AI and data analytics to improve lives in fragile and conflict-affected states.
Captivate Analytics is a data science and AI consultancy. We specialise in data analytics and AI services in complex environments. Our team brings a combination of expertise in data science, machine learning and conflict, security and development.
Data Science
Machine Learning
Conflict, security and development
The Problem
Too many organizations use outdated research and reporting methods that are no longer fit for purpose. Large teams manually compile long text-heavy reports that rarely get read or influence decisions.
How we can help
Our products and services enable clients to use more visual, efficient and data-driven research. We bridge the gap between data science, machine learning and international development, and enable decision-makers to find the signal in the noise.
Visual insights
No more 50-page text-only reports that you do not have the time to read
Data to inform decisions
Relevant and useful research products that are tailored to decision-making needs
Find efficiencies
Reduce costs and deploy scalable and reusable products
Social media & media analytics
Track online networks and disinformation, and use media data for real-time reporting.
Data & AI strategy
Identify opportunities to utilise data and machine learning for greater efficiencies and improved decision-making.
Automated reporting & dashboards
Drastically reduce time spent on inefficient manual excel and word report production through data engineering, automated reports and dashboards.
Forecasting
Predict the probability of future outcomes to inform planning and strategy.
Numerous organisations talk about the potential of innovative technologies to improve lives in fragile and conflict-affected states, but very few implement them in practice.
Decision-makers in government, the aid sector and private sector recognize the importance of data-driven decision making. However, it remains hard to make the right decisions:
- Despite the ever-increasing amount of data available – from social media to satellite imagery – it is difficult to separate the signal from the noise.
- A lot of research is too slow-moving to influence decisions, or presented in long text-heavy reports that decision-makers do not have the time to read.
- There are understandable and valid concerns about potential bias, data ethics and accountability for rapidly evolving technologies such as machine learning.
We founded Captivate Analytics in 2021 to help address these challenges. By combining cutting-edge data science and contextual understanding – underpinned by data ethics – we produce research that enables better decisions and improves lives in fragile and conflict affected states.
According to the World Bank, 31% of their reports have never been downloaded once.
Source: World Bank
“More data means that we need to be even more aware of what the evidence is actually worth.”
David Spiegelhalter, The Art of Statistics: Learning from Data
Data ethics
Our approach to data science includes multiple steps of quality assurance to identify and mitigate any potential ethical concerns, and we actively follow guidance on data ethics from thought leaders such as the Alan Turing institute and Ada Lovelace institute.
Conflict sensitivity
We recognize the importance of do-no-harm principles in fragile and conflict-affected states, and the danger of inadvertently worsening rather than solving problems without a conflict sensitive approach.
Social inclusion
We believe that inclusive approaches to technology and research have a far higher positive impact. We prioritise local expertise to guide our work, and have social inclusion requirements for our teams.
Transparency
We do not produce ‘black box’ algorithms that undermine accountability in decision-making, and share our code with clients to ensure transparency. We actively highlight potential bias and limitations in any data used.
Reproducibility
In line with scientific best practice, we seek to ensure the reproducibility of all our research. We apply transparent and honest approaches to research.
Felix Jackson
Co-founder and Data Scientist
Felix is a PhD student in the computer science department at the University of Oxford, where his research focuses on the application of deep learning in healthcare. He is currently developing natural language processing and risk prediction models for the UK CSSF. He previously worked at AKTEK, where he led research and algorithmic development applying natural language processing and machine learning techniques for UK-funded programmes in fragile and conflict affected states. Felix has a Masters and BA in Natural Sciences from the University of Cambridge, and programmes in Python and R.
Alistair McMaster
Data Engineer and Software Developer
Ali specialises in data engineering and software, with experience building full stack data analytics pipelines in Health Tech and Finance. He previously worked for five years as a Strategist within the Investment Banking Division at Goldman Sachs, before leaving to join a software startup. He is a strong advocate of open source development and is a member of the core team of Pandas, and has also contributed to Python programming textbooks. Ali has a BA in Natural Science focusing on Astrophysics from the University of Cambridge, and programmes in Python & Java.
Samuel Crew
Data Scientist
Sam is a researcher in security and quantum information theory at the Max Planck Institute in Bochum, Germany. His research work includes information theoretic aspects of neural networks, cryptography associated connections with theoretical physics and quantum information. As a data scientist, Sam is working on risk prediction models for the UK CSSF, and programmes in Python and R. Sam has a PhD in theoretical physics from the University of Cambridge, a masters in theoretical physics from the University of Oxford and a BA in mathematics also from the University of Oxford.
Elliot Meador
Co-founder and Data Scientist
Elliot is an experienced researcher specialising in the application of data science methods in social science research in the developing world. A rural development specialist at Delta Health Alliance, Elliot recently supported the UK CSSF during the 2020 Comprehensive Spending Review and was lead data scientist on a CSSF data analytics project. He has extensive experience using statistics and machine learning using Big Data and survey research. Elliot has a PhD in Rural Sociology and Applied Statistics from the University of Missouri-Columbia, and programmes in R.
Ghaith Dkmak
Data Scientist
Ghaith is a Syrian data scientist with experience in international development, fintech and management consulting. He has built statistical models to better predict internal displacement in Syria for a UK-funded stabilisation programme, and is passionate about developing end-to-end machine learning solutions to solve real-world problems. Now based in London, Ghaith was a UK Chevening Scholar and has a Msc in Data Science and Data Analytics from Brunel University in London, and a BsC in Information Technology from SVU in Syria. Ghaith is fluent in English, Arabic and Turkish, and programmes in Python.
