My services focus on data & AI strategy and leadership, having worked in hands-on roles as a data scientist, data engineering leader, data product director, data director at Microsoft, and most recently as a CIO responsible for global data and IT capabilities.
The services below can be set up on a fractional basis or full-time engagement based on my availability. Services are grouped in the following areas:
- Data & AI Strategy – evaluating the organization’s business strategy and priorities to design a comprehensive data & AI strategy to deliver the business outcomes.
- Data & AI Product Management – I view everything as a product – whether it’s a capability for internal use or a product consumed externally by customers. Product management starts with envisioning, building, and bringing it to life for users to derive value. This includes the build function, change management, and go-to-market strategies to monetize the product.
- Data & AI Operations – ensures the availability, performance, and quality of data & AI products.
- Data & AI Supplier Management – strategic selection and relationship with key data & AI software suppliers and system integrators.
- Data & AI Talent – organization design and development of top-tier data & AI talent.
- Leadership and Performance Coaching – elevating the performance of data & AI leaders to transform the business.
Data & AI Strategy
Objective: develop and manage a comprehensive data & AI strategy that aligns with and enables the organization’s business strategy and outcomes.
Deliverable: The scope of this deliverable is based on the complexity of the business strategy and the range of data & AI capabilities required to deliver the top business outcomes. The primary intent is to assess the current strategy and capabilities with recommendations that will inform a comprehensive roadmap of technology, processes, and people. This deliverable is primarily a strategic document with recommendations to guide an implementation
1 – Business and Data Strategy Analysis
- Contextualization: Review the organization’s strategic plan and top priorities to determine where and how data and AI capabilities are critical to delivering business outcomes.
- SWOT Analysis: Identify the internal strengths and weaknesses of the organization’s data & AI capabilities and external opportunities and threats. Rank the material risks and issues that must be addressed to accelerate and mature the organization’s data & AI capabilities.
2 – Epic/Use Case Definition & Prioritization
- Prioritization: Review/recommend a use case prioritization scoring approach.
- Epics/Use Cases: Collaborate with stakeholders to review/create an inventory of Epics and Use Cases for the top business priorities.
3 – Data & AI Technology & Process Capability Assessment
- Capability Maturity: Evaluate the current maturity and state of data and AI capabilities and set a target for the required maturity given the organization’s business strategies and goals. This will guide the investment plan and roadmap.
- Data Management: Evaluate the maturity of metadata, reference data, and master data capabilities and how data management processes are integrated throughout the technology infrastructure.
- Data Governance: Assess the processes, policies, people, and tools to ensure data stays reliable and secure.
- Data Quality: Assess the processes, tools, and organizational capability to detect and remediate data quality defects.
- Data Warehousing: review the current data warehousing strategy and technology and make recommendations given the business strategy.
- Analytics: Evaluate the current descriptive, predictive, and prescriptive analytics capabilities and outline recommendations.
- Data Privacy: Review the data processes to comply with privacy regulations such as GDPR and CCPA.
- Data Security: Review data security strategies, policies, and processes that protect access to data within data infrastructure and enterprise applications.
4 – Data & AI Talent and Culture
- Data Organization: Evaluate the current organization design for data & AI professionals and leaders, and recommend changes that align with the future state vision.
- Data & AI Culture: Assess the current state of the data & AI culture, and recommend strategies to mature an inclusive data-driven culture.
- Data & AI Strategy Sponsorship: Recommend and operationalize a cadence and process for a cross-functional set of leaders to sponsor and oversee the data strategy implementation.
5 – Data & AI Strategy Roadmap and Recommendations
- Data & AI Roadmap: Review the existing roadmap and/or create a sequencing of capabilities to align it with the business strategy and key dates.
- Technology Selection: Design selection criteria, conduct evaluations, and select technology suppliers.
- Implementation Partners: Recommend firms to implement the data and AI strategy recommendations.
- Change Management: Evaluate the scope of the data strategy and its impact on change throughout the organization to recommend a change management strategy.
Data & AI Product Management
Objective: envision, build, and operationalize data & AI products and services (internal and external) that build out the data strategy.
- Product Vision: articulate a data & AI product vision that sets a north star and product pillars for future capabilities.
- Product Strategy: collaborate with key stakeholders to envision candidate data & AI products and services for internal and external use.
- Use Cases: elicit customer end-user requirements to document high-priority use cases and stack rank/sequence for the data & AI roadmap.
- Prototypes/MVPs: collaborate with designers, engineers, and early adopter users to envision and build working prototypes and minimal viable products (MVP).
- Build Process: review and optimize the build function to deliver quality, secure, and safe data & AI products and services.
- Go-To-Market: design pricing, sales and marketing strategies to drive user adoption, credibility, and revenue.
- Partnerships: identify, select, and negotiate relationships with ecosystem partners to monetize data & AI products.
Data & AI Operations
Objective: operationalize processes to ensure availability, performance, and quality of data and AI services inside and outside the organization.
- Data Observability: Evaluate the maturity and capability to ensure data is accurate, reliable, and usable. This includes monitoring data flows, DQ metrics, anomaly detection, data lineage, metadata management, and auditability.
- DataOps Service Desk: Assess the capability of a service desk function to measure and remediate data quality defects and implement solutions that address the root cause.
- DevOps: Evaluate processes and talent operating the data infrastructure to identify risks and optimizations.
- MLOps/LLMOps: evaluate processes and tools for managing machine learning and LLM operations.
- Cost Management: Evaluate monthly operating costs for data & AI capabilities and recommend alternatives for optimizing spend.
Data & AI Supplier Management
Objective: strategically select the right technology and manage supplier relationships to deliver business outcomes and create mutual value for both organizations.
- Selection: collaborate with data experts to prepare requirements, RFP, and criteria for technology selection. Lead or support selecting the primary supplier by collaborating across stakeholders.
- Relationship Management: manage the executive relationship with technology suppliers to optimize cost, maximize value, and leverage the supplier’s expertise and investment in the organization’s strategy.
- License Agreements: review current license agreements and influence contract renewal to optimize cost and terms.
- Technology Portfolio Optimization: analyze licensed tools and services to rationalize and reduce the total cost ownership,
I have experience working with these technology products and suppliers:
- Software: Adobe, Atlassian, Asana, AWS, Cloudera, Databricks, data.world, Foursquare, Gartner, GoDaddy, Google, Lotame, Microsoft, Neo4j, Informatica, OneTrust, Oracle, Phunware, Precisely, Safegraph, Salesforce (MuleSoft, Sales, Service, Tableau), ServiceNow, Snowflake, Tibco
- Consulting: Accenture, Applaudo, Avanade, KPMG, Mouri Tech, phData, Slalom, Tata
Data & AI Talent
Objective: build a data-driven culture and organization design to develop top talent capable of delivering on the data & AI strategy with excellence.
- Org Design: evaluate the current organizational design and recommend changes to align with the data strategy and culture.
- Talent Strategy: design a technical career track and processes for data professionals to enable professional growth and retention.
- Assessments: conduct leadership assessments to identify strengths and opportunities for growth essential to personal and organizational success.
- Coaching: partner with technology professionals and leaders in an ongoing creative coaching process to maximize personal and professional potential. I am an ICF-certified performance and leadership coach. See leadership and performance coaching for more details.
- Change Management: coach leaders and/or guide the change management process with strategies and tactics given the change introduce by data & AI capabilities.
- Talent Sourcing and Selection: recommend external candidates and participate in the interview and selection process for hiring data professionals and leaders.