How Dirty Data Can Impact Business

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Quality over quantity is usually considered a high priority. Especially when it comes to handling data. In today’s tech-driven society, companies rely upon a wide variety of data sets to make impactful decisions. As such, organizations must take extra care to ensure that data is relatively free of errors while reflecting the true nature of the information being analyzed.

While today’s companies are increasingly data-driven, new findings have discovered a severe lack of data quality. Trifacta, a leading data research company, recently released a new study shedding light on issues related to data quality along with potential business implications.


What the Study Discovered


Findings from the study showed that dirty and disorganized data can hurt tech-driven AI initiatives. The study found that projects tend to take longer, are more expensive, and may even provide skew results. 


Although data quality has been an issue since humans first started transcribing events thousands of years ago, the impact can be felt more tangibly today. The study entitled, “Obstacles to AI and Analytics Adoption in the Cloud,” surveyed more than 600 high-level executives and posed questions on several topics related to data quality.


Among the findings related to data quality, the research found that:

  • 26% of respondents found data to be “completely accurate.”
  • 42% of respondents found data to be “very accurate.”
  • 32% of respondents found data to be “somewhat accurate” or “very inaccurate.”


Additionally, up to 38% of businesses believed that poor data quality had a negative impact on their businesses. Diving deeper into the survey, individuals stated that 38% of projects took longer than expected, 36% turned out to be more expensive, and 33% were unable to achieve initially desired results. 


Business leaders are no longer able to ignore the potentially negative consequences associated with poor data quality. While analytics play a decidedly impactful role in decision making, using bad data does not lead to accurate findings. 


Future Impact of Poor Data


As the number of organizations utilizing large numbers of disparate data sources continues to grow, there will be significant negative ramifications due to inadequate information. As leaders become more cognizant of the issues, they’ll be more likely to adopt policies aimed at improving data sets. 


A striking 75% of executives feel that current data is unreliable. Even companies with the most robust data processing capabilities often lack in areas of quality. 


Additional findings from the survey found that:

  • Only 14% of respondents had access to required data
  • 39% were unable to access customer data
  • 34% were unable to access financial data
  • 26% were unable to access employee data
  • 26% were unable to access sales transaction data


The Trifacta study findings noted that preparing data for use in AI and Machine Learning initiatives took up substantial employee capital. Results further showed that data preparation accounted for a significant portion of a data analyst’s workday. 


In fact, one study performed by Forrester found that data preparation takes up to 80% of a data analyst’s time. Expending resources of this magnitude wastes valuable hours that could be better spent addressing critical company initiatives. 


Why expend valuable human capital performing repetitive, time-intensive tasks? Enlisting the help of a knowledgeable third-party data company can help to minimize the number of business hours wasted.


How Bitvore Can Help Process Data


As data continues to grow at an exponential rate, it can be challenging to ensure that data sets are processed in an accurate and time-efficient manner. Bitvore handles massive amounts of unstructured data while providing valuable business insights via advanced AI techniques.


Our approach creates AI-ready data to help eliminate time wasted on mundane, manual tasks. If you would like additional information on how Bitvore Cellenus can improve your business results, read our case study below.


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