Building AI success: the critical role of data and leadership

Liam Edwards 3 Sep 2024

The global demand for expertise in data and AI is skyrocketing, as organizations race to enhance their analytics capabilities and commercialize their data assets. Businesses striving to stay competitive and drive innovation are increasingly realizing the transformative potential of big data analytics and artificial intelligence. But, are companies hiring the right talent and are they fully equipped to harness the vast opportunities these technologies offer.

Garbage in, garbage out

Acquiring talent proficient in critical areas such as data wrangling, data engineering, and data governance is essential for organizations at any stage of development. Senior technology leaders must prioritize these skills, as they ensure the accuracy and reliability of data inputs. These competencies are fundamental for driving effective, scalable, AI-driven decision-making processes. 

The maturity of an organization’s data function is pivotal in guiding investments in AI and ML initiatives. While federated and centralized data frameworks represent distinct approaches to data management, each uniquely influences these investments. Ultimately a mature data function provides the strategic foundation necessary to maximize the value derived from AI and ML technologies. 

While centralized capabilities can drive the scalability of use cases, they cannot operate in isolation. Achieving speed to market and fostering innovation requires deep business engagement. This could involve integrating product management functions and potentially changing reporting lines to front office and commercial functions to ensure alignment and responsiveness. 

Federated models may offer autonomy and agility to business units, however they can lead to challenges such as duplication and cost inefficiencies. A centralized, small yet powerful function can scale use cases while staying close to the business, and driving the commercialization of data. This approach offers a cohesive data perspective, enhances data accessibility, refines data integrity, and simplifies integration with AI/ML algorithms. Centralized investments in AI and ML typically focus on building resilient data pipelines, ensuring rigorous data governance, and leveraging extensive data analytics capabilities. 

Bridging the gap between data engineers and business leaders

Organizations should look to align their hiring of leaders with their data journey and maturity. We see more and more organizations eager to onboard IT professionals skilled in cutting-edge tools and techniques, such as vector databases, large language models (LLMs), and prompt engineering, to leapfrog competitors. The relative success of these hires often depends on the function they inherit, the quality of the data infrastructure, and the data literacy of the firm.  

The effectiveness of AI hinges on the quality of its data, which starts with robust governance. This highlights the critical role of hiring the right chief data officer (CDO). CDOs ensure data quality, security, and ethical standards, aligning AI initiatives with organizational goals and priorities. This approach not only boosts operational efficiency but also enhances user experiences and engagement. 

Beyond technical proficiency, there is a growing demand for professionals with deep expertise in AI. Individuals who can develop and refine machine learning models tailored to specific business needs are increasingly sought after. Additionally, there is a need for experts who can effectively manage the evolving risks and compliance challenges associated with AI implementation. 

Hands-on experience remains a significant differentiator in today’s competitive landscape. Companies are actively seeking candidates who can not only build, train, and validate models across various applications but also conduct exploratory analysis and hypothesis testing to extract actionable insights from complex datasets. There is also a strong preference for professionals who blend technical acumen with a business mindset, particularly those skilled in building robust data infrastructure and governance.  

We also see those with deep experience in AI-enabling technologies like Snowflake, Databricks, Amazon Bedrock, and Amazon Sage Maker, are highly valued for their ability to translate AI initiatives into tangible business outcomes. The need for strong data leadership is not going away, especially as AI evolves faster than the talent market. Training and development at the management level are crucial.  

The question isn’t just what should we do with data, but whether we are doing enough with it. Do we have the leadership to guide us on this journey? And what strategies can companies employ to bridge the gap between data engineers and business leaders, ensuring seamless collaboration?  

Image of Leathwaite employee Liam Edwards

Liam Edwards

Liam is a Senior Associate, within Leathwaite’s Hong Kong office and is a core member of the Global Technology Officer, COO and Digital practices. Liam works with clients across industry sectors with a focus on C-level appointments within technology, operations,…

See full profile