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Data & Analytics

Data integration solutions, data modeling, predictive and preventive analytics.

Data & Analytics 

Transform data into insights, drive decisions, enable innovation.  

In an increasingly competitive and interconnected market, the ability to extract value from data is a strategic advantage. Data is no longer a byproduct of business activities, but a key asset for optimizing processes, anticipating trends, and enabling informed decision-making.

Technological evolution – from Artificial Intelligence to cloud computing and advanced analytics platforms – now makes it possible to turn large volumes of information into actionable insights, improving efficiency, service quality, and the speed of operational decisions.
We support organizations in building data-driven ecosystems capable of enabling more agile, high-performing, and predictive business models.
We design modern data architectures, advanced analytics models, and AI and Machine Learning solutions that enhance analytical capabilities, improve data governance, and foster evidence-based decision-making.

We help companies place data at the core of their strategy, transforming it into a tangible driver of growth, innovation, and competitiveness.

Trasforma i dati in insight, guida le decisioni, abilita l’innovazione.

How we transform data into value

Through an integrated and multidisciplinary approach, the Data & Analytics Competence Center combines Data Engineering, Advanced Analytics, and Artificial Intelligence, offering an end-to-end vision – from data collection to strategic value creation.

Assessment

We assess data maturity and information sources.

Target architecture design

With scalable solutions.

Data ingestion and integration

From heterogeneous sources, including real-time data.

Data preparation

And definition of business KPIs.

Development of analytical and predictive models

Based on Machine Learning and AI techniques.

Advanced dashboarding

To monitor performance, trends, and anomalies.

Continuous improvement

Metrics and indicators to support ongoing adaptation.

Tangible benefits for organizations:

  • Anticipate trends and anomalies, turning reactivity into proactivity;
  • Reduce decision-making time, thanks to continuously updated insights;
  • Make processes more efficient by eliminating data silos and redundancies;
  • Support strategic planning through predictive and scenario-based models.

Use cases

Integrated Data Monitoring and Advanced Reporting

Enterprise

Objective

To create a comprehensive and reliable reporting system to monitor governance and supplier economics, integrating data from multiple sources and enabling dynamic, shareable dashboards.

Solution

  • Data integration and import from Power Apps, SQL Server, and SAP;
  • Data preparation activities and KPI definition;
  • Design and implementation of dynamic Power BI reports;
  • Definition of security roles and user profiling;
  • Configuration and management of BI Service environments;
  • Publishing and sharing reports in collaborative environments.

Result

  • Greater visibility and control over economics and governance;
  • Reliable and traceable data through structured pipelines;
  • Reduced time spent on manual activities;
  • Consistent and centralized reporting, available in real time.
Integrated Data Monitoring and Advanced Reporting

AI for Automated Ticket Classification

Enterprise

Objective

To automatically analyze support ticket texts in order to identify recurring issues, improve service quality, and reduce resolution times.

Solution

  • Unsupervised analysis and clustering of the ticket dataset;
  • Identification of key topics and high-incidence areas;
  • Development of AI models for the automated classification of future tickets;
  • Monitoring dashboards to visualize volumes, categories, and trends.

Result

  • Greater speed and accuracy in classification;
  • Proactive identification of operational issues;
  • Reduced average handling times (AHT);
  • A fully scalable and automated process.
AI for Automated Ticket Classification

AI for Public Transport Safety

Infrastructure & Transportation

Objective

To optimize public transport safety inspections through the automated identification of structural anomalies using Artificial Intelligence algorithms.

Solution

  • Object recognition and component classification through AI models;
  • 3D reconstruction based on photographic images;
  • Alerting system for the timely detection of anomalies and potential risks;
  • Integration with management systems to prioritize interventions.

Result

  • Faster, more accurate, and fully traceable inspections;
  • Reduced operational risks and inspection times;
  • Improved safety and service continuity;
  • Enablement of predictive maintenance models.
AI for Public Transport Safety

Data Lakehouse and Application Monitoring

Infrastructure & Transportation

Objective

To centralize and analyze all reports and application issues managed by FSTech, integrating data from heterogeneous sources – including Azure DevOps pipelines – and building an advanced data environment for proactive anomaly monitoring.

Solution

  • Implementation of a Databricks Data Lakehouse based on a Medallion architecture (Bronze, Silver, Gold);
  • Multi-source data ingestion (Azure DevOps, application APIs, operational databases);
  • Data transformation, normalization, and correlation within the Silver layer;
  • Analytical modeling and definition of service KPIs within the Gold layer;
  • High-complexity Power BI dashboards to visualize anomalies, SLAs, trends, and operational performance.

Result

  • A unified, real-time view of reports and application issues;
  • Improved data quality and consistency;
  • Reduced analysis time and identification of recurring patterns;
  • Proactive performance monitoring and support for operational decision-making.
Data Lakehouse and Application Monitoring

Nethex – Service Desk Automation with AI

Enterprise

Objective

To develop an advanced Service Desk capable of automating ticket routing and improving request management efficiency, reducing response times and operational workload for agents.

Solution

  • Development of an Artificial Intelligence engine for the automated analysis of requests and their correct routing to the appropriate teams;
  • Integration with existing ticketing platforms;
  • Implementation of a dashboard to monitor performance KPIs, configure AI parameters, and evaluate effectiveness in real time.

Result

  • Reduced ticket assignment time and increased Service Desk productivity;
  • Improved accuracy in automated routing;
  • Continuous monitoring of AI performance through KPIs and operational feedback.
Nethex – Service Desk Automation with AI

Automated Expense Report Dematerialization

Infrastructure & Transportation

Objective

To simplify and accelerate expense report management, reducing data entry errors and manual processing time.

Solution

  • Development of an AI- and OCR-based solution for automated expense recording via receipt image capture;
  • Integration with corporate accounting and management systems;
  • Automated compliance checks and approval rule validation.

Result

  • Significant reduction in expense report processing time;
  • Elimination of paper-based processes and full traceability of receipts;
  • Improved data accuracy and administrative compliance.
Automated Expense Report Dematerialization

Other skills

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Our Industries

Public Sector – Central Government

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Public Sector – Local Government

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Healthcare

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Infrastructure & Transportation

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Energy & Utilities

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Enterprise

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What we do

Capabilities

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FAQ

Un Competence Center di Data & Analytics supporta le organizzazioni nel costruire ecosistemi data-driven, trasformando grandi volumi di dati in insight azionabili. Opera fornendo consulenza strategica sulla maturità dati, progettando architetture dati moderne, sviluppando modelli analitici e predittivi basati su Machine Learning e Intelligenza Artificiale, e implementando soluzioni di dashboarding evoluto. L'obiettivo è abilitare modelli di business più agili, performanti e predittivi, mettendo i dati al centro della strategia e trasformandoli in uno strumento concreto di crescita, innovazione e competitività.

Un progetto di Data & Analytics segue un approccio end-to-end strutturato: assessment della maturità dati e delle sorgenti informative, progettazione dell'architettura target scalabile, ingestion e integrazione dati da fonti eterogenee anche in tempo reale, data preparation e definizione dei KPI di business, sviluppo di modelli analitici e predittivi basati su Machine Learning e AI, implementazione di dashboarding evoluto per il monitoraggio, e continuous improvement con indicatori e metriche per l'adattamento continuo. Questo approccio integrato garantisce che i dati diventino progressivamente una risorsa strategica sempre più sofisticata e allineata alle esigenze di business.

I progetti di Data & Analytics moderni utilizzano uno stack tecnologico variegato in base agli obiettivi specifici: piattaforme cloud come Azure e AWS per la scalabilità; soluzioni di data integration e ETL; database relazionali e data warehouse; tecnologie Big Data e Data Lake per la gestione di grandi volumi; librerie di Machine Learning e AI per la modellazione predittiva; strumenti di advanced analytics e visualization come Power BI, Tableau e Databricks; algoritmi di OCR e object recognition per l'automazione intelligente. La scelta dipende dall'architettura target, dalla complessità dei dati e dai casi d'uso, privilegiando soluzioni scalabili, sicure e interoperabili.

L'efficacia di una soluzione Data & Analytics si misura attraverso metriche concrete e business-oriented: capacità di anticipare trend e anomalie, trasformando la reattività in proattività; riduzione dei tempi decisionali grazie a insight sempre aggiornati; miglioramento dell'efficienza dei processi eliminando silos informativi e ridondanze; supporto alla pianificazione strategica attraverso modelli predittivi e di scenario. Altre metriche critiche includono la qualità e tracciabilità dei dati, la velocità di ingestion e processing, l'accuratezza dei modelli predittivi, e il ROI generato dalle decisioni guidate dai dati.

Un Competence Center di Data & Analytics riunisce competenze multidisciplinari: data architect che disegnano le architetture dati, data engineer specializzati in ingestion e integrazione, data analyst esperti di modellazione e KPI, data scientist che sviluppano modelli di Machine Learning e AI, specialist di advanced analytics e visualization (Power BI, Tableau), esperti di governance e qualità dati, e consulenti di business che traducono esigenze aziendali in strategie dati. A questi si affiancano AI/ML specialist e security expert per garantire robustezza e conformità normativa.

Soft Strategy, attraverso il suo Competence Center Data & Analytics, combina expertise tecnico approfondito con comprensione del contesto aziendale dei clienti. Adotta un approccio integrato e multidisciplinare che parte dall'assessment della maturità dati e prosegue fino alla valorizzazione strategica. Non agisce come semplice fornitore di tool, ma come partner strategico che identifica opportunità di valore nascoste nei dati e le trasforma in modelli predittivi e decisioni consapevoli. Garantisce elevato grado di customizzazione, trasparenza durante tutto il ciclo e risultati misurabili orientati alla crescita e all'innovazione del cliente.