ESG and Sustainability

Our pledge

Digital Futures is committed to sustainability whilst we work to help our clients meet their ESG goals

Carbon neutrality

We take our environmental responsibility seriously and thus monitor our carbon footprint closely, collecting carbon emissions data on an annual basis in order to help us offset our carbon emissions, with an aim of to be carbon neutral by 2025.

Social value

We are driven by the principles of diversity, equity, and inclusion with a mission to create opportunities for individuals from all backgrounds. Our work is centred around attracting and retaining a digital workforce that is truly representative of society.

Transparent governance

We strive to be a trusted partner for our clients and suppliers, with an ethos centred around good and responsible governance throughout all of our operations. We are employee owned and have a meritocratic culture based on trust and communication.

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We provide the end-to-end capability to make your ESG agenda a reality

Take a data-driven approach

We help our clients create and execute on their ESG agendas to meet the continuously evolving demands of both regulators and their customers by providing the critical technical and business capability required to meet organisations’ ESG goals.

We provide capability across data engineering, data analytics, data science and business analysis to support the creation and productionalisation of data assets to spearhead the net zero agenda.

We train and develop highly diverse, skilled and motivated talent in the key skills across data, cloud and change management, ready to deliver on ESG goals for our clients.

Closing the ESG data gap

Digital Futures provides the end-to-end technical and business capability to help your organisation sit at the cutting edge of ESG strategy

Data Engineer

Data engineers are required to design, build, and test data pipelines and infrastructure to collect, consolidate, clean and structure ESG data. Data engineering is the vital first stage in bridging the ESG data gap before analysis can begin.

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Data Analyst

Data analysts use ESG data to develop scenario analysis, to develop forecasting and risk management, analysis of performance against organisational ESG targets and to identify powerful opportunities within ESG for organisations.

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Data Scientist

Data scientists' work is critical in streamlining ESG data analytics. They also work with remote sensing ESG data in order to develop machine learning models which are used to tackle the challenges to achieving net zero vision.

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Insights

Learn about the work we’re doing across ESG

Article: Powering sustainability through digital skills

Read article

Case study: Creating a diverse data engineering capability

Read case study

Case Study: Providing data specialist resources to support the ESG agenda of a global financial institution

Read case study

"Digital Futures engineers have been a huge asset to the ESG team since joining. They have settled in very quickly, are technically​ very capable, are inquisitive, challenge the status quo, and encourage the broader team to share knowledge in order to drive collaboration."

Site Reliability Engineer, Global Bank

"The Digital Futures data engineers that have joined our ESG team have been very enthusiastic and engaged from the outset. They demonstrate clear ownership of all deliverables and have been quick to escalate and problem-solve issues as they arse, and see them through to resolution."

Engineering Lead, Global Bank

"In the last year, the Digital Futures engineers that have joined us have been an asset to the ESG data engineering chapter. They have been able to quickly absorb new technical skills and ways of working, including Agile practices, DevOps principles and the Scala programming language."

Engineering Manager, IT Services Provider

"The Digital Futures engineering team has been instrumental in delivering a key project. They demonstrate excellent values, not only in getting the job done, but also in doing it the right way, taking great care about the quality
of work they deliver."

Site Reliability Engineer, Global Bank
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Digital Futures

Role: Data Engineer

Data engineers are required to design, build, and test data pipelines and infrastructure to collect, consolidate, clean and structure ESG data. Data engineering is the vital first stage in bridging the ESG data gap before analysis can begin.

Tools & Methodologies
Skills & Technologies
Application in closing ESG Data Gap

Artificial Intelligence (AI)
Machine Learning (ML)

GCP ML
AWS ML
Python

ESG data comes from in both structured and unstructured formats. AI and ML will allow the automation of collection of this structured and unstructured data from a wider variety of sources.

Data Acquisition

Knime

Leverage new primary and secondary data sources to increase and enhance collection of raw ESG data.

Extract-Transform-Load
(ETL)

Talend
PostgreSQL

Centralise raw data from multiple ESG data sources into one repository to prepare data for analysis.

Data Pipeline

Apache Airflow
SQL
PySpark
Spark
Scala

Design efficient data pipelines to automatically process, transform, and move large amounts of ESG data between source systems and target repositories.

Data Quality

Talend

Measuring how well ESG datasets meet the criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose.

Big Data

PySpark
Hadoop

Large, varied and complex ESG data sets require more powerful big data processing, analysing, and visualizing techniques.

Cloud Services

AWS
GCP

Leveraging cloud computing power to build cost-effective and scalable data pipelines and to manage data storage and retrieval at scale.

Data Modelling

Data Warehousing
PostgreSQL

Design and implement efficient data models that capture the structure, relationships, and constraints of complex ESG data domains in a clear and concise manner, ready for analysis.

Digital Futures

Role: Data Analyst

Data analysts will use ESG data to develop scenario analysis, forecasting and risk management, analysis of performance against organisational ESG targets as well as to identify powerful opportunities within ESG.

Tools & Methodologies
Skills & Technologies
Application in closing ESG Data Gap

Data Visualization

Tableau

Represent ESG data through data visualisations to communicate complex data relationships and provide actionable insights and support data-driven decision-making within an ESG context.

Predictive Modelling

Python
Pandas
Scikit-Learn

Produce robust predictive models with a clear understanding of model performance for use in risk forecasting and prediction.

Machine Learning (ML)

Tensorflow Classifiers
Scikit-Learn

Employ supervised and unsupervised learning techniques to develop models from labelled and raw ESG data respectively.

Data Analytics

PostgreSQL
Tableau
PySpark

Understand the patterns within ESG data, changes over time in business metrics and deliver on regulatory requirements.​

Business Intelligence and
Stakeholder Engagement

Tableau
PostgreSQL

Increasing knowledge dissemination by combining methodical thinking and business acumen to transform ESG data into information that can be confidently communicated to all stakeholders.

Digital Futures

Role: Data Scientist

Data scientists work with remote sensing ESG data in order to develop machine learning models which are used to tackle the challenges to achieving net zero vision.

Tools & Methodologies
Skills & Technologies
Application in closing ESG Data Gap

Artificial Intelligence (AI)

Natural Language
Processing

NLP offers a huge amount of potential from an ESG perspective. Insight can be extracted from unstructured data sources, such as communications data, but can also be used to interrogate corporate information, all of which will be rich in ESG insight.

Data Science

Mathematics
Statistics

Applying mathematics, statistics and data mining techniques across numerical, text and time series data to gain actionable insights that generate value
in an ESG context.

Model development and governance

Python
Git

Handling all parts of the modelling lifecycle, including building, approving, back-testing, calibrating & deploying models across a range of on-prem and cloud-based environments, maintaining a strong audit trail and minimising model risk​.

Machine Learning (ML)

Tensorflow Classifiers
Sci-Kit Learn

Using supervised and unsupervised learning techniques to produce models and evaluate model performance to produce accurate, traceable, transparent and ethical predictions e.g. risk, climate modelling.