79.8% of organizations cite cultural barriers to data adoption, yet AI and data literacy rank at only 1.6% in the CDO’s list of priorities.
I find the research from New Vantage Partners, headed by industry legends Tom Davenport and Randy Bean, incredibly valuable. Their annual “Data and Analytics Leadership Annual Executive Survey” series delivers invaluable insights into how organizations are trying to get value from their data…and I mean trying.
Their 2022 report was stunning in surfacing that organizations were failing in their attempts to become data-driven. The survey highlighted that between 2019 and 2022, nearly 10% of the surveyed organizations had given up on their journeys to become data-driven (Figure 1).
Figure 1: “2022 Data and Analytics Leadership Annual Executive Survey” Takeaways
Now, from their 2023 report, we get the following opening statement:
“Yet it would appear that too much of the focus of data executives is on non-human issues — data modernization, data products, AI and ML, data quality, and various data architectures. Less than 2% of respondents ranked “data literacy” as their top investment priority. Could it be that we are leading the horse to water, but it isn’t drinking? Perhaps this accounts for the low level of overall success of the CDO/CDAO function found in the survey.”
The Chief Data Officer (CDO) is focused on keeping up with new technological advancements like Generative AI and Large Language Models, which are in their natural comfort zone. However, they overlook an even more critical factor in achieving data-driven adoption: organizational AI and data literacy. Due to this, they are failing to educate and prepare the organization to participate in and take advantage of the economic potential generated by the AI and data revolution.
And the impact? Well, the facts speak for themselves:
- Fact A: The importance of the CDO role continues to grow. The role has achieved an industry “critical mass,” with 82.6% of organizations now having a CDO.
- Fact B: Unfortunately, the CDO’s success is ambiguous. Only 40.5% of companies report that the CDO role is well-understood within their organizations, while only 35.5% of organizations responded that the CDO role is successful and well-established – an 11.6% decline from last year.
- Fact C: Becoming data-driven and building a data culture is still elusive for most organizations. Only 23.9% of companies characterize themselves as data-driven, and only 20.6% say they have successfully implemented a data culture.
- Fact D: 79.8% of CDOs cite cultural issues as their biggest challenge to realizing business value, reflecting that change is seldom easy and organizational transformation tends to move slowly.
- Fact E: Of the CDO’s primary areas of investment, data literacy ranks near the bottom in CDO importance at 1.6%.
Value Creation Success Equation Summary: The importance of the CDO is increasing, but their effectiveness is unimpressive. Their biggest impediment to success is cultural issues, not technology, but they prioritize technology investments over cultural investments.
Sorry, but the facts don’t add up (Figure 2).
Figure 2: 2023 Data and Analytics Leadership Annual Executive Survey” Takeaways
As organizations and their CDO leader struggle to get value from their data, the underinvestment in AI and data Literacy dooms these organizations’ data-to-value journey before it has even started. And throwing new technologies at the organization just exacerbates the cultural challenges.
Your data-to-value journey must start with prioritizing organization-wide AI and data literacy. And I know just how to begin that AI and data literacy program.
Addressing the AI and Data Literacy Challenge
I wrote “AI & Data Literacy: Empowering Citizens of Data Science” because I noticed significant organizational and societal challenges in instilling an AI and data literacy culture. The industry needed some pragmatic, no-hype educational material to ensure everyone understood the roles, responsibilities, and rights in ensuring that AI and data were used to deliver more relevant, meaningful, responsible, and ethical outcomes (Figure 3).
This AI and data literacy framework covered in my book is comprised of these components:
- Data & Privacy Awareness chapter discusses how your data is captured and used to influence and manipulate your thoughts, beliefs, and subsequent decisions. This section also covers personal privacy and what governments and organizations worldwide do to protect your data from misuse and abuse.
- AI & Analytic Techniques chapters focus on understanding the wide range of analytic algorithms available today and the problems they address. We will dive deep into how AI models work, the importance of determining user intent, and the critical role of the AI Utility Function in enabling the AI model to continuously learn and adapt. This chapter will also explore AI risks and challenges, including confirmation bias, unintended consequences, and AI model false positives and false negatives.
- Making Informed Decisions chapter explores how humans can leverage basic problem-solving skills to create simple models to avoid ingrained human decision-making traps and biases. We will provide examples of simple but effective decision models and tools to improve the odds of making more informed, less risky decisions in an imperfect world.
- Predictions & Statistics chapter explains basic statistical concepts (probabilities, averages, variances, confidence levels) that everyone should understand (if you watch sports, you should already be aware of many of these statistical concepts). We’ll then examine how simple stats can create probabilities that lead to more informed, less risky decisions.
- Value Engineering Competency chapter provides a pragmatic framework for organizations leveraging their data with AI and advanced analytic techniques to create “value.” We will also provide tools to help identify and codify how organizations create value and the measures against which value creation effectiveness will be measured across a diverse group of stakeholders and constituents.
- AI Ethics chapter explores how we integrate ethics into our AI models to ensure the delivery of unbiased, responsible, and ethical outcomes. We will explore a design template for leveraging economics to codify ethics that can then be integrated into the AI Utility Function that guides the performance of the AI models.
- Cultural Empowerment chapter focuses on creating a culture of empowered “Citizens of Data Science.” We will focus on empowering individuals and teams to embrace and leverage a wide range of viewpoints, giving them the understanding and comfort level for exploring where and how AI and data can empower their personal and professional lives.
Summary: Data to Value Journey Starts with Data Literacy
The challenge continues: how do we establish a culture of data-driven decision-making and value creation?
Unfortunately, it’s too easy for CDOs and other technology-savvy executives to stay in their comfort zones and focus on the latest technology innovations. It’s fun. It’s cool. It’s safe. Unfortunately, this tendency gets in the way of creating the culture necessary for organizations to take full advantage of the latest technological innovations.
As the New Vantage Partners research highlighted, CDOs must push themselves out of their comfort zones to lead the change necessary to create that data-driven, value-centric culture necessary for leveraging AI and data to deliver more relevant, meaningful, responsible, and ethical outcomes.
To create this culture, it’s necessary that everyone in the organization – and society as a whole – understands their role, responsibilities, and rights in determining where and how they can use AI and data for their personal and professional development. AI and data literacy is the starting point and foundation for creating that culture.