Maximizing your Return on Data Science

It is increasingly important to question every data investment decision you make.

As a business owner, are you pondering these four questions?

1. Is your organization extracting maximum value from the investments you make in data science?
2. Are you seeing the returns from your most valuable resources, your data scientists?
3. Is your lack of data fitness holding back these most critical resources?
4. Most importantly, how will you consistently earn the profits you deserve from the investments you’ve made?

In today’s AI-driven landscape, the return on data science and your ability to protect your resources could very well determine whether you power ahead or fall behind. Your data science team is building YOUR future and helping to deliver on your strategic bets. Anything that makes them more productive maximizes your overall performance.

The Great Data Science Talent Shortage

As demand for data science booms, there’s a talent war out there for the best resources available. Recruiting top talent is only going to get harder in the months ahead.

According to the US Bureau of Labor Statistic: Data Scientists : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics

• Employment of data scientists is projected to grow 36 percent from 2023 to 2033, much faster than the average for all other occupations.
• On average, about 20,800 openings for data scientists are projected for each year over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force through retirement.

Market forces always dictate that when demand for limited resources is up, salary expectations will follow. You can see this most recently in AI talent grabs happening at the top levels of Meta and OpenAI. For any business not operating in that stratosphere (which is basically everyone – even the Fortune 500), it’s consequential to know if you will get the return on the investment in data science human capital, and that ties directly to them having the right resources they need to deliver.

According to a report from Indeed, the average data scientist base salary is $126K/year (as of March 31, 2025). However, the Department of Labor notes that the role’s 75th percentile salary is $155K/year and the 90th percentile salary is $195K/year.

For organizations that have managed to secure these resources, the pressure to retain and engage the talent is tremendous. And what’s the biggest drain on a data scientist’s output and their least satisfying daily task? It’s often the arduous process of data preparation and the struggle to access trusted, usable data.

The Hidden Costs: Data Wrangling vs. Data Science

Consider the following:

Multiple researchers in recent years have found that 60 to 80% of a data scientist’s time can be spent collecting, organizing, and cleansing data. As data science morphs from a science project to a strategic bet, your team needs help.

Even if just 10% of the time your team takes to prepare data could be improved, it could have a huge impact across a team of ten! If nearly half of your most valuable data resources’ time is spent simply finding and preparing data, the opportunity costs are enormous.

This isn’t just about wasted salary; it’s about missed innovation, delayed insights, and slower payback for your AI initiatives. With a scarcity of talent, you cannot afford to simply hire your way out of it; you can’t throw more people at the problem.

The impact of data wrangling can be seen in the following table. If your organization is in the 60-80% realm, start at the top and consider what significant improvements are worth. Like any maturity model, different organizations will all have different starting points. If you’re not in that worst case, consider another baseline, but know that a 15 percent productivity gain across a team of just 10 is valued at over $200,000 yearly! Let’s look at the hypothetical cost for a single data scientist:

• Annual Compensation: $155,000 (using the 75th percentile)
• Estimated Time Spent on Data Preparation: 70%
• Annual Cost of Data Preparation (in terms of data scientist time): $155,000 * 0.70 = $108,500
• Impact on a team of 10 – Over $1M!

Summary

While this is a simple “back of the envelope exercise,” it is a compelling thought exercise the implications are clear; investing in the right data management software could potentially save your organization up to $77,500 per year for each data scientist on your team. The dividend on your investments can be reinvested back and compound over time.

Ready to maximize the return on both your invaluable data and your most important human capital – your data scientists? Discover how Pentaho can help you automate data pipelines, easily prepare data on the fly, and enable your organization to become truly data fit.

Click to request a demo or learn more about the potential of the Pentaho platform to help drive your data science success.