A data team will move as fast as the amount of knowledge they reuse.
The more one masters the art of reusability the more they can limit the time reinventing the wheel and can focus on innovation and high-impact work.
Reusability is not only about automation, it is actually (way) broader. It is in fact a direct descendant of knowledge. Read Huspress about knowledge from 2 weeks ago.
Personal knowledge significantly enhances problem-solving abilities by drawing from past experiences and learnings. The brain, along with accumulated knowledge, can recognize previous situations, connect the dots, and suggest ways to resolve a problem. These solutions can be a direct "copy-paste" from the past or a novel approach, inferred by connecting ideas from different domains.
However, personal knowledge is always limited by the amount of information one can assimilate or build upon. To learn faster, people rely on collective knowledge, asking: "What have others learned or built that I can use?" This approach drastically reduces time spent on redundant research or problem-solving and encourages collaboration among members of a community. This community can range from a small group like "Data Analysts in Lyon" to something as broad as "Humanity" (yes, that broad).
For analytics teams, knowledge is divided between technical knowledge (e.g., how to write SQL, understanding the data pipeline) and business knowledge (e.g., comprehending the User entity or MRR metric). By fostering a friendly and impactful atmosphere, teams can better share and apply this knowledge to their work.
Accessing knowledge hasn't always been an easy task. Over time, however, it has become more accessible, particularly with increasing literacy rates in our societies.
The advent of computers has further revolutionized access to information. Now, we can search through vast bodies of knowledge, allowing us to quickly locate relevant information, whether it's on the entire web, StackOverflow problems, or within small communities.
In every case, search functionality facilitates knowledge sharing within communities and promotes continuous learning.
For analytics teams, the ability to search for previously executed pieces of code or analyses enables them to reuse this knowledge, reducing the time spent on a task or improving consistency. This ease of access to knowledge has transformed how we learn, collaborate, and grow.
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Search is an incredibly powerful tool for navigating vast amounts of knowledge, but it often leaves the information somewhat intangible. When a specific piece of knowledge is used or searched multiple times, it might be more efficient to create a new component to make applying the knowledge easier.
Components are small, digestible pieces of knowledge that can be reused as is. They can take various forms, such as books, processes, code functions, and more – the format isn't as crucial as their reusability.
For analytics teams, components can include SQL functions, model transformations, how-to guides, ... These materials can be readily used by data analysts, engineers to boost their proficiency and efficiency in their work. By creating and sharing such components, teams foster an impactful environment that encourages growth and collaboration.
Funnily enough, in the developer world, components are often named libraries 🤓
Once you've divided knowledge into reusable components, you can connect them and automate processes.
This not only increases operational efficiency by reducing manual, repetitive tasks but also minimizes human error. As a result, more time is freed up for strategic, high-value work.
For analytics teams, think of automation as the creation of dashboards. You've built small pieces of knowledge with automated access. There's still a vast scope of knowledge to master that should not end in dashboards. This is where investigations and ad-hoc analyses come into play, helping teams explore new insights and develop a more comprehensive understanding.
Recently, the latest GenAI (LLM) advancements have dramatically changed the dynamics of knowledge sharing.
By creating a parallel network of knowledge, GenAI can:
For analytics teams, this means that their traditional ways of working are evolving. The focus and energy once spent on technical topics will gradually shift towards business insights and knowledge sharing.
Reusability of code, takeaways, and how-tos is already crucial for building an impactful data team.
As time progresses, technical skills will become more commonplace, while analysts' impact will continue to grow. By concentrating on delivering the right insights from the data and creating and sharing more knowledge with the rest of the team, analysts can further enhance their value and drive innovation in a friendly and collaborative environment.