The fields of artificial intelligence (AI) and machine learning (ML) are, at the tail-end of 2021, no longer nascent fields with uncertain futures ahead of them. AI and ML have grown to become massively influential spheres of influence on the broader world of data science, a fact that has remained truer than ever throughout this year.
As AI, ML, and, subsequently, data science have continued to expand, though, so too have the parameters that can make or break the success of data science teams. The opportunities to obtain significant and profound insights from the fields of AI and ML are predicated on data science teams that are larger than simply one data scientist operating with a single laptop. There’s simply too much data that needs to be obtained, cleaned, and prepared for analysis – a process that consumes a significant portion of a data scientist’s average workday – for any one person to handle alone.
Modern data science projects revolve around important information regarding data preparation, prior data science projects, and potential ways forward to deploy data models that must be shared with multiple data sciences. Therefore, it’s crucial to investigate the reasons why data science teams require context, consistency, and secure collaboration of their data to ensure data science success. Let’s quickly examine each of these requirements so that we can better understand what data science success moving forward may look like.
Part One: Context
Our examination of future data science success begins with context: no process of iterative model-building that relies on try-it-and-fail experimentation can last long without institutional knowledge that is documented, stored, and made available to data scientists. And, yet, a great deal of institutional knowledge is regularly lost because of a lack of proper documentation and storage.
Consider this common scenario: a junior or citizen data scientist is pulled into a project to improve their skills, only to struggle soon after with synchronous and asynchronous collaboration because of a lack of context. These ad-hoc team members need context to know more about the data with which they’re interacting, the people that have addressed problems in the past, and how previous work influenced the current project landscape.
The need to properly document projects as well as data models and their workflows can easily distract a team of data scientists, let alone a single one operating alone. Leaders may consider the option to hire a freelance developer to contribute their time toward the preservation and dissemination of institutional knowledge to improve the standard review and feedback sessions of modern data science projects. These sessions as well as software systems, workbenches, and best practices can streamline the more effective capture of project-related context that improves data discoverability of junior and citizen data scientists in the future.
Data science success requires the streamlined management of knowledge and its surrounding context. Without it, new, junior, and citizen data scientists are likely to struggle with onboarding and the meaningful contribution to their projects, which in turn leads to teams re-creating projects rather than contributing to previous work.
Part Two: Consistency
The fields of ML and AI have contributed to foundational changes when it comes to financial services, the health and life sciences, and manufacturing; these industries, though, are subject to significant regulatory environments. This means that an AI project that takes place in a regulated environment must be reproducible with a clear audit trail. In other words, IT and business leaders who are in some way, shape, or form involved with a data science project need to ensure a level of data consistency when it comes to their data science project’s results.
IT and business leaders who can expect a reliable level of consistency can also enjoy more confidence when it comes time to make the types of strategic shifts that AI facilitates. There’s plenty at stake when it comes to data science projects and there’s a lot of investment riding on them, so data scientists deserve an infrastructure in which they can operate with a guaranteed level of reproducibility from start to finish. This full reproducibility translates into the consistency in data that top executives are looking for in order to decide whether or not a data science project is sufficiently significant and in alignment with their business objectives.
These top executives should, in turn, expect that as their science teams expand, so too will the necessary training sets and hardware requirements to ensure consistency in results from older projects. Therefore, processes and systems that help manage an environment are an absolute necessity for a data science team expansion. 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 next. The bottom line: executives should ensure that their data collaborators have a consistent way of sharing the exact same software environments.
Part Three: Collaboration
Finally, we come to the importance of secure collaboration. As businesses continue to shift their operations to a work-from-home model, organizations are realizing that data science collaboration is much more difficult than in-person collaboration. Although some core data science duties are manageable with the help of a single data science (data prep, research, and data model iteration), the majority of business executives have mistakenly left collaboration by the wayside and have subsequently hindered remote productivity.
But how does one facilitate the effective and remote coordination between project participants as well as the security of project data? The answer lies in shareable work files and data pertaining to a data science project that make it more viable to disseminate information remotely. And as dissemination of project-related data becomes simpler, the simpler it becomes to share information, the easier it is to facilitate remote data collaboration. Participants of a data science project can leverage cloud-based tools to strengthen the security behind their research. but too many leaders have made the mistake of not encouraging collaboration, reducing productivity.
Conclusion
The sheer progress that has unfolded in the realm of data science in recent years has been unprecedented and frankly amazing. Data science’s progression has made it feasible for companies worldwide to address questions that previously had few, if any, readily available answers without the innovations that have been made possible by AI and ML.
However, as the world of data science continues to mature and grow, it’s time for top executives and the data science teams they oversee to migrate 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 like software workbenches are likely to be essential to data science success. Ultimately, projects will demand less effort from the data scientists, engineers, analysts, and researchers, who will be better able to accelerate the field’s continued and astonishing success.
