5 impactful data analytics skills non-tech professionals can harness in their roles

Data is everywhere, shaping the way businesses make decisions, streamline operations, and stay ahead of the competition. Moreover, as businesses increasingly rely on data to inform decisions, the demand for professionals with data analytics skills continues to grow. 

But you don’t need to be a data analyst or data scientist to benefit from data analytics. Even if you’re in a non-technical role, understanding how to work with data can give you a serious edge in your professional life and complement your existing skillset. 

By learning a few key analytics skills, you can identify key trends and insights, improve workflow efficiency, and drive better business outcomes. In this blog, we’ll break down the top five technical data analytics skills that non-tech professionals can learn today, plus practical ways to apply them in your day-to-day role.

1. Excel and spreadsheet modelling

Microsoft Excel is a powerful tool that every professional should have in their data analytics toolkit. Many of us may already have a basic understanding of how to use Excel for structuring and analysing data. However, despite being widely used, many people only scratch the surface of what Excel can do. Mastering Excel and spreadsheet modeling can enable non-tech professionals to perform advanced calculations, create data models, and manipulate large datasets with ease.

Knowing how to effectively use Excel’s features can dramatically increase productivity and efficiency. Functions such as VLOOKUP, pivot tables, and conditional formatting allow non-technical professionals to analyse data quickly and draw insights that would otherwise be time-consuming or difficult to achieve.

How non-tech professionals can use it:

• Financial Analysts: Excel is essential for creating financial models, tracking budgets, forecasting, and managing large datasets related to finance. Financial analysts can use advanced Excel functions such as VLOOKUP, INDEX-MATCH, and pivot tables to analyse financial statements and generate reports. They also leverage scenario analysis and Monte Carlo simulations to assess risk and predict financial outcomes. 
• Project Managers: By using Excel, project managers can track key project metrics, timelines, and performance indicators in an easy-to-use format. Gantt charts, created in Excel, help visualise project schedules and dependencies, enabling better time management. Conditional formatting and data validation also ensure that milestone tracking is accurate and up-to-date. 
• Sales Teams: Excel can be used to track sales performance, monitor lead generation, and manage customer relationships more effectively. Excel’s ability to integrate with CRM tools like Salesforce allows for streamlined data management, helping sales professionals make data-driven decisions and improve overall sales strategies.

How you can start learning it:

• Learn key functions such as pivot tables, advanced formulas, and data manipulation through free resources like YouTube tutorials or paid platforms like LinkedIn Learning.
• Use sample datasets from your work or publicly available datasets to practice building models and performing data analysis.

2. Basic SQL (Structured Query Language)

SQL (Structured Query Language) is the standard programming language used to manage and manipulate relational databases. While learning SQL in-depth might require some technical expertise, non-technical professionals can still benefit from understanding the basics. SQL allows users to query databases to retrieve, filter, and analyse data.

Understanding basic SQL can help non-tech professionals access and query company databases directly, eliminating the need to rely on technical teams for simple data retrieval tasks. It is especially useful for roles that involve regular interaction with large datasets or customer information.

How non-data professionals can harness it:

• Sales Professionals: Sales teams can use SQL to query customer databases, identify high-value prospects, and analyse sales performance by segment. SQL queries such as SELECT, WHERE, and GROUP BY can be used to filter customer records, determine prospects more likely to buy, and segment customers based on purchase history. 
• Marketing Professionals: By querying customer data and campaign metrics, marketing teams can gain deeper insights into customer behaviour and optimise their marketing strategies. For example, using INNER JOIN and LEFT JOIN commands, marketers can combine website activity data with CRM data to understand how different customer segments interact with marketing campaigns. 
• Operations Managers: Operations professionals can use SQL to track inventory levels, monitor supply chains, and analyse operational efficiency. When writing queries to check stock levels, operations teams can use the COUNT and SUM functions on inventory tables to ensure they have optimal stock levels to meet demand.

How you can start learning it:

• There are free platforms like Codecademy and W3Schools where you can learn SQL fundamentals with video tutorials and hands-on exercises provided.
• Practice writing simple queries to retrieve data from datasets. You can use sample data from websites like Kaggle or work with your company’s database