Essential Competencies for Succeeding as a Data Analyst in 2025: Six Necessary Skills
In this era of data and artificial intelligence dominance, the significance of data analysts has magnified tremendously. Regardless of the industry or size, companies are in dire need of individuals who can dissect data and derive useful applications.
What sets top-notch data analysts apart, however, might not be what you'd initially assume. Both job seekers and employers often prioritize technical skills or proficiency with data analysis tools. But, in reality, these are not the decisive factors.
While tools undeniably matter, today's accomplished data analysts need more than just technical expertise. They require a unique set of traits that enable them to uncover insights and ultimately influence business decisions. Here are six qualities that characterize a stellar data analyst:
1. Sharp Observational Skills
Let's delve into the past, all the way to 2nd-century Rome, and Marcus Aurelius' renowned work, Meditations. He wrote, "In life, nothing has as much power to broaden your mind as the ability to observe things systematically and truthfully." This insight mirrors the observational prowess of exceptional data analysts, who can pick out subtle details in both numbers and business tactics. They recognize patterns, inconsistencies, and trends that others might overlook, but their insight doesn't stop there.
Great observers also grasp the dynamics of the business, which allows them to identify areas where analysis has potential to add value. They connect the dots between the business's realities and the data, ensuring that analysis remains applicable and actionable.
2. Probing Questioning
American quality guru, W. Edwards Deming, once said, "If you don't know how to ask the right question, you'll discover nothing." A stellar data analyst asks penetrating questions of both the data and the business, ensuring they address the right problems. These inquiries frequently challenge assumptions and unveil blind spots.
While providing answers to original questions may yield satisfactory results in some instances, top-performing data analysts know that the ideal outcome could be a completely new question to ponder. Examining the right questions helps analysts align their efforts with business needs and generate insightful outcomes.
3. Identifying or Creating Relevant Data Sources
Availability of data has become a persisting challenge for many organizations. According to Data Literacy's five-year maturity assessments across numerous global organizations, "difficulty finding and accessing data" consistently emerges as the primary hinderance to effective data utilization, with over 40% of respondents citing this as the primary hindrance to effectively using data.
Great data analysts are adept at locating the appropriate data sources and gaining access to them. They are knowledgeable about the most promising repositories and can bridge gaps if data is incomplete. If a relevant dataset doesn't exist, they don't just give up; instead, they create the missing data themselves.
Without relevant and complete data, analysis can lead to misleading conclusions. Clever analysts leave no stone unturned.
4. Preparing Data for Analysis
Upon acquiring data, a top-class data analyst first explores its structure to detect any flaws or inconsistencies. Data exploration isn't optional because it almost always reveals deficiencies in the data.
What follows is cleaning and preparing data, which may not appear exciting, but it's essential. Skilled analysts know how to tackle messy data, converting it into a usable format. They understand this step's importance and welcome it.
Prepared data reduces errors, making analysis both accurate and reliable.
5. Discovering Key Insights
The most tempting part of data analysis is the discovery phase. Analysts are like treasure hunters, earning the right to explore a valuable mine. As they delve deeper, they eventually unearth hidden gems.
The process of data discovery calls for a blend of technical skills, business intelligence, and curiosity. Excellent data analysts know how to focus on high-potential areas and execute work swiftly and efficiently. They filter out the noise, pinpointing the most crucial metrics and patterns that resonate with stakeholders.
Timely insights can drive immediate action, whereas overly complex analysis may become obsolete.
6. Sharing Insights Effectively
Discovering valuable insights is not the finish line; communicating those insights effectively might well determine the success or failure of the entire endeavor.
Even the most stringent analysis can lose significance if poorly presented. Powerful data analysts excel at transforming complex findings into engaging stories, employing creative visualization and clear narratives to captivate their audience. They bridge the chasm between technical analysis and business understanding by avoiding convoluted terminology and focusing on providing practical, actionable insights.
Honing Your Skills as a Data Analyst
In my view, these six traits set apart mediocre data analysts from their exceptional counterparts. While cultivating these skills is not easy, they are all learnable.
A comprehensive data literacy training program can help streamline this process. Beyond training, aspiring analysts can build these skills through hands-on practice and application, ultimately empowering themselves to contribute meaningfully to their organizations.
At the heart of being an excellent data analyst, it's all about being perceptive, inquisitive, adaptable, and committed to delivering value.
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Ben Jones, as a top-notch data analyst, exhibited sharp observational skills, often finding subtle details in both numbers and business tactics that others might overlook. He was also known for his probing questioning, constantly challenging assumptions and unveiling blind spots in the data and business.
During his tenure at XYZ Corporation, Ben was instrumental in identifying and creating relevant data sources for complex projects, ensuring that the analysis remained applicable and actionable. His ability to prepare data for analysis was commendable, as he diligently cleaned and prepared the data, reducing errors and making analysis both accurate and reliable.
