Continual learning and data literacy at the organizational level

Advanced analytics and AI teams can do great work and deliver impressive models, but if the front-line workforce is not trained, upskilled, and directed to implement and use the new processes, models, and insights, then it is all for naught:

As AI tools become easier to use, AI use cases proliferate, and as AI projects are deployed, cross-functional teams are being pulled into AI projects. Data literacy will be required from employees outside traditional data teams—in fact, Gartner expects that 80% of organizations will start to roll out internal data literacy initiatives to upskill their workforce by 2020. [8]

In Gartner's third annual chief data officer survey, respondents said that the second most significant roadblock to progress with data and analytics is poor data literacy, rooted in ineffective communication across a wide range of increasingly perse stakeholders. Data and analytics leaders must learn to treat information as a second language and data literacy as a core element of digital transformation. [9]

In a previous role, our team built and delivered an analytical model to a functional business unit. The application was delivered in multiple phases; each phase took from 3 to 5 months. Each phase delivered stand-alone functionality that was intended to be used immediately by the business analysts. During the first year, the analytics team was receiving questions about the applications and requests for additional and improved functionality. Also, we received continual positive feedback about the application and the value that it delivered.

When the business team was asked to present the application to new management, the team admitted that they dabbled with the application, but never used it to actually change how they managed the relevant processes or made decisions based on the insights produced by the descriptive, predictive, and prescriptive models in the application.

The analytics team was surprised to say the least and the new management team questioned the value of the entire effort. In the end, the new management dictated that the new application be an integral part of the new process and that the team employ the insights as part of the revised decision-making process.

Organizational change can be hard, and, in most cases, it needs to be mandated to ensure that staff members follow through to leverage innovations and developments.

In Chapter 10, The Future of Analytics – What Will We See Next?, we expand upon the concept of and need for data and algorithmic literacy, innovation, and the widespread use of a broader set of analytic approaches. A small percentage of innovative companies are exploiting data and analytics to the fullest and those companies are utilizing a narrow set of algorithms and technologies. We will all be better served by expanding the implementation of data and analytics in a manner that will engender trust and bring analytics into the mainstream of technology and everyday use.