Assessment use cases


For a company that decides to build or revise its own Data Strategy, it is essential to dedicate time to reflecting on the motivations that should guide the new strategy, which key processes will be impacted, how to adapt its technology stack to a new architecture, and which use cases to identify in order to test the new approach to data.

Over the years, Quantyca has gained extensive experience in guiding and advising its clients in defining and adopting a new Data Strategy (which has become increasingly dynamic and global over the years) and has collected the lessons learned and best practices in a process proposed to its clients as part of designing a Data Strategy that is articulated in four phases:

  • Assessment
  • Foundation
  • Mobility
  • Execution

The assessment phase is a planning process for the new Data Strategy, done collaboratively with the client. As cloud-native use cases or migrations to the cloud increase, managing environments and security becomes more challenging. It becomes essential to have a tool capable of effectively distributing and controlling corporate policies.

The Cloud Foundation represents a set of configurations, templates, and automations designed to manage connectivity, identities, and security of cloud environments and always keep costs under control.

Quantyca diagramma di Data Strategy in 4 fasi: Assessment, Foundation, Mobilitization, Execution image


On this page, we will see in detail what the assessment phase consists of: a planning process for the new Data Strategy, done collaboratively with the client to jointly identify critical points, business and technical needs, use cases from which to begin a new Data Strategy, and streamline the adoption of new processes.

Punti Critici

The opportunities to be seized in the field of data are multiplying so quickly that many companies feel like they are struggling to keep up. Sometimes, this leads the company to enthusiastically dive into new projects and strategies, without a planning and needs mapping phase, investing in new technologies and fanciful analyses that are not suitable for their business. The consequences of this momentum can be low-return investments or strategies that require immediate revision or significant change. In any case, these companies do not fully realize the possibilities that their data can offer.

The main consequences of the lack of an assessment phase during the selection and adoption of a data strategy are:

→ Lack of a global company-wide Data Strategy, with a broad and comprehensive outlook that is not subject to continuous changes in direction.

→ Proliferation of Data Strategies that are too limited or vertical to a single use case, which are conducive to creating data silos and business function silos.

→ Loss or lack of interest from business stakeholders in strategic data projects.

→ Low ROI on company data projects.

Quantyca has developed a time-boxed assessment methodology to evaluate the current data context (maturity and readiness) of its clients and collaboratively build a minimum viable strategy that enables data-driven business decisions and maximizes the ROI of the Data Strategy.

The assessment proposed by Quantyca is designed to adapt to the different needs and organizations of its clients. Once the team responsible for analyzing the possible Data Strategy is identified, there are two ways to proceed:

We start from a specific case (a simple or high ROI use case) and then expand the analysis to the most relevant elements for the objectives of the specific type of assessment.

We start from the big picture and then gradually narrow down to the most relevant elements for the objectives of the specific type of assessment.


The complete assessment process covers and delves into all the most relevant technical, business, and organizational aspects:

  • Strategy & Motivation
  • Business architecture
  • Information System Architecture
  • Use Case mapping
  • Platform blueprint
  • Transition plan

with the aim of preventing and reducing possible risks that the adoption of a new Data Strategy may entail, following the Agile ROAM methodology (Resolved Owned Accepted Mitigated).

The complete route

Definition of motivations, expectations, principles and key functions
High-level analysis of key impacted processes
Analysis of the as-is architecture from an application, information and infrastructure perspective
Mapping of use cases impacted by the platform (enabling and enabled)
Definition platform blueprint (architecture, tools and running costs)
Definition of governance model and high-level roadmap
Quantyca diagramma percorso completo dell’assessment


Monitoring and optimisation of activities and critical points
Efficient, shared and wide-ranging data strategy
Reducing operating costs
Greater agility of development

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