As companies progress on their journey towards data maturity, the role of the data strategist becomes ever more important. These technical business experts have to master increasingly specialized skills in their quest to bridge the gap between data initiatives and business strategy.
It has been stated that a functioning data & AI strategy must tie closely to the organization’s business strategy and short and long term objectives. This holds true on multiple levels. On a project or product level, you need to ensure that there is a transparent use case sourcing process as well as clear linkage and follow-up of your project’s KPIs in relation to your department’s or company’s objectives. On a transformation or program level (that is your data & AI transformation program or equivalent overarching initiative), you need to yet again establish this linkage to your overall business objectives, otherwise your program will risk losing its executive buy-in and support, and potentially fade away over time.
Ensuring this linkage has been the job of a silent role in many organizations for the past decade. While data scientists were named to be the sexiest job of the 21st century (Forbes, 2014), and data engineers continued to rise in popularity, as the groundwork for all things data, analytics and AI required their expertise, one role has been silently establishing its presence in the data & AI space: Data & AI Strategists.
These tech-savvy business people have been glueing the data pieces together organizationally, ensuring that requirements from business get translated sufficiently to technical teams, as well as technical results are being communicated back to business in clear and impactful ways. Their presence in making data & AI initiatives succeed is paramount, as they enable both technical and business sides to do what they are best at: 1, business to focus on solving business problems with the best insights and tools available, and 2, technical teams to focus on building state-of-the-art data, analytics and AI solutions.
This role has, at its infancy, been named “analytics translator”, however, over the years the playing field and the maturity of the role has widened significantly. Companies have realized that this business and technology alignment does not only have to be in place on a project level, but equally important is to think about and design the whole data & AI transformation, as well as the practical operations of data & AI teams with the key business strategy in mind. This has prompted the role to be relabelled to the better suited “strategist” – taking care of the strategic direction of data & AI projects, operations and transformation alike.
An evolving role gets more complex
We have seen organizations trying to accelerate their data transformation, leading to the circumstance that talent in the respective fields is in even higher demand. At the same time, the academic offering cannot match the speed in which the demand develops. This leaves organizations in a difficult position. Experienced talents are scarce and expensive. Simultaneously, the skills required to execute the data strategy have further developed for all data domains.
We now regularly identify three distinct data strategist profiles:
- those leading data & AI transformations from a strategy perspective,
- those that are responsible for translating this strategy to the operational level
- those that work closely with technical teams to deliver data products (for internal or external use).
Some are expected to bring about transformational changes, others focus on contributing with incremental impact. Some must have their strengths in conceptual thinking and communication, while others need to bring executional excellence. None are more important than the others, but having a good mix of these different talents is key to data-driven success. Relentless specialization has brought us back full circle to the old problem of the data scientist: it is still possible to find the right people for the job, but it will become ever more difficult.
The good news is that the specialization of data strategists does not require the carve out of yet another corporate function. All three sub-profiles broadly do the same crucial thing – play a vital translational role between the data professionals and the business side. They all rely on similar skills, if in different concentrations: data and tech savvy, domain expertise within the company’s business, good communication, corporate strategy thinking, product and project management experience, operational know-how.
Crucially, this means a data strategist can thrive with different combination of skills, though perhaps no longer be the only data strategist aboard.
Finding the right talent for the role is challenging
To address the growing need for data and AI strategists, companies must proactively invest in identifying and upskilling talent with the right blend of technological expertise and business acumen. This involves recognizing the unique characteristics that define an effective data strategist – as outlined above in this article.
Companies can implement in-house identification and upskilling programs. By identifying individuals with potential and providing them with targeted training and development opportunities, organizations can cultivate a pipeline of skilled data strategists from within their own ranks. This approach not only helps to address resource shortages but also ensures that data strategists possess valuable domain-specific knowledge and a deep understanding of the company’s unique challenges and opportunities.
Coupled with internal talent development initiatives, companies may also consider hiring external candidates to fill the role of data and AI strategist. While external hires may lack company-specific domain expertise initially, they bring valuable perspectives and experiences gained from previous roles, such as product managers or business-savvy data scientists. These individuals can leverage their diverse backgrounds to offer fresh insights and innovative approaches to data and AI strategy and execution. However, it’s important to acknowledge that external hires may require additional time to acclimate to the company’s specific challenges and culture compared to internally developed talent. Nonetheless, by combining internal talent development with strategic external hires, companies can create a well-rounded team of data strategists capable of driving impactful change and innovation.
Furthermore, companies may find value in collaborating with external experts bringing their experience from running successful data & AI transformations and projects. Such experts can temporarily fill the gap in this missing role, as well as supporting organizations with identifying the talent and designing a program to upskill them along the way. Indeed, data strategists can additionally fill an educational role, helping companies along their data & AI journey.
Deeper specialization is around the corner
The role of data and AI strategist is becoming increasingly vital in today’s data-driven business landscape. Over the next five years, we expect data strategists to add even more skills to their profiles – data management and governance, for example. The role of the strategist has always been about building bridges and connecting dots in the organization. Between business and IT. Between strategy and execution.
As the field matures and new problems arise, building these bridges requires more specialized skills in every role. There will still be a role for generalist data strategists – and people who can master all skills will likely rise faster than others. But companies need to brace for specialized data strategists becoming more important – and exactly the right ones harder to find.
By investing in the identification and upskilling of talent, both internally and with the assistance of external experts, companies can ensure they have the expertise needed to successfully navigate the complexities of data and AI initiatives and drive sustainable business growth.
Key take-aways:
- Rising Importance of Data & AI Strategists: As companies advance in their data journey, the role of data strategists becomes increasingly crucial. These experts serve as the bridge between technical capabilities and business objectives, ensuring alignment between data initiatives and strategic goals.
- Talent Development Strategies: To meet the demand for data strategists, companies are investing in internal talent identification and upskilling programs. By nurturing individuals with the right blend of technical proficiency and business acumen, organizations can cultivate a skilled workforce from within. Additionally, strategic collaborations with external experts can offer fresh perspectives and experiences, enriching the data strategy landscape.
- Specialization and Future Trends: The role of data strategists is evolving, requiring deeper specialization in areas like data management and governance. As the field matures, companies must anticipate the growing importance of specialized data strategists while investing in talent development initiatives to meet evolving demands.