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.