Every organization wants to experience business transformation because of their data analytics efforts. However, the road to a successful data strategy is not a linear path and needs a detailed plan to make it successful.
Everyone’s data strategy roadmap will be different. But every data strategy requires the expertise of dedicated data team who can navigate the complexities and orchestrate a cohesive approach, ensuring the alignment of data initiatives with overarching business objectives.
For organizations undergoing their data journey, they have two choices – either build their own data analytics team or outsource their data analytics projects to experts.
If you are trying to decide between in-house data analytics team or outsourcing, this blog will help you understand which is the right approach for your organization.
How to Structure a Data Team
Building an effective data team is essential for any organization looking to become data-driven, but it can be challenging due to the lack of in-house talent. Crafting a data team structure that will be successful requires a strategic approach to ensure long-term viability and success. Here's how to structure a data team:
- Operating Model Assessment: Organizations must determine the most suitable data and analytics operating model – decentralized, centralized, or a hybrid approach – based on their size, maturity, and specific needs.
- Establish a clear vision and goals: Bring together key stakeholders to define a compelling vision and set achievable yet ambitious goals for your data strategy. Through open discussions, explore how data can drive value, whether by optimizing operations, enhancing customer experiences, or uncovering new revenue streams. Align the data team's objectives tightly with these business goals to ensure their efforts directly contribute to meaningful outcomes.
- Assess current data capabilities: Next step is to evaluate the robustness and scalability of your existing data infrastructure, the efficiency and effectiveness of your data processes, and the depth and breadth of your talent pool's data skills. Identify strengths to leverage and prioritize areas for improvement based on their potential impact on your data strategy goals.
- Role and Function Alignment: Successful data team structures require roles and functions that span the entire data lifecycle, from acquisition and preparation to analysis, interpretation, and communication. This holistic approach ensures comprehensive coverage and maximizes the value derived from data assets.
- Talent Acquisition Strategy: Given the shortage of skilled data and analytics professionals, organizations should consider a blended approach that combines internal team building and strategic partnerships with consulting services. This flexible strategy can help address talent gaps and ensure access to specialized expertise as needed.
- Continuous Improvement: It's crucial to embrace a mindset of continuous improvement. Regularly revisit and refine your data strategy, critically evaluating whether it still aligns with your evolving business goals and market landscape.
We know that data initiatives are critical for organizations to unlock the true value of their data assets. Getting to that success depends on having the right roles and responsibilities in place. From setting the strategic vision to architecting the platform, executing the plan, building and utilizing the platform, ensuring operational excellence, and governing the data assets, a diverse set of roles is essential.
Roles and responsibilities to Drive your Data Strategy Roadmap
Defining the Strategic Direction
- Data Strategy Sponsor: Responsible for setting the clear vision and mission that will guide the data initiative.
Architecting the Platform
- Solution Architect, Data Architect, Infrastructure Architect: Architects the platform, ensuring it aligns with the organization's goals and requirements.
Execution
- Delivery Manager: Oversees the execution of the plan, coordinating efforts across teams and stakeholders.
Building the Platform
- Infrastructure Engineer: Designs, builds, and maintains the underlying infrastructure for the data platform.
- DevOps Engineer: Ensures the reliable and efficient deployment, operation, and maintenance of the data platform.
Leveraging the Platform
- Data Analyst: Analyzes data to derive insights and support decision-making.
- Data Modeler: Develops data models to represent and organize data effectively.
- Data Engineer: Builds and maintains the data pipelines, ensuring data integrity and accessibility.
- Data Scientist: Applies advanced analytics and machine learning techniques to extract valuable insights from data.
- Data Visualization Specialist: Presents data and insights in a visually compelling and understandable manner.
Operational Excellence
- Service Manager: Manages the delivery of data services, ensuring efficient and effective operations.
- Support Engineer: Provides technical support and troubleshooting for the data platform.
- DataOps Engineer: Implements and maintains processes and tools for efficient and reliable data operations.
Governance and Oversight
- Compliance Officer: Ensures adherence to relevant laws, regulations, and policies related to data management.
- Data Steward: Oversees the responsible and ethical use of data assets.
- Data Owner: Defines and manages data ownership and accountability.
- Data Discovery Specialist: Facilitates the discovery and understanding of available data assets.
- Data Administrator: Manages and maintains the data platform, ensuring its smooth operation.
Your data team structure will be unique to your organization’s needs, resources, and capabilities. You have the option to build an in-house team or outsource services, but no matter which option you choose, it requires a diverse set of roles and expertise.
Choosing the Right Path: Build Internally or Outsource for Data Success
This critical choice impacts your ability to drive insights, innovation, and maintain a competitive edge. To guide your decision, here’s a comparison between building an internal team versus outsourcing.
| Aspect | Building Internally |
Outsourcing |
|
Control and Alignment |
Direct control over team composition and alignment. |
Vendors strive to understand your needs, creating a symbiotic partnership focused on your goals. |
|
Cultural Fit |
Easier integration into company culture. |
Cross-domain experts with ability to work seamlessly across culture. |
|
Knowledge Retention |
Ability to retain knowledge within the organization. |
Have SLAs in place to ensure seamless knowledge transfer and training in-house talent. |
|
Cost Efficiency |
Higher upfront costs for hiring, training, and maintaining an in-house team. |
Scale resources up or down based on project needs and eliminate overhead costs. |
|
Skill Gaps |
Hiring and retaining skilled data professionals is extremely competitive. |
Access to a wide range of specialized skills without the hiring pains. |
|
Focus on Core Business |
May have other competing priorities that could delay progress. |
Allows focus on executing the strategy in alignment with business goals. |
|
Dependency Risk |
No dependency on external vendors |
A long relationship can mitigate the dependency risk. |
|
Quality Concerns |
Ensuring quality standards internally |
Choose the specialized vendor with success stories and good brand |
As the comparison suggests, outsourcing comes out on top from a cost and speed perspective. While in-house teams provide control, scalability, and cost-effectiveness.
10 Must-Ask Questions for Selecting the Right Partner for Data Analytics and AI Services:
- What specific areas of data expertise do you have? (Check for keywords like data engineering, data science, machine learning, visualization, etc.)
- How many years of relevant experience do your data professionals have on average?
- Can you provide case studies or examples of similar successful projects to our industry?
- What is your project management and delivery methodology? How do you ensure transparency?
- How do you approach knowledge transfer to upskill your internal team's data capabilities?
- What data security, governance and compliance controls are in place?
- How does your team plan to understand our unique business needs?
- Will there be a dedicated team assigned to your account long-term?
- What is the pricing model? Are long-term commitments required?
- Are you using AI or ML in projects?
Outsourcing to a partner like ProArch provides access to diverse expertise across data domains that accelerates the development of your data strategy roadmap.
Crafting a data strategy roadmap may seem daunting and overly complex. Especially if you don’t have the internal skills and bandwidth to get it done. Wherever you are on your data journey, our data analytics and AI services transform data from a hurdle to an asset.
Take the next step today and contact us.