Data Lieutenant
Work with a data lieutenant or bridge executive to support your enterprise analytics program
How We Can Help
Executives responsible for data often include (or report up to) the CIO, CDO, CTO, CAO or CFO. Some organizations have specialized CxOs with data responsibilities, e.g., dealing with risk, security, compliance, marketing, clinical outcomes.
Even high-performing executives can benefit from a Data Lieutenant who can play effectively across business strategy, data, analytics, and other technologies. The Data Lieutenant may advise executives or be given responsibility over a key initiative / program. This person understands critical success factors for analytics programs, and the organizational dynamics that influence success.
Occasionally you may have a gap in executive leadership for your analytics program. In those cases, we can temporarily fill those gaps and help you to vet new candidates.
When To Call Us
Here are some common reasons clients call on us.
- Exploring how to capitalize on AI.
- Identified dysfunction around analytics.
- Want to augment current data executives.
- Need more executive leadership over data.
- Specialized needs arise such as security and data governance.
- Setting up an analytics Center of Excellence.
Key Activities
Don’t see what you’re expecting? Let’s talk about how to tailor to your needs.
Your Data Lieutenant will first determine where your organization stands with respect to data, analytics AI. This includes researching the business strategy as well as assessing current technical capabilities. He will work with you to understand parameters of the role and then engage with other executive leaders throughout your company.
The Data Lieutenant’s most important function is to assist you in defining the strategy to implement and adopt analytics. From there, it is a matter of ensuring strong execution among key initiatives, projects, departments, and executives.
TECHNOLOGY
- Review and advise on analytics architecture.
- Assess analytics strategy.
- Budgeting, forecasting, and planning.
- Conduct technology risk assessment.
- Review and mitigate licensing costs.
PROCESS
- Assess DevOps practices.
- Optimize business requests.
- Improve estimation of work.
- Facilitate steering and prioritization.
- Set expectations for team productivity.
- Establish delivery cadence.
- Improve testing and QA practices.
ORGANIZATION
- Define the AI strategy.
- Clarify value prop for analytics.
- Plan analytics staffing, roles, and skillsets.
- Incorporate analytics into business processes.
- Drive adoption of analytics.
