In our modern era, data is a non-negotiable business asset. From tracking materials and product inventories to customer profiles, accounting, and budget strategy, businesses in all industries benefit from data analysis. With the right analysts on your team, you can harness data more efficiently and outpace your competitors. That's why learning how to hire data analysts is vital.
But finding the right talent to drive your company's data initiatives, including AI and machine learning, can be a challenge. Additionally, leaders must choose between building an in-house team, working with contractors, or partnering with a vendor for outsourcing. Which model is the best fit for your needs?
In this guide, we're exploring everything you need to know about how to hire data analysts in 2025 and beyond:
In-House Data Analytics Teams
Building an in-house team for data analytics means hiring full or part-time employees, such as data scientists, data engineers, and data analysts. (Although we're focusing on data analysts in this article, you may discover a need for other roles to help build and manage your company's data infrastructure.)
While hiring in-house creates the potential for deep institutional knowledge and integration with your company's culture, it also requires significant investment and carries a certain degree of risk.
Pros of Hiring In-House
- Deep Contextual Knowledge: Once on-boarded and integrated into your company, an in-house team will possess an intimate understanding of your products and services, culture, and strategy.
- Direct Control: You'll maintain direct control over your in-house employees' priorities, including project roadmaps, as well as data governance.
- Stability and Long-Term Growth: A permanent in-house team can grow with your company, adding strategic value as they become more invested in their roles over time.
Cons of Hiring In-House
- High Cost: In learning how to hire data analysts, you'll discover that these roles are highly sought-after and generally require a high annual salary. Between annual salaries, benefits, and recruitment fees, hiring a permanent data analyst can cost between $150,000 and $200,000 in the first year alone.
- Slow Time to Hire: Because the talent pool for data analysts is so competitive, the recruitment process can be lengthy and difficult. Top candidates often have multiple offers.
Skills Bottleneck: You will also need to invest in frequent training for your in-house team, as rapidly evolving fields like MLOps and agentic AI require them to maintain cutting-edge skills.
Staff Augmentation: Outsourcing Data Analysts with Control
Due to the potential downsides of hiring in-house, many companies opt for staff augmentation via outsourcing. Staff augmentation is a modern, flexible approach that integrates external experts with your existing team. It provides specialized data analyst expertise that scales to your needs.
Pros of Staff Augmentation
- Speed: Partners like the JADA Squad offer highly specialized data and AI talent that can be added to your organization within a short timeline that's much faster than traditional recruitment.
- Scalability: You can easily scale your team up for a major project or reduce hours after a deliverable is complete, making budget planning predictable.
- Cost-Efficiency: Rather than investing in full-time employees, organizations like JADA provide access to data analysts and other experts at a fraction of the cost.
- Specialized Expertise: Outsourcing also provides immediate access to domain-specific expertise, such as GenAI implementation, without the high costs and time investments of training your in-house team members.
Cons of Staff Augmentation
- Integration Efforts: Although outsourced talent is highly specialized and trained, you'll still need to ensure that they can integrate effectively within your organization. However, a strong partner offers workflows to streamline this process.
Variable Quality: If you outsource via partners who don't specialize in data analysis, you may receive inconsistent quality work. That's another reason why it's imperative to choose a partner like the JADA Squad, which offers talent with deep technical knowledge and Data and AI expertise.
Choosing the Right Data Talent Hiring Strategy
Ultimately, choosing between an in-house hire and staff augmentation boils down to balancing cost, speed, and specialization. While traditional hiring may be right for some organizations, it's often slow, expensive, and subject to intense competition. Additionally, companies risk losing significant investment if their in-house talent leaves.
Due to its cost efficiency and flexibility, a staff augmentation approach is generally the best approach. It combines the flexibility of outsourcing with the expert-level skills of full-time employees. By rapidly augmenting your team when you need it most, you can lower costs and improve output.
When to Hire a Data Analyst
If you're wondering how to hire data analysts, you should also ask yourself when to hire data analysts. The answer to this question varies depending on your company's level of data maturity, as well as your current projects and strategic goals.
Consider hiring a data analyst when:
- Your data pipelines are disorganized
- It's inconsistent or inaccurate
- Data is scattered across multiple systems
If your organization struggles with disorganized data and you're unable to use your data effectively, bringing a data analyst on board will help in a variety of ways.
The right data analyst will perform data collection, cleaning, and use statistical methodologies and tools to identify patterns within your organization's data. Then, they can communicate their findings to your team and your stakeholders, providing strategic guidance to help your organization grow.
Accelerate Your Data Analysis with JADA
Are you ready to accelerate your data-based strategies? The JADA Squad can help you determine how to hire data analyst talent that's the perfect fit for your organization. Don't let the challenges of traditional staffing hinder your growth.
Contact us today to learn more
Frequently Asked Questions
How to hire a data analyst?
Start by defining the specific problems you want them to solve and the tools your data analysts will need to use. Look for candidates with strong SQL, Excel, and visualization skills who can turn raw data into actionable insights. It's also recommended that you have potential candidates undergo a skills test or sample project to gauge their capabilities.
What are the 4 types of data analysis?
The four main types of data analysis include descriptive, diagnostic, predictive, and prescriptive analysis. Descriptive shows what happened, diagnostic explains why, predictive forecasts what might happen next, and prescriptive recommends what actions to take. When looking to hire a data analyst, make sure your candidates have a thorough understanding of these methodologies.
What is the 80/20 rule in data science?
The 80/20 rule suggests that data scientists spend about 80% of their time cleaning and preparing data, and only 20% analyzing it. It highlights how much effort goes into ensuring data quality before insights can be trusted.
What is the biggest risk of building an in-house data analytics team?
The biggest risk of hiring data analysts in-house is the high cost and long hiring process. Due to the scarcity of top talent, hiring internally can take months and create significant bottlenecks. Additionally, making sure that your team's skillset remains competitive will require significant investment.
What key skills should I look for when I hire a data analyst?
When you're interested in hiring a data analyst, look for strong proficiency in SQL and visualization tools such as Tableau or Power BI. Additionally, data analysts need excellent communication skills to translate data findings into clear, strategic business recommendations.
What is the main difference between a data scientist and a data analyst?
While there is often overlap between these roles, a data analyst focuses on explaining what happened. They use historical data, in-depth reports, and CMS dashboards to create actionable insights. A data scientist, on the other hand, uses advanced statistical models and machine learning to extrapolate insights about what will happen based on the gathered data.
