As any reader of this blog knows, data science can deliver tremendous results by identifying business problems, finding opportunities for cost-savings, and creating models to generate market insights. However, inaccurate or failing models can provide incorrect results, sabotaging business opportunities and damaging companies’ reputations.

The latter scenario is more common than you might think; one recent study found that 82 percent of data science executives are concerned about a significant revenue loss or reputational damage stemming from a bad model. With that in mind, let’s take a look at some of the chief dangers of unimproved data models: 

Wrong Decisions and Incorrect KPIs 

Amazon’s much-maligned—and since abandoned—recruiting model underscores how data science can reinforce inequalities such as gender biases and unequal pay. Results will always suffer if an AI project uses the wrong signals during machine learning. To avoid this, it’s important to clearly define business goals and KPIs and unify data and business teams in model development.  

Loss of Productivity 

Thirty-three percent of data science executives in the study mentioned above believe that not improving data models can result in loss of productivity or rework. A model may be successful at its creation, but business conditions, customer demographics, and the competitive landscape are constantly evolving. Data science teams must stay abreast of these and other changes and periodically retrain their models to ensure that productivity isn’t impacted.

Security and Compliance Risks 

As AI takes an increasingly prominent role in industries like healthcare, it will raise the already high stakes on security and compliance. After all, if there are errors in the medical records or training sets, the consequences could be fatal. The U.S. government is also pushing for more transparency and trackability, with two laws aimed at tackling algorithmic bias currently pending. 

Discrimination and Bias 

As mentioned above, if a dataset isn’t inspected and validated, it can easily inherit bias and make flawed predictions. Forty-one percent of executives point to this discrimination and bias as a prime concern in poor data models. However, when managed properly, there are scenarios where AI is better than humans at making unbiased decisions—check out this HBR article for more details. 

Data models are living, evolving projects and must be treated as such. Failing to update and improve data science models means companies don’t have an accurate representation of the world and current business landscape, and insights will suffer as a result.