Data & AI Strategy Assessment

Data & AI Strategy

Data Platform

Data Governance

Design a Data & AI Strategy aligned with business objectives in order to extract and democratize the value of data.

Industries:

Finance & Insurance - Retail & FMCG - Transportation - Energy & Utility - Life Science - Industrial

Solutions:

Data & AI Strategy - AI - Data Platform - Data Governance

Background

In a constantly evolving social and technological landscape, it is becoming increasingly difficult to understand what truly holds value for companies and where to begin in building an effective data strategy.

Opportunities related to data are growing so rapidly that many organizations often feel overwhelmed trying to keep up. As a result, they may eagerly jump into new projects and strategies without a preliminary phase of planning and needs assessment—investing in technologies and complex analyses that ultimately do not fit the company’s actual context. This can lead to low-return investments or strategies that soon require major revisions or even a complete overhaul.

The need to build or revise a data strategy is becoming ever more pressing, especially considering the growing impact of Artificial Intelligence on business processes. It’s no surprise that the accuracy of AI agent responses largely depends on the quality and semantic richness of the underlying data.

But how can organizations harness the full potential of their data in such a dynamic and competitive environment?
What are the main risks of lacking a clear and shared data strategy?

Challenges

The main consequences of lacking a data strategy are:

Lack of alignment with the corporate strategy and business objectives, leading to the perception of the Data Management function as a cost center rather than a value generator
Proliferation of local, limited, or use-case-specific strategies, which create silos and shadow IT within business functions
Unreliable or unsustainable Artificial Intelligence solutions due to inconsistent, fragmented, or poorly governed data foundations
Inability to monitor and maximize the ROI of data-driven initiatives

Solutions

The Assessment activity aims to highlight business needs and define a clear, shared, and company-specific Data & AI Strategy, which can also serve as the foundation for the effective, responsible, and sustainable use of Artificial Intelligence.

This represents the first phase of a broader journey that Quantyca has developed over the years, supporting clients in the definition and adoption of a Data & AI Strategy. The complete journey consists of four phases:

1 Assessment

Identification of business needs and definition of a clear and shared Data & AI Strategy, evaluating both organizational and technological impacts.

2 Foundation

Implementation of the Minimum Viable Strategy, i.e., the essential foundations and rules from both an organizational and technological perspective.

3 Mobilization

Application of the new approach on a first use case to validate its effectiveness, collecting useful feedback from the selected stakeholders.

4 Scaling

Extension of the new approach to the entire organization, involving stakeholders from different areas and evolution of organizational and technological capabilities.

The Assessment phase, conducted in a time-boxed manner, has two main objectives:
• To evaluate the client’s current data context (maturity and readiness)
• To design the initial Data & AI strategy, known as the Minimum Viable Strategy, which enables new organizational and technological capabilities in an incremental and modular way, while monitoring ROI

Quantyca’s approach is designed to adapt to different needs in the Data & AI domain and to various organizational structures. It is structured into two main phases:
• As-Is Analysis: Analysis and deep-dive sessions to assess the current state from both an organizational and technological perspective. The outcome of this analysis will serve as the basis for identifying the GAPs to be addressed
• Data & AI Strategy Design: Joint sessions to define and review the new strategy and share the final results with stakeholders

As-Is Analysis

Structured and interactive sessions to analyze the current state from both an organizational and technological perspective, using a collaborative board.

Business Strategy & Motivation

Definition of motivations, needs, risks, and key business objectives to guide the development of the Data & AI strategy.

Business Architecture

Analysis of key processes, organizational models, team structures, and interactions (e.g., Team Topology).

Business Model Canvas. Fonte: miro.com

Information Architecture

Analysis of the information architecture: high-level modeling of core business concepts and their connection to data systems.

Technological Architecture

Mapping of the existing technological platform, application flows and main data flows.

Use Case Analysis

Analysis of the most relevant and most valuable use cases, useful for prioritizing strategic activities.

Data & AI Strategy Design

The entire analysis path culminates in the design of the Data & AI Strategy and the main elements: vision, objectives, programs and success metrics.

Today more than ever, a well-designed Data Strategy is the essential prerequisite for developing a concrete and scalable AI Strategy. Vision, metrics, tools and governance must be designed to bring data and artificial intelligence together in a synergistic way, guaranteeing both the quality of results and the transparency of automated processes.

To guide the Data & AI Strategy design process, Quantyca adopts a revised version of the Lean Value Tree, which is a dynamic and collaborative tool that allows you to share and guide a company’s strategy and vision at all levels, departments and teams. The Lean Value Tree represents the basis for the subsequent phases and for the management of the strategic portfolio.

Deliverables

The main deliverables of the Assessment are:

Executive Summary: a summary of needs, benefits, impacts, and priority actions
Solution Blueprint: GAP analysis of technological and architectural capabilities
Governance Framework: GAP analysis of organizational capabilities, roles, and responsibilities, with a proposed framework and management and coordination processes
Roadmap: incremental and modular transition plan toward the target data strategy

These deliverables are not only functional for building a modern data platform but also represent the foundation to enable scalable and reliable AI initiatives, thanks to an integrated vision of data, processes, and intelligent technologies.