We’ve written numerous posts on the continuing data analytics explosion and the myriad benefits of transforming into a data-driven organization. But while the opportunities are tremendous, data science is not without its challenges. 

Read on to learn about some common data science roadblocks and what you can do to overcome them. 

  1. Data Integration 

Even as new data is being generated at a staggering rate, many organizations are still struggling just to manage their legacy data systems. Whether it’s data collection, data cleansing, data learning, or data execution, numerous challenges exist in leveraging that data in a unified approach. It’s critical that companies invest the time and resources necessary for data integration to organize their data environment into a single, centralized repository in order to make the best use of their enterprise’s information. 

  1. Explainable Models and Bias 

As AI and ML mature, companies must now confront the unfortunate reality of bias in their models. The transition to creating explainable ML and AI models will likely be slow and, at times, difficult, but it’s an important step in the technologies’ evolution. For more information on what businesses can expect from new regulations designed to address AI ethics and accountability, check out this previous APEX of Innovation post.

  1. Data Scientists vs. Technical Experts

Data scientists are increasingly collaborating with stakeholders from other areas of the business, and it’s not uncommon for differences in working models and approaches to create friction. Companies can get ahead of the potential negative impact on productivity and other challenges that might emerge by developing strong collaborative systems and encouraging frequent and transparent communication. 

  1. Communication with the C-Suite 

Speaking of communication, another challenge that can arise is miscommunication with the C-Suite. When the executive team isn’t clear about the technical metrics or becomes bogged down by technical jargon, they will struggle to implement data science recommendations. Data scientists can address this issue by using trend reports, data visualization techniques, and other tools that can translate technical detail into more business-friendly terms. 

  1. Talent Gap

The data science talent problem is well-recognized and only poised to grow as the demand for data scientists increases. The good news is that there are many data science boot camps in which veteran workers and new hires alike can retrain in data science skills. This option may be particularly beneficial for smaller organizations that lack the budget to recruit experienced data science talent.

If any of the above challenges are familiar to you, take heart: the first step in addressing a problem is recognizing that one exists. For more on getting the best out of your data science investment, take a look at this recent APEX of Innovation post.