Data & Analytics Enterprise · Sustainability

Sustainability data operations, automated from scattered sources.

A data transformation engagement covering sustainability datasets, certification workflows, CRM operations, sales and marketing extraction, event data, dashboards, and cross-functional analytics leadership.

10+

Roles Supported

50+

Interviews Led

Data

Driven Decisions

Data Engineering BI Dashboards Sustainability Analytics Automation & CRM

About the client

CommonShare operates in the certification and sustainability ecosystem, where business-critical information often lives across disconnected files, CRMs, internal systems, external sources, and client-specific datasets.

The work focused on turning fragmented sustainability, certification, sales, marketing, and events data into structured workflows, automated pipelines, dashboards, and decision-support systems.

Goals

Centralize scattered sustainability and certification datasets into reliable data workflows.

Automate extraction, transformation, validation, and reporting processes.

Build dashboards and analytical views for leadership, sales, marketing, events, and operations teams.

Lead data teams, cross-functional communication, recruitment, and data-driven decision enablement.

The Need

To create a scalable data operations layer around CommonShare's sustainability and certification work — transforming scattered information into usable intelligence for client delivery, sales, marketing, events, CRM operations, and executive decision-making.

Challenges

What we had to solve

Fragmented
Data Sources

Certification, sustainability, CRM, marketing, sales, and event data lived across multiple formats and systems, requiring clean extraction and consolidation.

Pipeline & Script
Reliability

Automated workflows had to handle inconsistent source data, transformations, recurring exports, and business-critical delivery requirements.

Cross-Team
Data Needs

Different teams needed different outputs: leadership dashboards, sales lists, marketing segments, event datasets, CRM views, and operational reporting.

Sustainability Data Quality

Certification and sustainability data needed strong quality control, normalization, and validation before it could be used for analysis or client-facing operations.

Scaling
Data Teams

Beyond technical delivery, the work required leading data scientists, hiring new talent, and translating business needs into executable data initiatives.

Solution Development

What we built

A complete data function spanning engineering, analytics, sustainability data management, CRM support, team leadership, recruitment, and business enablement.

Data Engineering & Automation

For internal operations

Data Pipelines & Scripts

Created scripts and pipelines to extract, clean, transform, merge, and manage scattered sustainability and certification data.

Data Consolidation

Turned disconnected files, CRMs, client datasets, and operational sources into structured datasets usable by multiple teams.

Process Automation

Reduced repetitive manual work by automating recurring extraction, manipulation, preparation, and reporting tasks.

Analytics, Teams & Business Support

For decision makers

BI Dashboards

Built dashboards and KPI views that helped teams understand performance, track operations, and make data-driven decisions.

Sales, Marketing & Events Data

Prepared targeted datasets, segmentation outputs, campaign lists, event data, and reporting extracts for commercial and marketing execution.

Data Team Leadership

Led the Data Science team, coordinated delivery across departments, and conducted around 50 interviews for data, marketing, and related roles.

Analytics & Reporting

Decision support

Dashboards & BI

Interactive reporting systems for leadership, operational teams, sustainability workflows, sales, marketing, and events.

Data Storytelling

Translated complex sustainability and operational data into clear visuals, KPIs, and actionable insights.

Business Enablement

Helped non-technical teams understand, trust, and use data in day-to-day decision-making.

Engineering & Systems

Automation layer

Python / Scripts

Automation scripts and data-processing workflows for extraction, transformation, validation, and recurring business outputs.

Databases & CRMs

Structured storage, CRM management support, data models, operational lists, and cross-system data preparation.

Leadership & Delivery

Team scale

Recruitment

Led approximately 50 interviews and supported hiring for 10+ Data Science roles plus marketing and business-related positions.

Cross-Functional Management

Managed communication between data, sustainability, sales, marketing, events, operations, and leadership teams.

Tech Stack

How we built it

A practical data stack built around automation, structured storage, analytics, reporting, CRM support, and team-scale workflows.

Achieved Results

The outcome

Unified

Scattered sustainability, certification, CRM, sales, marketing, and events data became usable through structured pipelines, scripts, dashboards, and data workflows.

10+

More than 10 data roles were supported through recruitment, interviews, evaluation, onboarding input, and team leadership.

Less

Manual extraction, cleaning, reporting, segmentation, and preparation work was reduced through automation and reusable data processes.