magically appear. But as we have seen over and over, insightful analytics cannot be achieved with poor data quality.
Only 3% of Data Meets Quality Standards
A 2017 Harvard Business Review article, “Only 3% of Companies’ Data Meets Basic Quality Standards,” outlined the data quality crisis best, stating that, “Forty-seven percent of newly-created data records have at least one critical (e.g., work-impacting) error.”
If you are struggling with data quality issues, know that you are not alone. In our latest article on ISHN.com, we examine why data quality is so critical, why companies struggle and finally, what can be done about it.
At first, when data is entered into a database, missing values, incorrect selections, relational hierarchy issues, etc., do not cause much of a problem. However, as we continue to accumulate data, the data quality issues that we did not perceive at first have now compounded as the volume and velocity of data increased.
If data quality is not addressed before we attempt to preform analytics, we will be left with eroded insights. By eroding analytics capabilities, poor data quality will also lessen the return on investment (ROI) on EHS (Environmental, Health and Safety) management systems.
Read our latest article on ISHN.com, “Data Quality – It’s a Dirty Job, but Someone’s Got To Do It” for more insights.