Data & Analytics Team Strategy

In March 2023, I was faced with a significant challenge when asked to lead the data and analytics team despite having limited expertise in data and analytics management. I inherited a team of 3, increasing my direct reports to 5, including a technical lead who I quickly realized was not ready for a leadership role. Additionally, I discovered significant deficiencies in processes and strategies within the team.

Throughout the process of understanding the team and developing a data team strategy to address these deficiencies, my strengths in team development and organizational strategy were evident. I successfully addressed skill gaps within the team, improved processes for efficiency and effectiveness, and developed a clear roadmap for future growth and success. My ability to identify and address challenges, coupled with my strategic vision and leadership skills, enabled me to create a comprehensive plan that was well-received by the CEO in November 2023.

The first 6 months

Getting to know the data team and understanding the discipline

Over the first six months, I focused on getting to know the team and the discipline, while developing my team's skills in areas of stakeholder management and expectation setting. I worked with the team to develop goals to identify and assess the current work process, establish a plan to address data literacy at the company, and an assessment of the technology and tools we were using. I also spent this time creating a comprehensive strategy to address these issues and deficiencies. In November 2023, I presented a three to five-year maturation plan to the CEO. This plan aimed to move the team from its current opportunistic stage to a differentiated stage in data analytics maturity over a 3-5 year period, aligning with The Gartner Data Analysis Maturity Model. It included specific objectives for each stage of maturity and detailed tactics for bringing the team from its current stage (two, Opportunistic) to the next stage (three, systematic).

The immediate goals from this strategy for the end of Q1 2024 were to establish specific data strategies supporting key business objectives, define and implement new processes, identify next steps for the technical plan/roadmap, establish baseline metrics for progress tracking, and create individual training and coaching plans for team members.

Deliverable: Data & Analytics Team 3-5 Year Maturation Plan

Problem:

Current limitations hinder efficient report generation, restrict self-service options, create workload pressures for both the data team and business stakeholders, and impede collaborative data and Analysis efforts between the data and analytics team and the business.

Align to Gartner Data Analysis Maturity Model
Targeted goals to bring us closer to the next stage (3 - Systematic)
Scope is Data Team Strategy, not overarching company data strategy

Although the scope of this initial strategy was focused on the Data Team itself, there are elements that aimed to increase data literacy across the company and to influence our senior leadership regarding the value of leveraging data more than we are. I knew there would be challenges ahead in managing up to our executives, but I was up for the challenge having faced a similar challenge in the past as one of the founding members of the company's UX team.

Current State

Opportunistic | Stage 2: The team is optimized for Report Generation

Deliver business originated requests
Move applicable reports to self-service
Standardize essential processes
Increase business literacy

Desired Future State

Differentiated | Stage 4+: The team is optimized for Consultative Analysis

Collaboratively managed backlog with business
Self-service enables organic insights
Processes deliver predictability
Strong emphasis on data quality and governance practices
Business partnership for data-driven decision-making

Maturity Plan

Implementation of Best Practices:

Intentional Plan to move through stages of the Gartner Maturity Model. I found that most data maturity models were very similar, and could be customized to our needs. The Gartner Maturity Model was selected as a well-known name that our executives would recognize and provided an initial framework that I could then customize for the needs of our company based on my research.

Goals for Q1 2024

Data strategies to support key objectives for select business areas.
Processes defined and in practice.
Next steps for technical plan/roadmap identified.
Team/individual training plan(s) in process.
Baseline metrics to track progress towards stage 3 established.

The next 6 months

Implementing the strategy and achieving goals for Q1 2024

Over the September 2023 - March 2024 goal period, I worked with the team to establish goals aimed at evaluating our relationship with stakeholders, as well as implementing recommendations from the previous goal period’s deliverables. To support this, I delegated to our UX designer and product manager to coach the data and analytics team in several key areas, including stakeholder management, workflow process improvements, Agile and Scrum practices, meeting facilitation, and running interviews.

Additionally, I focused on creating comprehensive development materials and coaching plans for individuals. This included a formal coaching plan for the technical lead who needed to enhance specific skills to align within their new senior-level role. The coaching plan was submitted to HR, and there were no concerns about my ability to implement it.

The UX designer and product manager conducted various activities to inform stakeholder profiles, such as interviewing team members, sending surveys to primary stakeholders, and conducting one-on-one interviews with those stakeholders. From these activities, we identified specific problems and opportunities, including:

Reducing ambiguity around timelines for requests
Managing high-value requests and ad hoc/smaller requests
Ensuring data needs are captured and visible
Prioritizing data accuracy and BI reporting scope
Growing a collaborative partnership

Through these activities, we also discovered that one business area believes they can perform data analysis internally and another area does not use data to make decisions, but rather uses data primarily to show statuses. Some higher-level leaders believe the company lacks data of interest for deeper analysis, and there is skepticism about achieving agreement on an overarching data strategy.

From the stakeholder analysis I developed an overarching strategy to address these issues with four main areas:

Process Improvement and Formalization: This involves the product manager leading the team through Azure DevOps, building backlogs, breaking down work, and other Scrum ceremonies. Additionally, a data team member is developing a process for handling errors and escalating issues.
Stakeholder Management: The product manager is working with one business area, coaching them on managing stakeholders, identifying problems and opportunities, developing requirements, prioritizing work, and managing expectations. The UX designer is working with another business area on similar tasks.
Stakeholder Data Literacy: We are developing a plan to address stakeholder data literacy, integrating it into our stakeholder management and exploring company-wide options based on a data literacy goal one individual led in the previous goal period.
Data Accuracy and Integrity: One data team member is identifying a data governance tool, while I am working with a junior data engineer on documenting a technical roadmap. The junior data engineer is also assessing pipeline development to reduce errors. I also recommended developing a plan to formalize data validation processes in the coming goal period and consider hiring a consultant to assess our toolset, implementation, and usage.

Processes

During the previous goal period, we identified several processes that needed improvements or needed to be more clearly defined. These processes revolved around request intake, work item management, stakeholder engagement, error escalation and management.

Request Intake:

Before: Requests were made ad hoc through multiple channels with little visibility. New work requests took priority over existing work, and deadlines were unclear.
Current: Requests funneled through a single email alias, refined between the subject matter expert and stakeholders. Coaching on stakeholder communication in-flight.
Future: Moving request intake to Zendesk for visibility. Collaborative backlog management with clear timelines.

Work Item Management:

Before: Work items in Asana lacked structure, leading to delays in value delivery. Limited Scrum and Agile practices.
Current: Moved work item management to ADO, improved item grooming, and implemented stakeholder reviews early in the process. Leveraged the product manager for coaching in Scrum and Agile.
Future: Refining work boards, breaking down work, and improving stakeholder relationships. Aiming for a self-organizing team leveraging Scrum and Agile for productivity and predictability.

Stakeholder Management:

Before: Stakeholder management was ad hoc, with stakeholders lacking visibility into work progress and requirements.
Current: Leveraging product team resources for stakeholder management and establishing regular meetings. Using ADO for work status visibility.
Future: Empowering the data team to manage stakeholders, ensuring trust and transparency, and developing a sponsor sync for overarching initiatives.

Error Escalation and Management:

Before: No priority matrix, leading to low-priority items taking precedence. Errors handled based on intuition, lacking notification and debrief processes.
Current: Work in progress to identify a priority matrix and improve notification consistency. Stakeholder interviews to establish prioritized monitoring.
Future: Implementing the priority matrix, adding timestamps to reports, and establishing proactive error debrief processes. Maintaining a list of high-priority reports for monitoring.

Technical Plan/Roadmap

For the technical roadmap, our next steps included formalizing the data engineer role. We had a BI specialist who had been working as a junior data engineer for the past year, and my intent was to establish this role officially. Additionally, we were in the process of developing a modern pipeline development framework. Our plan was to prioritize the list of reports for implementation of this new framework.

My final recommendation for the technical plan was to hire a consultant to review:

Scalability: Assessing whether our current tools could accommodate our current and anticipated data volume and complexity.

Complexity: We also needed to determine if these tools were more complex than necessary for our needs.

Cost-effectiveness: This assessment would help us evaluate the cost-effectiveness of our tools, considering whether their features and functionalities justified their cost or if a simpler solution could suffice.

To facilitate this process, I had developed a Request for Information (RFI) as well as a list of potential consulting companies and freelancers to reach out to.

Team/Individual Training Plans

In the previous goal period, I quickly realized that the team needed to improve their stakeholder management skills. To address this, I initiated coaching and development activities even before formalized plans were in place. One of the steps I took was to have everyone attend a Nielsen Norman Group workshop on Successful Stakeholder Relationships. While Nielsen Norman Group primarily focuses on UX, the training offered high-level tactics suitable for anyone needing to manage stakeholders, regardless of their specialization.

During the subsequent goal period, I worked closely with team members to assess their job criteria, identify areas for improvement, and prioritize them. We narrowed down the list to the top two to four areas for each individual. Together, we evaluated their current proficiency against the desired benchmarks, and then identified specific training resources and opportunities for improvement. This process culminated in the creation of formalized coaching plans tailored to each person's needs, including details such as training phases, meeting cadence, and documentation requirements.

Additionally, I began developing a new job description for the data engineer position, outlining specific criteria for assessing readiness and coaching them into the role. I also determined the appropriate compensation level for them, ensuring that it matched the demands of the position we were defining.

For the technical lead I was coaching, I identified training opportunities to enhance their stakeholder management skills, deepen their understanding of statistical concepts, and improve their ability to identify and address problems and requirements. It was later revealed that they had been interviewing for roles outside the company, and they attributed their successful job offer to the coaching and training I had provided, which they believed significantly contributed to their readiness for the new role.

Baseline Metrics

In order to gauge the progress and effectiveness of our data and analytics initiatives, we established a set of baseline metrics across various key areas. These metrics served as the foundation for evaluating our performance and guiding our efforts towards achieving our strategic goals.

First I developed an overarching list of possible metrics from quanitative and qualitative perspectives, and then narrowed it down to a few focus areas to get us started before diving into establishing a baseline.

Business Support with Data Analysis:

Quantitative: Team proficiency level.
Qualitative: Stakeholder feedback on the effectiveness of data analysis.

Establishment of Data Engineering Role:

Quantitative: Presence of a dedicated data engineering individual.
Qualitative: Impact of the role in streamlining data processes and efficiencies (e.g., time to create data models, time to create pipelines, number of errors in pipelines, etc.).

Formal Data Processes in Place:

Quantitative: Number of documented processes and adherence to industry standards.
Qualitative: Assessment of how well processes are followed and impact on data handling.

Leveraging Robust Tools:

Quantitative: Number of tools in use, cost savings, or efficiency gains attributed to these tools.
Qualitative: User feedback on ease of integration, impact on workflow, and ability to create reports.

Reliable Data Supporting Core Business Processes:

Quantitative: Accuracy and consistency measures (e.g., error rates, data validation results).
Qualitative: User satisfaction feedback, relevance, and impact on decision-making.

Advanced Reporting and Dashboards for Business Areas:

Quantitative: Number of reports and dashboards, use of statistics.
Qualitative: User feedback on usability, relevance, and impact on decision-making.

Clear Data Governance and Quality Assurance:

Quantitative: Compliance rates, number of data issues resolved.
Qualitative: Feedback on clarity and effectiveness of policies.

Focus Areas:

Stakeholder satisfaction
Time to complete requests
Data quality and accuracy
Cost and resource efficiency
Return on data investment (RODI)

CSAT Survey:

Conducted a CSAT Survey to measure stakeholder perception in areas of:

Timeline comprehension
Satisfaction
Collaboration
Business literacy
Data availability
Accuracy
Collaborative insights.

CSAT Results

Additional Statistics (pre-reorganization):

I was in the process of documenting baseline stats for several other metrics at the time of the company's reorganization, including:

Number and frequency of errors in Power BI and Azure
Cost of BI tools
Evaluation of requests by type, size, and complexity and associated timelines

Conclusion

In conclusion, my journey leading the data and analytics team from March 2023 to February 2024 has been marked by significant challenges and rewarding achievements. Despite starting with limited expertise in data management, I successfully identified and addressed key deficiencies within the team and developed a comprehensive strategy for future growth and success. By leveraging my strengths in team development and organizational strategy, I was able to improve processes, address skill gaps, and create a clear roadmap that was well-received by the CEO.

Over the course of the first six months, I focused on understanding the team and developing their skills in stakeholder management and expectation setting. I also developed a three to five-year maturation plan aligned with The Gartner Data Analysis Maturity Model, aiming to move the team from its current opportunistic stage to a differentiated stage in data analytics maturity.

In the subsequent goal period, I worked on implementing the strategy and achieving the goals set for Q1 2024. This included evaluating our relationship with stakeholders, implementing recommendations from previous deliverables, and developing comprehensive training plans for team members. I also initiated coaching and development activities to improve stakeholder management skills and established a new job description for the data engineer position.

While my journey with the data and analytics team was cut short due to a company reorganization, I am proud of the progress we made and the foundation we laid for future success. I believe that my experience leading this team has not only strengthened my skills in data management and analytics but has also prepared me for future challenges and opportunities in leadership.