How To Embrace Data And Change?
The report on Chief Data Officers (CDOs) for 2024, conducted by AWS, highlights several key findings relevant to the evolving role of CDOs, particularly in the context of generative AI, business value creation, data governance, and the challenges of fostering a data-driven culture [1]. When reading the following key takeaways, think about how L&D could play a role in the organization’s success in supporting the CDO.
How L&D Can Support Chief Data Officers
Key points include (if you ask ChatGPT):
- Visible business value creation
CDOs are increasingly focused on demonstrating the tangible value of their initiatives, primarily through analytics and AI. - Generative AI
There’s strong enthusiasm among CDOs for the potential of generative AI despite it being in the early stages of adoption. The emphasis is on integrating AI without sidelining existing data initiatives. - Data quality
Identifying appropriate use cases and maintaining data quality are significant challenges for leveraging generative AI effectively. - Data Strategy
A robust data strategy and governance are critical for generative AI success, with many CDOs working on adapting their data strategies to support AI initiatives. - Creating a data-driven culture
Transforming organizational culture to be more data-driven is a major challenge, but is essential for the effective use of data and AI technologies.
As for me, my interest is always about what stakeholders care about most and what they get excited about. Once you know what matters to them (in this case, CDOs), you can work backward to how you can help. Supporting stakeholders to deliver on their vision and enthusiasm is an effective way of showing value. Waiting until someone identifies a learning or training need is often too late.
CDOs are excited about the possibilities of generative AI, even though their companies are mostly experimenting with it. They feel that data and data strategy will be critical to success with generative AI, and they are in the early stages of that transformation. Other topics that are a top priority for CDOs include data governance and cultural change toward a data-driven organization. The savviest CDOs are prioritizing change management, communication, and evangelism, and they consider making other executives successful as critical to their success in the role.
Supporting The Vision
Based on the surveyed Chief Data Officers, the following items stood out for me (bolded above):
Overall Goal: Making Other Executives Successful
They do this by focusing on:
- Data (literacy) and data strategy
- Data governance
- Data-driven organization
- Change management
- Communication
- Evangelism
Now that we have identified the targets, the next step is to explore the barriers. If these were simple things to do, CDOs would already have done it. Therefore, there must be certain barriers or challenges the CDO will need help with.
Challenges Holding Chief Data Officers Back From Achieving These Goals
CDOs currently face multiple challenges to deliver on these key points. Interestingly, not all of these barriers involve technology or data. What common pattern do you recognize in the following barrier list they mentioned?
- Difficulty in changing organizational behaviors and attitudes
- Absence of data-driven culture or data-driven decision-making
- Insufficient resources to accomplish goals
- Lack of data literacy or understanding
- Unclear or overly broad job definition
- Lack of support from other senior executives
- Rapidly-changing technologies such as generative AI
The top two barriers are about change management and culture. Combine them with the fourth about data literacy, and you can see the opportunity for L&D to support enterprise-wide success. Behavior change will require knowledge, skills, motivation, and organizational changes. It is beyond the scope of “learning” in the traditional sense. Course content is not enough. However, with a consultative mindset, L&D can directly impact the business by solving for these two critical issues:
- Difficulty in changing organizational behaviors and attitudes
- Absence of data-driven culture or data-driven decision-making
Behavior Change Is Complex
Behavior change is always a change management issue. Training and technology can be part of the solution, but people’s behavior is not as simple as telling them what to do. If people did what they were told to do, we wouldn’t need as many prisons. People are complicated, like Facebook statuses. There’s a whole field of science dedicated to changing behaviors: behavioral science. Motivation plays a key part in change, but leadership and management must be on the same page and follow the same strategy to support individuals and teams going through the change.
A Failed “Behavior Change” Effort: A Personal Example
Once I had the pleasure to experience change management in the wrong way. Senior leadership decided that we were going to collaborate better, and so, the first step of this process (according to them) was to implement some collaboration platform where we all had our profiles. Well, adoption tanked. Why? First, because collaboration features may sell but they don’t collaborate. People do. And completely ignoring how people are collaborating today (whether it is efficient and effective or not) is a crucial mistake. Adoption problems are common when you focus on technology and you believe in the “if you build it they will come” approach. People are human.
- Conclusion
People are human. They make mistakes. Even C-level executives. But here’s the biggest problem with that: not learning from mistakes. What was the reaction of senior leadership when collaboration didn’t happen according to plan? Instead of implementing change management, they mandated that everyone complete their profiles on the platform by an arbitrary deadline. It looked like great progress on someone’s performance review, but the whole collaboration project was a failure. And guess how senior leaders “collaborated” with us during this time? Through emails.
What Can L&D Do To Stay Relevant In The Data Game?
Let’s start with acknowledging that L&D does not own learning. Learning is a process. It is invisible. You can’t observe learning. It’s like the wind. You may see the trees moving, the leaves flying, and you may hear the sound, but these are all symptoms of the wind, not the wind itself. Learning is the same. You can’t measure learning. You can only measure the application of learning through some proxies. It can be an assessment, reflection, observation, etc.
If L&D is not responsible for learning, what are we responsible for? I believe a practical way of thinking about it is that we are responsible for “designing” the best conditions for learning and the application of learning. This does not mean course design. It means we need to have a consultative mindset, working with stakeholders and acting as problem-solvers and advisors. But we can’t control what happens on the job. Why should we be responsible for that?
Employees don’t come to work to learn, they learn so they can come to work. They are also complex humans with a complicated status. Focusing only on how people learn in theory will not deliver the results in practice. Learning is a shared responsibility that involves not only L&D but, beyond that, managers, operations, executives like the CDO, and even the employees themselves.
The Chief Data Officers are focused on data. Data literacy and data analytics are fundamental for decision-making. That is a huge opportunity for L&D to provide value for the organization through upskilling and reskilling the workforce not only on basic data literacy but also on decision-making using insights from data (and often based on partial information). This is just a starting list for a data-driven strategy, but I believe it is a good start:
Upskilling And Reskilling
The emphasis on generative AI and data analytics presents a significant opportunity for L&D to design targeted training programs. These programs can focus on upskilling employees in data literacy, AI understanding, and applying these technologies in various business contexts. Note that upskilling and reskilling are often mentioned in a single sentence but they require two different approaches. Upskilling is a vertical growth within the current domain while reskilling is more of a horizontal move into an adjacent domain.
Cultural Transformation
L&D can be crucial in driving the cultural shift toward a data-driven organization. This involves creating programs that foster data literacy across all levels of the organization and embedding data-driven decision-making processes within the company culture. Note that data literacy or even data analytics are not enough. L&D also needs to focus on providing meaningful practices for decision-making.
Collaboration Building
As Chief Data Officers focus on making other executives successful, L&D can facilitate cross-functional projects/workshops that encourage collaboration and knowledge sharing across departments. This approach can help break down silos and build a cohesive strategy for data and AI initiatives. Hackathon-type innovation workshops can not only build collaboration across different skills and roles but also come up with products. For example, imagine your new hire engineers learning the company, the systems, the processes, the tools, etc., by building products that others can use internally. And maybe the next new hire cohort can build on?
At the same time, we must be aware of the challenges and risks in order to mitigate them. Here are a couple of them :
Rapid Technological Change
The fast pace of technological advancements, especially in AI, poses a challenge for L&D to keep training content relevant and up-to-date. Continuous learning and agile development of training programs are essential.
Alignment With Business Objectives
L&D must ensure that learning programs are closely aligned with the organization’s strategic objectives, particularly in demonstrating the business value of data initiatives.
Overcoming Resistance To Change
Resistance to cultural and behavioral change is a significant barrier. L&D needs to develop strategies for change management that address these challenges, potentially through experiential learning, leadership engagement, and showcasing success stories.
Conclusion
What’s in your data strategy to support the time of change? How does your L&D strategy change to stay relevant in the changes coming? How do you embrace data skills and AI in the new model? More to come on these in the next article.
