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Want a Data-Driven Organization? Start with Your Talent Strategy

What sets a data-driven organization apart? Evidence suggests better revenue and customer value. Data-driven organizations are better equipped to make decisions and take the right actions. Today's generative AI is driving a data revolution. Recent evidence suggests that the percentage of data-driven organizations recently doubled, an increase greater than at any time before. But, unlocking the full potential of what is possible requires a talent strategy tightly aligned with your company's data analytics strategy. Executives with the right analytics infrastructure and the right talent in the right place have a significant competitive advantage. To avoid falling behind and making costly mistakes, here are two essential talent strategy steps you need to take now.

Talent Strategy Step #1: Identify the right analytical skill sets

After establishing your data-analytics strategy that is tightly aligned with the mission and culture of the ogranization you need to determine the roles and the knowledge, skills and abilities of the talent most critical to meeting the needs.

Analytical skills include more than the obvious need for technical competence with applications for modeling, forecasting, and statistical analysis, such as SPSS, R, and Python. Analytical skill sets also need to include:

  • negotiating

  • consulting

  • communication

  • developing others

  • quantitative analysis

Also, organizations need analytical leadership at every level, not just in the CTO or IT department. In data-driven organizations, leaders need to:

  • possess a passion for data analytics

  • develop other's analytical capabilities

  • set strategy with analytic performance metrics

  • seek out and exploit quick wins for analytics

  • take a long-term view of analytics

  • grow their analytical networks

  • work across the business

Leaders and employees with the right skills are shaping the future of the workplace. There is a high demand for employees with data analytics skills, and it is very challenging to source, recruit, and retain those who possess these analytical attributes.

The World Economic Forum suggests that as the utlization of technology increases the in-demand skills across jobs change over the next five to 10 years will continue to shift. The table below shows the expected employee skills from 2015 to 2030.

Having the right talent strategy begins with getting clear on the analytical skills your organization need to support the organizations culture and data-analytics strategy so you can effectively source and develop the best and most creative talent.

Talent Strategy Step #2: Align your analytical organization

An organization's culture and having enough of the right talent with the right skills in the right places is essential.

Architecting culture is an essential activity for leaders. Having an analytical orientation within the organization's culture is vital to building a successful analytical organization. An organization's perceived value associated with analytics directly influences decisions on the best way to align analytical resources across the business.

The following are six high-impact and low-cost culture levers leaders can pull to build an analytical cutlure orientation.

Cutlure Lever #1: What leaders pay attention to regularly.

This is one of the most potent mechanisms every leader has in your company. What leaders choose to measure, reward, and control matters, and the opposite is also true. For example, a great starting point is to ask leaders what data they use to make decisions. By asking the question, you reinforce the importance of data-driven decision-making.

Culture Lever #2: How leaders react to critical incidents.

Much can be revealed when a business or a leader faces a significant challenge. Mike Tyson said, "Everyone has a plan 'til they get punched in the face." These crucible moments in business are like a refining fire. It is the heightened emotional intensity that increases individual and organizational learning.

Culture Lever #3: How leaders allocate resources and control costs.

Budgets reveal a lot about the organization's assumptions and beliefs. Resources include physical assets such as equipment, tools, and human resources. What gets resourced gets reinforced. Leaders should consider what tools and resources employees have available for data analytics.

Culture Lever #4: Deliberate role modeling and training.

How leaders act and behave outside of training is more significant than what is said or demonstrated within leadership development events. Leaders looking to build an analytical cultural orientation would benefit by explaining to and showing the organization how they use data to make decisions on a routine basis.

Culture Lever #5: How leaders allocate rewards.

Rewards and recognition come in many different forms. Also, what is considered a reward varies from person to person. What gets rewarded, how it gets rewarded, and what does not reinforce organizational culture. There are tangible rewards and social rewards. Simply saying thank you for presenting a decision using data analytics is a social reward.

Culture Lever #6: How leaders recruit, promote, and excommunicate.

 Who gets hired, promoted, and fired, and for what creates and reinforces organizational culture. Talent management decisions can be viewed as a more subtle nuance to culture change because decisions are influenced by explicitly stated criteria and unstated value priorities. A leader looking to influence an analytical cultural orientation would benefit from assessing the skill sets needed within the organization and then hiring based on those skills.

Having a critical mass of analytical talent across the organization creates a tipping point. The following is a simple tool you can use to perform an organizational evaluation. You can then use the results of this evaluation to set hiring, development, and succession planning activities in support of your strategy.

The evaluation involves counting the number of analytical talent resources across your organization and assessing their depth of analytical capability within three categories of tasks:

  • Level 1: capable of workbench, standard reports, and alerts

  • Level 2: capable of multidimensional analysis, analytical applications, and data visualization

  • Level 3: capable of what-if planning, predictive modeling, and statistical analysis

Note: This example is adapted from Davenport et al. (2010). It uses a talent competence scale rating from basic to advanced.

Once you can visualize the organization's analytical talent structure, capacity, and capability, it is easier to leverage talent strengths and address opportunities.

The organizational design challenge is placing the analytical resources close enough to the business to focus on the most critical initiatives while still enabling mutual learning across the analytical resources. This organizational design decision needs to take into consideration the organization's analytical culture orientation and maturity.


Abina, A., Salaj, A., Cestnik, B., Karalič, A., Ogrinc, M., Lukman, R., & Zidansek, A. (2024). Challenging 21st-Century Competencies for STEM Students: Companies’ Vision in Slovenia and Norway in the Light of Global Initiatives for Competencies Development. Sustainability. 16. 1295. 10.3390/su16031295.

Bughin, J., Hazan, E., Lund, S., Daholstrom, P., Wiesinger, A., & Subramaniam, A. (2018, May 23). Skill shift: Automation and the future of the workforce. McKinsey Global Institute.

Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at work: Smarter decisions, better results. Harvard Business Press. MA.

Deloitte. (2019). Deloitte survey: Analytics and data-driven culture help companies outperform business goals in the age of with’.

Grossman, R. L., & Siegel, K. P. (2014). Organizational models for big data and analytics. Journal of Organization Design (Aarhus), 3(1), 20-25. (2020, November 23). Analytical skills: definitions and examples. Indeed Career Guide.

Schein, E. H. (2004). Organizational culture and leadership (3rd ed.). Jossey-Bass.

Tambe, P. (2014). Big data investment, skills, and firm value. Management Science, 60(6), 1452-1469.

Wallace, D. (2022). How Data Maturity and Product Analytics Improve Digital Experiences and Business Outcomes. IDC Research.


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About Dr. Jeff Doolittle

He is the founder of Organizational Talent Consulting in Grand Rapids, MI, and Program Director of online graduate and continuing business education at Olivet Nazarene University in Bourbonnais, IL. Executive leaders who work with Jeff describe him as thoughtful, decisive, intelligent, and collaborative. Jeff is a business executive with over twenty years of talent development and organizational strategy experience working with C-suite leaders in Fortune 100, Forbes top 25 private, for-profit, non-profit, and global companies in many industries.

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