Building Internal Data Analytics Talent: Digital Transformation in a Tight Labor Market

Data is the foundation of digital transformation, and skilled talent is hard to find. A McKinsey report predicted that by 2018, the United States will be short by 140,000–190,000 people with "deep analytical skills" and 1.5 million managers with expertise in understanding and using their work.



This information is true across industries, including the government. During my tenure as chief strategy officer, innovation and technology, at the State of Illinois, we implemented a three-point plan to build data analytics capabilities across the organization.

1. Centralized team: State Data Practice. Improves enterprise data analytics capabilities by:

• Providing enterprise best practices and standards to increase the maturity of data use and governance


• Supporting the creation of agency-level data practices in the business units working with but not a part of IT
• Promoting a data-centric culture

This skill increases by driving to and leveraging standards.

2. Distributed/horizontal team: Analytics Center of Excellence. A virtual team of people across the enterprise sharing expertise, lessons learned, policies, solutions, training, and tools to achieve better business results.

3. Vertical team: Innovation Incubator (i2). Develops a data ecosystem to support comprehensive analysis—360-degree views—of populations, programs and providers within and across each vertical of state government (health and human services, public safety, business and workforce, education, transportation, and natural resources) to drive more efficient, effective, and customer-focused state government.

By keeping a 'big tent' definition of analytics, connecting practitioners of all levels help build everyone's skills. This communication and education is a channel for standards and best practices of state data practice.

Grouping units that have common or overlapping data need exponentially increases the value of all analytics activities and skills. This meant creating a master data sharing agreement between agencies that is now held up as a model at the Federal level.

It is a top down, bottom up and within a vertical approach. All of these are up and running, and creating enterprise level analytics capabilities and value.

Originally published on LinkedIn.

Marian Cook is currently a solutions principal for Slalom Consulting, as well as the head facilitator for MIT's blockchain certification course and a strategic advisor to the Chicago Blockchain Center. Immediately prior, she was the chief strategy officer for Innovation and Technology for the State of Illinois, having moved from the private sector to public service in 2015.

She started as a systems engineer with IBM, re-engineering processes, implementing systems, and creating business and technology strategies. Moving to international consulting firms, she worked globally, developing business growth and turnaround strategies, as well as the client side as the head of IT for a top healthcare organization.

Opinions expressed by the author are not necessarily those of WITI.