Building Internal Data Analytics Talent
Data is the foundation of digital transformation, and the skilled talent is hard to find. A McKinsey report predicted that by 2018, the US 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 is true across industries, including government. During my recent tenure as Chief Strategy Officer, Innovation and Technology, at the State of Illinois, we implemented a 3 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, and
- Promoting a data centric culture
This increases skills 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.
By keeping a ‘big tent’ definition of analytics, connecting practitioners of all levels helps build everyone’s skills. This is also a communication and education channel for the standards and best practices of the State Data Practice.
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.
Grouping units that have common or overlapping data needs 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.
