Data scientists face many challenges that can block productivity throughout the data science workflow. As organisations continue to become more data-driven, a collaborative environment for easier access and viewing of data, models trained on data, reproducibility and insights found in data are critical.
Given the speed of the fields of artificial intelligence and machine learning are evolving, and the resulting opportunities to uncover insights, top-notch data science requires more than one person with a laptop. Once you have a data science team, all members must work together, important information needs to be shared about data preparation, results from previous projects, and the best way to deploy models.
So, if you want your data science team to achieve more, make sure they meet these three Cs: Context, Consistency, and (secure) Collaboration.
Model building is an iterative process that relies on try-it-and-fail experimentation and can last long without institutional knowledge documented, stored, and made available to data scientists. So, Context is elementary, to know more about for example the data they're looking at, how people have addressed the problem in the past, and how prior work informs the landscape.
Without knowledge management and context, new or junior employees may struggle with onboarding, slowing their ability to contribute, and teams spend time re-creating projects instead of adding to former work, which can slow down the entire company.
Building this foundation of knowledge also reduces key person risk. If someone goes on vacation or leaves a project, other team members have the necessary base from which to jump in and keep that project going.
As data science's teams grow and various tools, training sets, and hardware requirements become more complex, getting consistent results from older projects can be challenging. Processes and systems for environmental management are essential for growing teams.
IT and business leaders who can expect a reliable level of consistency can also feel more confident in the types of strategic shifts that AI facilitates. When it comes to data science projects, the stakes are high and require significant investment, so data scientists should have an infrastructure in which they can operate from start to finish with a guaranteed level of repeatability. This full reproducibility translates into the data consistency that top executives are looking for to determine whether a data science project is important enough and aligned with its business goals.
Teams need a consistent way of sharing the exact same software environments. If, for example, a data scientist is using a laptop while a data engineer is running a different version of a library running on a cloud VM, that data scientist may see their data model producing different results from one machine to the other.
Collaboration requires more than just data scientists working together on a project. It involves collaborating with the business, properly documenting code and processes, building a library of existing technologies, ensuring repeatability, and building collaborative validation pipelines.
As businesses continue to transition their operations to a hybrid work model, organisations realise that data science collaboration is much more delicate than face-to-face collaboration. While some core data science responsibilities are manageable with the help of a single data science (data preparation, research, and data model iteration), most business leaders mistakenly put collaboration on the sidelines, obstructing remote productivity. But, the easier it is to share information, the easier it is to leak it.
Most people don't like to spend time searching for emails or comparing files to make sure they have the correct data. Having to rely on various sources only adds unnecessary cognitive load. By using cloud-based tools, data science professionals can bring corporate security to data science research and leverage IT best practices.
It's amazing to see how far data science has come in the past few years. Data scientists are helping companies around the world confidently answer previously unsolvable problems. Digital tools such as software workbenches that provide context, promote consistency and enable secure collaboration will help us make data science more useful and consistent with less effort.
However, as the world of data science continues to evolve, it is time to move away from a more ad hoc and reactive way of getting work done. Resources that data scientists can use to generate context, consistency, and greater collaboration, such as software workbenches, can be critical to data science success. In the end, projects will require less effort from data scientists, engineers, analysts, and researchers who will be better able to accelerate the field's continued and phenomenal success.
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