As you can imagine, there are many leadership areas in which CDAOs focus on collaboration with HR, recruiting, and technology. In their role as the leader of the data analytics (DA) practice and as executive general manager for the firm, current concerns for CDAOs revolve around recruiting, management, and retention of DA talent.
With more firms adopting the center of excellence (COE) and practice model (often Agile at Scale practice models) for data analytics (DA), success begins with designing and implementing a world-class talent architecture.
Let's Start At The Beginning: Why Do We Need Talent Architecture?
Just as a building plan defines the elements of a house to be built, a talent architecture (TA) clearly explains the elements of the jobs to be done. TA is leveraged to understand what skills and competencies are to be recruited, how they should be managed, and what expectations new hires (and current team members) should have regarding job performance, competencies, career progress, and compensation. A finely tuned talent architecture will achieve these goals and set the practice up for organizational, business, and team member success.
An Impactful DA Talent Architecture Has Two Prime Elements With Multiple Powerful Benefits
- Fit for Purpose Job Descriptions: To provide robust, well-defined job descriptions that clearly define role profiles within your organization. These profiles describe the boundaries of the role, years of experience required, and technical/business qualifications. This is important for honing your recruiting strategy and spelling out the expectations for the existing team of each roleâwhat the role is, what it does, and what spells success.
- Career Path and Salary Range: The talent architecture creates spans and layers within each job function that makes it clear to existing staff what it takes to get to the next level, the expectations of those occupying each role, and the salary band for that particular job. When you design the spans and layers, HR will coach the CDAO to be people agnostic and not to think about the existing team but rather what roles are needed for the function and how they will calibrate to the market and best practices and the desired end state organization.
- Credibility and Professionalism: Ensures DA roles are filled with actual data analytics professionals. The talent architecture helps create credibility for the organization's role and the team, with all stakeholders aware that the position is part of an endorsed competency center of excellence. Historically, there were issues with hiring managers slamming people with connected skills (but not the required ones) into a job, only to have them leave or to create sub-optimal results for that particular role. [We all know folks in roles where we scratch our heads wondering how they got there based on required qualifications. Talent architecture helps avoid this syndrome.]
- Business Competitiveness: Roles are clearly defined and are priced to the market via regular surveys. Calibrating to the market allows adjustment within your compensation strategy to attract and retain talent. The salary banding should be reasonably broad to allow for flexibility for advanced, hard-to-find talent/skill sets in data science, engineering, and AI.
- Career Path: Team members know where they stand with a defined career pathâ'I know who I am, where I am, and where I can go.' Everything is published and why people hold their roles becomes less of a mystery.
- Organizational Transparency: Clarity of job functions and associated levels creates and builds trust with the professionals on the team and rational thinking and understanding of the function by management. I find the higher the trust amongst the team, the lower the turnover!
- Teamwork and Collaboration: Workflows and handoffs are known with understood roles and responsibilities. Very often, there is confusion between data scientists and data engineering regarding the handoffs and who is building what aspects of the tooling. TA brings that clarity and helps engender collaboration with clearer handoffs and job scopes.
An Example Of A Talent Architecture
A talent architecture is a living, breathing system of job families and functions calibrated in content and compensation with a market study. This architecture defines all subject areas, job functions, and categories within an overarching job family. There can be many job functions within this architecture, each with a role profile having the following essentials:
- Role Title
- Role Description and Key Responsibilities: The essence of what the role does. These activities should be stated if the role leads people, uses platforms, and supports the business.
- Competency Level: The level of knowledge that the holder of the role should possess, for example, from Knowledge of to Competent to Expert level capabilities. These levels often help by translating to salary bands, and specific skill sets help define a role profile. For example, the number of years of experience in machine learning in data science can be a differentiator between the salary paid for the role and the level.
Illustrative: (There are more jobs than these two)
Executive Data Scientist
Data Science Manager
- Leads a team to apply statistical methodologies on big data platforms to help business lines improve decision-making.
- Focuses on enterprise customers.
- Working team leader manages other data scientists using machine learning to transform data into predictive models applied to solve business problems for business partners.
- Leads the team to design, build, and execute client data science projects.
- Engages with key executives on the integration of data science work products across the organization
- Advocates the data science vision.
- Collaborates with business partners to define the business problem.
- Selects and builds the appropriate data science or statistical tools with a small, focused squad.
(Including COE/Practice skills, Organization and Leadership skills, and Technical skills)
Rated by knowledge level. For example:
- Data science: Expert
- Business knowledge and translation skills level: Expert
- Technical skills: Advanced
Rated by knowledge level. For example:
- Data science: Expert
- Business knowledge and translation skills level: Advanced
- Technical skills: Expert
Tips On Designing A Talent Architecture Governance And Management
- In alignment with CDAOs and their Drs, HR owns and governs the talent architecture.
- Hiring managers can customize business focus but not competencies. In other words, hiring managers don't get to change the job family at will. They must leverage the governance model to update the roles based on the desired end state and market calibration.
- The dedicated technology team works with HR to make the role profiles and full TA available to enterprise recruiting and LOB teams.
- People analytics teams should be formulated to understand the key insights that can benefit talent planning from the talent architecture.
- LOB leaders/clients are sponsors of data analytics projects. They can be part of the hiring process and give input into the business scope of the role.
- Third-party consultants and best-practice firms should be leveraged to guide any necessary calibrations to the talent architecture. Get in touch with me if anyone wants recommendations for these providers based on my experience.
I hope this paints a picture of some of the critical elements of talent architecture and how CDAOs help with its design. This post should also paint a picture of some of the future of work (FOW) leadership dimensions CDAOs are involved in. As always, the devil is in the details, but I believe I've left much here for you to ponder. Please send your thoughts, comments, and suggestions.
Stay tuned for future posts on What it means to be a CDAO, the critical elements of the job, and the success factors.