What kind of growth opportunities can be offered to data/analytics talent? How do we keep them in the org?

Data & AnalyticsTalent Management & Performance
270 viewscircle icon2 Comments
Sort by:
Director of Data in Manufacturing2 days ago

A common misconception in corporate development is viewing it solely as a hierarchical ladder, where advancement is equated with promotions to management roles and corresponding salary increases. This narrow perspective often results in losing valuable technical experts who are pushed into managerial positions they may not be suited for. To foster sustainable growth and retain top talent, companies should establish parallel development paths for technical roles—such as Principal Engineer, Lead Specialist, or Reference Expert—that offer recognition, career progression, and competitive compensation without requiring a shift into management.

Director of Data and Analytics in Miscellaneous24 days ago

Most DS/Analytics people want to upskill. I have found that supporting this to be the most effective growth opportunity as it tells them that we are interested in their growth and future development.

Content you might like

In your experience, what roles are best positioned to act as executive sponsors for AI governance?

Chief Data/Analytics Officer28%

Chief Executive Officer19%

Chief Information Officer 46%

Chief Risk Officer18%

Chief Technology Officer30%

Chief Compliance Officer19%

Chief Operating Officer10%

Chief Human Resources Officer9%

General Counsel or Chief Legal Officer16%

Other (please share in the comments)2%

None

View Results

What are the fundamental leadership capabilities that are needed for an era of exponential technology and AI innovation?

Read More Comments

What legacy AI systems are still in use at your organization? Select all that apply

Rule-based expert systems for decision-making20%

Traditional machine learning algorithms (e.g. linear regression, decision trees)41%

Systems requiring manual feature engineering38%

Early natural language processing (NLP) systems (relying on rule-based approaches)46%

Legacy AI hardware (hardware architectures/specialized AI chips not suitable for deep learning models)24%

Early chatbots and virtual assistants (lacking natural language processing)16%

Other2%

None

View Results

Any suggestions for metrics that ensure data analytics teams are meeting organizational needs? How are you tracking your team’s performance?