Leaders at life sciences organizations know that gathering data is a vital part of strategic planning. With robust quantitative information, it’s possible to learn a great deal about market conditions, consumer behavior and logistics challenges. However, not all data is created equal.
While quality data leads to invaluable insights, misleading, error-ridden findings can send a company in the wrong direction, wasting time and resources. Failing to cleanse data properly leads to a range of disadvantages, including greater administrative costs and poorly directed marketing. Dirty data is expensive, and that’s why it’s essential to have this information professionally scrubbed.
The costs of dirty data
Any data record that includes errors is considered “dirty.” This could mean that a record has been duplicated, that the information is out-of-date or incomplete, or that findings drawn from multiple systems have not been correctly parsed. Failing to catch these problems can lead to losses in a variety of ways, such as:
- Miscalculations leading to lost revenue.
- Relying on old contact information and falling out of touch with customers.
- Wasting limited budgets on poorly targeted marketing.
- Allowing misleading metrics to guide plans for expansion.
- Failing to meet stringent compliance standards for record-keeping.
“U.S. businesses estimated 32 percent of their data was inaccurate.”
While organizations are well aware of the risks that go along with their databases, questionable practices for gathering and analyzing information remain a significant issue across many industries. In a 2016 report from Experian, respondents from U.S. businesses estimated 32 percent of their data was inaccurate. This high occurrence of problematic information stems from a combination of human error and poor data management strategies.
Dirty data is especially prevalent in healthcare, as Fortune explained. Electronic health records frequently contain errors, and incompatible systems lead to breakdowns in communication. In many cases, medical professionals simply do not have the training to ensure they enter information into their systems clearly and consistently.
Taking steps to ensure high-quality insights
Fortunately, there are ways to catch dirty data before it leads to major setbacks for an organization. Data cleansing is the process of finding and correcting or deleting those error-ridden records. By manually using wrangling tools or running automated scripts, data experts locate anomalies and misleading outliers while ensuring systems are able to exchange necessary information.
Data cleansing commonly involves steps like:
- Finding user-created errors, such as misspellings.
- Standardizing file formats and organizing records accordingly.
- Checking values for accuracy and uniformity.
- Establishing data filters to root out low-quality information more efficiently.
Once organizations thoroughly scrub findings drawn from multiple sources, they can pass the valuable intelligence to the departments that need it. With a clear, effective data governance procedure in place, a business minimizes its risks and unlocks fresh opportunities.
Making the most of data in life sciences
Clean data gives life sciences organizations an edge in planning for the future and connecting with customers. With accurate information, marketers can be confident their email campaigns are reaching key personnel at hospitals and practices, and representatives are able to maintain long-term relationships with decision-makers. The business as a whole maintains the level of transparency required for compliance with rules such as those set by the Centers for Medicare and Medicaid Services’ Open Payments reporting program.
In today’s business landscape, all industries are driven by data. Capturing the full value of that information requires strategic thinking and advanced tools. Enlisting data cleansing services is a crucial step in supplying your organization with the meaningful insights that enable better communication, greater profitability and expansion over time.