Key Data Analyst Skills You Need in Any Position

Data analytics is a rare skill set that transcends industries and sectors. From engineering to retail, being able to transform data into actionable insights is invaluable. Companies worldwide rely on this expertise to drive their strategies and decisions. This is why even professionals in non-data roles can benefit greatly from mastering analytics skills.

This article outlines the ten essential data analyst skills that will help you thrive in any role. From data visualization to analytical tools, we break down the key competencies that will give you an edge in the job market. We also add three more skills for those who want to become data analysts.

The Evolution of Data Analytics Skills

Data analysts transform raw data into actionable insights that shape strategies and operations. It involves gathering, processing, and analyzing data to help organizations make informed decisions. Analytics first emerged as an academic discipline in the 1990s. That was the decade when every sector - from public administration to healthcare - came to rely on large data sets for decision-making.

As the volume, velocity, and veracity (the three Vs) of business data surged, practices like data scraping, data mining, and big data analysis became commonplace. Today, almost 2.5 quintillion bytes of data are produced every day. Even small and medium businesses produce and analyze thousands of gigabytes annually. As a result, the importance of large-scale data scraping and analysis techniques has skyrocketed over the last decade.

Basic data analyst abilities are no longer considered a specialized skill set. Instead, they have become a must-have requirement no matter your field of work. For example, Amazon expects almost all office employees to have a basic ability to explore and analyze data.

This expectation is especially high in departments or roles where data-driven decision-making is a core part of the work. In such cases, even employees not associated with data analytics are expected to have some skill in SQL or at least Excel spreadsheets.

What Makes Data Analyst Skills Important

The ability to analyze data, spot patterns, identify problems, and find logical solutions has become essential at all levels of professional activity. These basic capacities are as crucial for a warehouse order picker as they are for managers and executives.

Businesses operating in competitive industries recognize that analytical abilities reflect a mindset that is more impactful than any single skill. They drive workplace innovation, efficiency, and the development of a company's work ethic and culture. Employees with strong analytical skills are better at handling negotiations, evaluations, and team-building. This is why the right mix of analytical skills and emotional intelligence is seen as a key trait of effective managers.

Let's take a more detailed look at the factors that make analytics skills so important in the workplace:

• Problem-solving: In the complex world of data analytics, you're bound to face challenges daily. Logical thinking, foresight, hindsight, and the ability to connect the dots are key to identifying problems and making decisions based on data that help businesses thrive.
• Critical thinking: Knowing which data to analyze and critically evaluating it are crucial for drawing sound conclusions. 
• Creativity: Tackling problems often requires out-of-the-box solutions. Analytical skills enable you to develop effective strategies by accurately analyzing underlying factors.
• Detail-oriented big picture: Understanding how data-based recommendations impact all aspects of a business, not just the bottom line, is essential. Analytical skills help you see the broader effects of your decisions.
• Communication: Clear communication allows you to translate complex findings into insights that non-technical stakeholders can understand. It helps the entire team understand data insights and make informed decisions. 

Improving your analytical skills involves self-evaluation, learning from mistakes, continuous improvement, and being open to new ideas and knowledge.

Types of Data Analytics Skills

While the field of data analytics is diverse, all analytical skills can be split into two major types: technical and soft skills. The key distinction between the two lies in how those skills are acquired.

Technical Skills

These are the hard skills required to handle data. They include understanding statistical methods, mastering programming languages, and becoming proficient with data visualization tools. Technical skills usually require formal training or certification in data analytics. 

To learn more about the technical skills you need in your workplace, check out Turing College's Data Analytics program.

Soft Skills

Equally important are the skills that help data analysts communicate their findings and work well with others. These include critical thinking, problem-solving, and effective communication. Soft skills ensure that the entire team understands and acts upon data insights. These qualities are developed over the course of professional employment. 

10 Essential Data Analyst Skills Applicable Anywhere 

Both technical and soft analytical skills are useful across nearly every business function - from management and sales to marketing, finance, planning, and accounting. Even if you're not planning to become a data analyst, they will come in handy in your career.

Proficiency in SQL

SQL (Structured Query Language) is essential for data management and arguably the most vital skill for data analysts.  It's the tool that allows you to query and manipulate data stored in relational databases. If your workplace relies on data-driven decisions, you may want to get familiar with it even if your role is not directly tied to analytics. 

Why it matters: SQL is essential because almost all companies use relational databases to store their data. Whether you're working in sales, finance, or operations, SQL is the key to accessing and making sense of all the related data.

Action steps: Start with the basics, such as the SELECT, INSERT, UPDATE, and DELETE commands. Then move on to more complex queries involving joins, subqueries, and indexes. Practice regularly on platforms like SQLZoo or LeetCode to solidify your understanding.

Spreadsheet Skills

Microsoft Excel and Google Sheets might seem basic compared to other tools, but they are still powerhouses for data analysis. Advanced Excel skills go beyond simple spreadsheets. They include mastering functions like VLOOKUP, HLOOKUP, and INDEX-MATCH, creating pivot tables, and using macros for automation.

Why it matters: Spreadsheets are everywhere in business, and their versatility makes them indispensable. For quick analysis, visualization, and sharing of data, spreadsheets are hard to beat.

Action steps: Take online courses that focus on advanced spreadsheet techniques. Practice creating complex models and dashboards. Learn how to write and debug macros using VBA to automate repetitive tasks.

Performance Metric Monitoring

Monitoring performance metrics is essential for any team member. It helps you track key performance indicators (KPIs) and measure the success of your initiatives.

Why it matters: Knowing and tracking performance metrics enable businesses to assess their strategies and operations. It allows for timely adjustments and data-driven decision-making to optimize performance.

Action steps: Familiarize yourself with common performance metrics in your industry. Use tools like Google Analytics or custom dashboards in Tableau or Power BI to keep an eye on these metrics. Develop a habit of reporting on these metrics and providing actionable insights.

Data Analysis with Generative AI

Generative AI, such as OpenAI's GPT-4, is transforming data analysis by automating complex tasks and generating insights from large datasets. While it won't replace all data analysis skills, it makes basic analytical tasks faster and more accessible to those without a data background. 

Why it matters: Generative AI can swiftly process vast amounts of data, build predictive models, uncover patterns, and provide recommendations. This frees you up to focus on higher-level strategic analysis.

Action steps: Learn the basics of generative AI and explore tools that offer these capabilities. Experiment with AI for tasks such as data exploration, trend analysis, forecasting, and anomaly detection. Keep up with AI advancements and integrate these tools into your workflow.

Data Visualization

Extracting insights from data is only one part of the data analysis process. Visualizing data is critical for bridging the gap between complex data and actionable insights. Tools like Tableau, Power BI, and Looker Studio are essential for creating clear and compelling visualizations.

Why it matters: Charts, graphs, maps, and other visual representations of data make it easier to understand. Good visualizations can turn dry data into engaging stories that drive decision-making.

Action steps: Learn how to use one or more visualization tools. Start with basic charts and graphs, and then move on to more complex visualizations like heat maps, scatter plots, and interactive dashboards. Practice by visualizing data sets you work with regularly.

Data Storytelling

Data storytelling is the art of turning your data findings into a compelling narrative. It’s about presenting the data and explaining the significance of the insights and their implications.

Why it matters: Good storytelling makes data more accessible and engaging for non-technical stakeholders. It helps convey the importance of the insights and drives better decision

Action steps: Learn the elements of a good story: a clear beginning (context), middle (analysis), and end (insights and recommendations). Practice presenting your data in a narrative format, focusing on clarity and engagement.

Presentation Skills

Mastery of presentation tools and the ability to present data visually and verbally are key to translating complex data into actions. Effective presentation skills can help you convey your insights clearly and persuasively.

Why it matters: A good presentation can determine whether the stakeholders will act upon your insights or ignore them. It's about crafting a story that your audience can connect with..

Action steps: Practice your presentation skills. Focus on making your slides clear and engaging. Use visuals effectively and practice your delivery to ensure you can present your findings with confidence.

Domain Knowledge

Having a deep understanding of the industry you work in can greatly enhance your effectiveness in any role. This includes knowledge of industry-specific metrics, trends, and regulatory requirements.

Why it matters: Knowing the context helps you make sense of the data and draw meaningful insights. It helps you understand what is important and why.

Action steps: Stay updated with industry trends and news. Engage with industry reports and whitepapers. Participate in relevant forums and discussions to deepen your understanding.

Business Acumen

Aligning your data analysis – or any daily task – with business goals is critical. Understanding the broader business context ensures your decisions are relevant.

Why it matters: Business acumen helps you see the bigger picture and understand how your work supports the overall strategy. It helps you provide insights that drive business growth and efficiency.

Action steps: Build your business knowledge by learning about your company's goals, strategies, and operations. Talk with business leaders and stakeholders to understand their needs and challenges. Use this knowledge to guide your daily work.

Collaboration

Data analysis is often a collaborative effort, and so is being part of any organization. Working effectively with cross-functional teams and communicating with various stakeholders is essential.

Why it matters: Collaboration improves your work by bringing in other perspectives and expertise. It ensures that your decisions align with different departments' needs and expectations.

Action steps: Develop strong interpersonal skills. Practice active listening and open communication. Talk to your colleagues and stakeholders to understand their needs and integrate their feedback into your work.

Advanced Skills for Aspiring Data Analysts

If you're ready to launch your career as a data analyst, all the above-listed skills still apply. No matter your industry, you'll need things like domain knowledge and data visualization to perform your job effectively. SQL is also a must-have for any data analyst. On top of that, you'll need a diverse technical skill set, including data cleaning and programming languages.

Here are three more essential skills you need to be a successful data analyst.

Data Cleaning and Preparation

When it comes to data analysis, the ability to clean and prepare data is fundamental. As a data analyst or data scientist, you'll spend a lot of time on tasks like retrieving data from various sources, checking data quality, and dealing with missing or inconsistent values. 

Why it matters: Properly cleaned data allows even simple algorithms to generate valuable insights. On the flip side, uncleaned data can produce misleading patterns and lead a business toward mistaken conclusions. So, while data cleaning may not be the most glamorous part of a data analyst's job, it's well worth the effort.

Action steps: Develop a systematic approach to data cleaning. Master techniques for handling missing values, outliers, and duplicate records. Use tools and libraries such as pandas in Python to automate parts of the data preparation process.

Statistical Programming Languages

Skilled data analysts often use statistical programming languages to analyze large data sets. Learning Python and R can significantly enhance your data analysis capabilities. These tools are designed for performing complex analyses that are beyond the scope of Excel.

Why it matters: Python and R come with powerful libraries like pandas, NumPy, and scikit-learn. These simplify data manipulation, statistical analysis, and machine learning. Mastering these languages allows you to conduct sophisticated analyses and build predictive models.

Action steps: Focus on learning the basics of Python. Understand how to use key libraries for data manipulation and analysis. Build small projects to apply what you've learned in a practical context.

Machine Learning

Machine learning takes data analytics to the next level by enabling computers to learn from data. While not always required, understanding the basics can greatly broaden your capabilities as a data analyst.

Why it matters: Machine learning offers deeper insights and more accurate predictions, helping businesses make better decisions. It's particularly useful for customer segmentation, demand forecasting, and anomaly detection.

Action steps: Learn the fundamentals of machine learning, focusing on key concepts like supervised and unsupervised learning. Practice building models using libraries like scikit-learn and TensorFlow. Apply your skills to real-world datasets to gain practical experience.

Get Certified Analytics Skills at Turing College

Whether you're looking to move into data analyst roles or to take your current data skills to the next level, having in-demand data qualifications is essential. Adding a recognized certification to your resume can work wonders for your career trajectory. By the way, learning data analytics doesn't have to be a daunting journey. Turing College's curated distance learning programs allow you to upskill without leaving your day job.

Our comprehensive courses are crafted by industry experts from top tech companies like Amazon and Google. We prioritize one-on-one sessions to provide hands-on learning for future data analysts, data scientists, data engineers, and even digital marketers!