Organizations have long recognized the importance of data and analytics in understanding their customers, enhancing their products and processes, and gaining market share. So, the question is not whether companies should integrate data in their decision-making, but how to do it best. Below, we outline some of the most integral skills productive data teams exhibit, which companies can adopt as they grow.
Seek the Most Important Skill in Data Analytics: Critical Thinking
Discussions of data analytics typically revolve around technical requirements like coding or modeling. However, it’s the behavioral qualities that lead to the best data products. Ranking topmost in the list of those qualities is critical thinking: the process of sensible and impartial evaluation of data to gather value.
This goes beyond technical design and extends deeper into genuine curiosity about each business unit’s goals, methods, and structure. It’s asking the right questions and presenting just enough skepticism needed to derive value and uncover patterns unknown to the business today. This process of questioning moves organizations toward healthy decision-making grounding their success in data analytics.
Foster Connectors of Data and Business Operations
Technical skills are commonly separated from business acumen, disregarding the necessity of both to achieve full potential. A great analyst demonstrates a strong understanding of the business while also being able to translate that into technical solutions. Additionally, speaking the language of the business can greatly improve the understanding of analytical findings as these analysts can translate complex technical findings into actionable insights.
These dual-threat analysts save tremendous amounts of time by not needing long discovery or requirements gathering sessions before building data artifacts. Without business understanding, analysts are often only used for basic operational reporting and support. This provides some value to the organization but fails to unlock the value that exploratory analysis can generate. Exploratory analysis can identify remarkable correlations or new connections, generating actionable insights for the organization. This analysis is dependent on an in-depth knowledge of the business but can expose underlying issues and drive toward favorable outcomes.
Bring Champions of Continuous Learning and Rapid Growth
It’s no coincidence that we haven’t mentioned a specific curriculum to master. It isn’t that technical skillsets are peripheral, but rather that data tools and trends are ever evolving. What is often more important is the ability to adapt to new and upcoming technologies. Instead of being fixed on skillsets they already have, a great data analytics team needs to be ready for continuous learning and growth.
Having a solid foundation in analytics tools and key concepts that underpin the science of data is valuable. However, what remains more critical is fostering a learning and growth attitude ready to face and comb through all the mess, problems, misunderstandings, and imperfections that come with each data project. There are various learning indicators to look for in a candidate including compelling side projects, certifications, or skills acquired outside of formal settings. These demonstrate intrinsic motivation and courage towards newness and ambiguity, making them distinguished contenders against imperfect data.
Working with data to help an organization make informed decisions is a substantial journey. When employing a data analytics team with the right qualities, it becomes less intimidating and more worthwhile. Seek people who challenge themselves to question their own biases, ones who search for creative outlooks to information, examine and evaluate evidence, and make well-informed conclusions. Professionals who think intentionally, ask the right questions, and seek learning and growth opportunities relentlessly make data analytics teams that much more successful.
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Contributions from Zubaidah Farhan