As enterprises evolve towards greater data sophistication, the significance of the Data & AI Strategist role becomes increasingly critical. Occupying a pivotal position, these experts are tasked with harmonizing the realms of technical prowess and business acumen, acquiring specialized skills to seamlessly connect data endeavors with overarching business strategies.
Past discussions in this series have emphasized the necessity for a robust data & AI strategy to be intricately woven into the fabric of an organization’s business planning, covering both immediate and future goals. This integration is essential across various layers – from ensuring a transparent process for identifying use cases at a project or product level to establishing a clear correlation between project KPIs and the broader objectives of a department or organization. Similarly, at the level of broader transformation initiatives or programs, reaffirming this connection to the company’s overarching goals is vital to maintain executive support and ensure the program’s sustained relevance.
This critical connection has historically been managed by an understated yet essential role within many organizations. While the title of data scientist was heralded by Forbes in 2014 as the “sexiest job of the 21st century,” and the role of data engineers has seen a surge in demand for their foundational expertise in data, analytics, and AI, the Data & AI Strategist has quietly cemented its importance in the data and AI landscape. These individuals are the linchpins in the organizational data framework, adept at translating business requirements for technical teams and vice versa. Their work ensures that the technical outcomes are communicated back to the business stakeholders in a meaningful and impactful manner. The role of the Data & AI Strategist is crucial in the success of data and AI initiatives, facilitating a synergy where (1) the business can concentrate on addressing problems with the best data-driven insights and tools, and (2) technical teams can dedicate themselves to crafting cutting-edge solutions in data, analytics, and AI.
Initially dubbed as “analytics translators,” the role has significantly evolved in scope and depth as organizations journey through data maturity. Businesses have come to understand that aligning business objectives with technology is not a task confined to individual projects. Equally crucial is the strategic consideration and design of the entire data & AI transformation journey and the operational effectiveness of data & AI teams, with a laser focus on the overarching business strategy. This broadened perspective has led to a shift in terminology to a more fitting title: “strategist.” This title aptly reflects the role’s expanded focus on guiding the strategic direction of data & AI projects, daily operations, and the overarching transformation process, ensuring alignment with key business goals.
The Growing Complexity of the Data Strategist Role
As organizations intensify efforts to hasten their data transformation journeys, the demand for skilled professionals in these arenas has surged, outpacing the availability of academic programs designed to cultivate such expertise. This discrepancy has placed companies in a predicament, facing a shortage of experienced talent who are both scarce and costly. Concurrently, the skill set required for implementing data strategies has broadened across all data domains.
In response to these evolving needs, we’ve observed the emergence of three distinct types of data strategist roles: (1) those steering data & AI transformations from a strategic standpoint, (2) those tasked with translating strategic visions into data & AI operations, and (3) those collaborating closely with technical teams to develop data products, services, solutions for both internal and external applications.
These roles vary in focus, with some aimed at driving transformative change and others at achieving incremental advancements. The competencies needed range from conceptual thinking and effective communication to excellence in execution. While no role is deemed more critical than another, achieving a balanced mix of these diverse talents is essential for the success of data-driven initiatives. The challenge of finding the right talent echoes the longstanding dilemma in recruiting data scientists: while it’s still possible to identify suitable candidates, it’s becoming increasingly challenging.
Fortunately, the specialization within the data strategist domain doesn’t necessitate the creation of a new corporate function. Despite their differences, all three sub-profiles share a common, essential objective: bridging the gap between data professionals and the business side. They depend on a similar set of skills, albeit to varying degrees, including data and technological acumen, industry-specific knowledge, strong communication abilities, strategic insight, and experience in product and project management, as well as operational expertise.
This indicates that a data strategist can excel with a varied skill set, although they may not be the sole strategist in an organization. This diversity underscores the role’s adaptability and its critical function in aligning data initiatives with business objectives, ensuring the success of data-driven strategies.
Navigating the Talent Challenge for Data and AI Strategists
To meet the escalating demand for data and AI strategists, organizations are compelled to actively engage in scouting and enhancing talent equipped with the essential combination of tech savvy and strategic business insight. Identifying the specific traits that distinguish a proficient data strategist is the initial step in this process.
Organizations can establish internal programs for talent identification and skill development. By pinpointing individuals displaying potential and offering them specialized training and growth opportunities, businesses can build a reservoir of adept data strategists from within their ranks. This strategy not only mitigates the challenge of talent scarcity but also ensures that emerging data strategists have an intrinsic understanding of the business’s unique landscape and challenges.
In conjunction with nurturing internal talent, engaging external candidates to assume the data and AI strategist role is another viable strategy. Though newcomers may initially lack the nuanced understanding of the business, they compensate by introducing fresh perspectives and methodologies honed in previous positions, such as those of product managers or technically inclined business analysts. These individuals can infuse the organization with novel ideas and strategies for data and AI implementation and management. It is worth noting, however, that external hires might need a period of adjustment to fully grasp the specific nuances and culture of their new environment.
Moreover, forming partnerships with seasoned experts who have a track record of steering successful data and AI transformations can provide immediate relief for the shortage of in-house data strategists. These external consultants can serve a dual purpose: acting as interim strategists and assisting in the talent identification and development process. Beyond merely filling a vacancy, data strategists play a crucial role in educating and guiding their organizations through the intricacies of data and AI integration.
By blending internal development initiatives with the strategic addition of external talent, companies can assemble a dynamic team of data strategists. This hybrid approach facilitates a comprehensive strategy for fostering innovation and driving meaningful transformation, ensuring organizations remain competitive in the data-driven landscape.
Anticipating the Evolution of Data and AI Strategist Specialization
In the rapidly evolving realm of data-driven decision-making, the significance of the data and AI strategist is set to grow exponentially. Looking ahead to the next five years, it’s anticipated that data strategists will be expected to enhance their skill sets further, incorporating areas such as data management and governance into their expertise. Traditionally, the essence of this role has revolved around forging connections and constructing bridges within the organization – linking business with IT and melding strategy with practical execution.
As the landscape of data and AI continues to advance and new challenges emerge, the demand for more specialized skills across all roles is inevitable. While there will remain a place for generalist data strategists, those capable of mastering a comprehensive skill set are poised to advance more swiftly. However, organizations must prepare for the reality that specialists in data strategy will become increasingly crucial – and finding the perfect match for these specialized roles will be progressively challenging.
By dedicating resources to the identification and enhancement of talent, both from within their ranks and through collaboration with external specialists, businesses can position themselves to adeptly handle the intricacies of data and AI projects. This proactive approach to talent development will be key in securing the specialized expertise required to foster innovation and achieve long-term business success in the AI-centric era ahead.
Reach out to us, if you want to learn more about how we can help you on your data journey.
