Big Data, Big Problem: Coping With a Shortage of Talent in Data Analysis

What is data analysis?

Broadly defined, data analysis is the systematic application of statistical techniques to a collection of information to gain insight. Put simply, it’s the process of collecting and interpreting data to uncover trends, answer questions and guide business decisions. The typical data analysis process consists of five distinct steps:

1. Identify the objective.

What are you trying to learn or improve? You can’t get meaningful answers without first defining the question. Do you want to analyze sales figures, revenue streams, inventory or customer retention rates? It’s impossible to create a reliable, data-driven process, such as a decision support system, without identifying the topic upfront. As a business owner, you may already know what needs attention, or you might rely on others in your company to make that determination.

2. Collect the data.

Once you know what you want to measure, the next step is gathering the right information. Analysts collect data either internally or externally. Internally, this may include surveys, product data or insights from one of the best CRM software tools, which house valuable customer information. External sources may include market reports, public records or third-party research.

3. Clean the data.

Data preparation is a critical yet time-intensive phase, partly because of issues like duplicate entries, errors and data decay, where information becomes outdated or inaccurate. Removing duplicates, correcting errors and standardizing data formats make up most of the work at this stage. It can be tedious, but companies can use automated tools or artificial intelligence to speed up the process.

4. Analyze the data.

Once the data is clean, analysts use various statistical and logical methods to examine it. The goal at this stage is to identify trends, outliers and patterns that can provide insight into a market, product or customer base.

5. Interpret the data.

The final step is connecting the analysis to real-world decisions. Did the data help answer the question you identified in Step 1? Did it provide insight into improving business operations? For example, big data in social media marketing can reveal what content your audience engages with most and help strengthen customer interactions. It’s often the data analyst’s job to present findings in a clear, easy-to-understand format, such as a report, data visualizations or a presentation, to help business leaders act on the results.

Why is there a shortage of data analysts?

Even as businesses urgently invest in data-driven decision-making, the talent pipeline isn’t keeping up. The World Economic Forum’s 2025 Future of Jobs Report shows roles such as big data specialists, AI and machine learning specialists and data analysts are among the fastest-growing jobs worldwide through 2030. At the same time, 63 percent of employers say skills gaps are their biggest obstacle to growth, underscoring how difficult it is to find the talent needed to move forward. 

Even as more students pursue data science degrees and training programs expand, there still aren’t enough qualified data analysts to go around. Here are four reasons why.

Highly skilled job

Being a data analyst requires a high level of technical skill. A fully trained analyst will have strong abilities in statistics, mathematics, programming, probability and data systems. However, while more people are enrolling in data science and analytics programs, the number of graduates still falls short of industry demand. According to a 2024 report from the American Statistical Association, master’s programs in data science produce roughly 2,400 graduates per year, while analytics programs have exceeded 4,600 graduates annually, underscoring the gap between supply and demand. 

The time it takes to gain and master these subject areas is one reason companies often look for candidates with an advanced degree. And whenever a master’s degree is a requirement, the pool of qualified applicants becomes much smaller.

Lack of experience

Data science is a relatively new career path, which is one reason there’s a significant shortage of experienced data analysts. The field only emerged as a distinct discipline in the early 2010s, meaning many professionals have less than a decade of specialized experience. It’s not enough to understand the theory behind data analysis; candidates need hands-on experience collecting, cleaning and interpreting data to solve a company’s unique challenges. 

Those new to the field often lack experience applying data analysis concepts to real-world business situations, making it harder for employers to find talent that can contribute at a higher level from day one.

Intense competition

As more companies adopt data analytics to guide decision-making, competition for qualified talent has become intense. In the Adastra 2024 Data Professionals Market Survey, 76 percent of organizations said there is a real shortage of data and analytics talent, a figure that climbs to 82 percent among large enterprises. With so many businesses prioritizing data-driven strategies, employers are often competing for the same limited pool of experienced analysts, making hiring more challenging than ever.

Lack of guidance

Any new career field tends to lack standardization and clear pathways for growth, and data analytics is no exception. While organizations like the Institute for Operations Research and the Management Sciences (INFORMS) offer credentials such as the Certified Analytics Professional (CAP), the industry still lacks widely recognized standards for qualifications and training. 

The field would benefit from a more unified framework to help develop, certify and guide the next generation of data analysts. A centralized body could also foster networking opportunities and create hiring pipelines — two things that are still limited in this field — making it easier for companies to find qualified talent.

How can you find data analytics professionals for your business?

Given the talent shortage, it can be challenging to hire data analysts for your company. The iCIMS Insights Workforce Report (March 2025) found that tech roles — including data-focused positions — take an average of 51 days to fill, which is 10 days longer than the overall labor market average. 

However, there are steps you can take to improve your chances of finding qualified workers. These strategies include reassessing how many data analysts your business truly needs and considering whether existing employees can be trained or upskilled to support data initiatives.

Hire from within.

Hiring externally is often more expensive and time-consuming than promoting from within, which can be a smart solution when qualified external applicants are scarce. Internal mobility supports both retention and business outcomes. According to LinkedIn research, employees with clear internal mobility opportunities stay with their companies 41 percent longer than those without. And organizations that prioritize internal mobility and skills development are 1.8 times more likely to perform better financially, according to Deloitte’s 2025 Global Human Capital Trends report.

An internal hire also already understands your company’s culture, systems and processes, which shortens the onboarding process and ramp-up time. However, there are a few steps to take for this option to succeed:

• Identify employees with the aptitude and interest to grow into a data analyst role.
• Provide the necessary training to build their skills. Because data analysis requires advanced capabilities, additional learning will likely be needed.
• Support their development. Consider partnering with online learning platforms or local universities that offer data analytics certificate programs, which typically take six to 12 months to complete.

With time, training and support, a current team member could develop into a highly capable data analyst who already aligns with your business.

Redefine the data scientist’s role.

It’s expensive to hire an entire team of data analysts, and the talent shortage doesn’t make it any easier to find multiple qualified people. With the median annual wage for data scientists reaching more than $112,000 in 2024, expanding a full in-house data team isn’t realistic for many businesses. 

Fortunately, you may not need a full team to build an effective analytics function. In many cases, a business can rely on one or two data experts supported by employees from other departments. Some tasks within the data analysis process can be delegated to team members with lighter technical skills, freeing your data expert to focus on higher-value work.

For example, if you have at least one data analyst who can determine the best approach for collecting data, a less-skilled employee can assist with executing the collection. The data analyst can then supervise a small team to clean data by correcting formatting issues, removing duplicates and checking for common errors. This approach — often referred to as data democratization — allows organizations to scale their analytics capabilities without proportionally increasing specialist headcount. 

In other words, instead of hiring multiple analysts to handle every step of the process, you can equip existing employees to support the work and reserve advanced tasks for your expert.

Merge external outreach and internal development.

Sometimes, the most effective way to build a capable data function is to combine external hiring with internal talent development. This approach works well for businesses creating a data analytics division from the ground up. Start by hiring one experienced data analyst with strong communication or leadership skills. This external expert can guide your strategy and help your company begin using data effectively without needing to hire a full team right away.

Here are some ways a single data analyst can collaborate with your current staff to meet your company’s data needs:

• Sales team: A data analyst can develop dashboards and automated reporting tools that help your sales team track key performance indicators in real time, identify customer trends and adjust their sales strategies using data-driven insights.
• Customer service: Customer service teams capture valuable feedback daily. A data analyst can help this department use that information to spot product or service issues, track customer sentiment and identify recurring pain points.
• Human resources: The hiring process generates a large volume of data. A data analyst can work with HR to interpret candidate data, streamline recruiting and improve hiring efficiency with more informed decisions.

Define your business objectives.

Data analysis is a broad field, so it’s important to be clear about how you want to use data to improve your operations. Start by conducting a data maturity assessment to understand your current capabilities and identify specific gaps in your analytics function. This step helps you focus your hiring efforts on the right type of data expertise.

For example, if your goal is to improve customer retention, look for a data analyst with experience working with customer service data or conducting customer surveys. Create a detailed job description that outlines the tools, programming languages and industry experience you need. This helps attract candidates who are genuinely qualified for your specific needs, rather than generalists who may not be the best fit.

By hiring for the skills that match your business goals — instead of searching for a one-size-fits-all data analyst — you’re more likely to find the right person for the job.