At Datasparq, we’re experienced AI practitioners who are passionate about delivering valuable, operational AI solutions at speed. Our teams combine a unique mix of data science, data engineering, product thinking and design.
We help businesses at every stage of their AI journey. From value discovery; to designing, training and building; to managing AI; we serve our clients as a trusted AI growth partner.
We identify opportunities for you to use your data to drive business improvements and deliver data analytics solutions that transform day-to-day operations.
This is about the practical application of data platforms, data and analytics, data science and machine learning to create real tools you’ll use every day to improve efficiency, effectiveness and, ultimately, create more value.
Datasparq people are unique. Just as no business challenge fits neatly into a box, neither do our people. Rather than simply hiring a data scientist or a data engineer, we seek out passionate experts who can bring a range of overlapping skills to any team.
This multidisciplinary ability is particularly useful when tackling business problems, each of which is different. We prefer to take a bespoke approach, putting together the right line-up to work together with you, rather than following a one-size-fits-all approach.
Our Data Scientists take cutting-edge mathematics and use it to tackle complex business challenges.Our Data Science capability->
Our Data Engineers build strong, reliable pipelines that support mission-critical business capabilities.Our Data Engineering capability ->
Our Product people make sure that client teams get the maximum possible value from their new data tooling.Our Product capability ->
Every business is different, with different challenges specific to your industry, size and in-house functions. Here are a few of the typical challenges that our customers present to us:
In this situation we start by identifying the high value use-cases and priority requirements for your data platform. We’ll use DiagnostiQ to quickly assess the key gaps in your data capabilities and provide recommendations to ensure your data platform performs - now and in the future.
Everyone’s data requirements are unique (and often in a messy state), and there’s no one-size fits all. Our principles and blueprints for modern data platforms are proven, modular, and cloud-agnostic; allowing us to quickly design and build data platforms and services that not only meet the needs of your business today but are able to scale as demand for data in your business grows.
In a situation like this we’d recommend beginning with a SparQshop™ to identify high value and relevant applications of data and AI in your business.
We’ll uncover high-value opportunities by analysing your value-chain to identify and prioritise solutions where data and AI can address the highest value opportunities. It’s a great way to get clarity on where the bottlenecks are and how (and if) an AI solution can help. The sessions end with a paper prototype to demonstrate how AI could be operationalised within your business, and a delivery roadmap to show how it can become a reality.
Machine learning models are notoriously difficult to operationalise. What can seem relatively simple in theory can explode into complexity and brittleness when you plumb it into your production data and expose it to real users.
Datasparq runs ML models dealing with terabytes of fast changing data daily. Our approach includes automated model deployment, retraining, and testing; monitoring for early warnings of model degradation, and automated expectation testing of incoming data. ML can only be effective if its output is trusted and used. That’s why we ensure that any model predictions are explained in a manner and format that is understood by all users.
Datasparq brings years of experience in managing data processes. We run fully automated Business Intelligence projects for our clients, often enabling self-service for internal teams.
We can help you move your data and processes to the cloud, improving robustness and quality while removing the overheads of managing an on-premise platform. We provide self-service BI solutions to get the right data to the right people at the right time. This result is a more efficient process that equips people to make faster and better decisions.
We use a three-stage process for end-to-end value creation. It's founded on the principles of only tackling high-value challenges and getting results fast.
Define the opportunities: Before a line of code is written, we work with you to identify the high value problems and opportunities in your organisation. SparQshop™ is our workshop designed to identify, quantify and validate these opportunities. The output is a prioritised set of problems which can lend themselves to an AI solution, each with validated feasibility and quantified value.
Deliver proof of value: Rather than making assumptions about data quality and availability, ongoing maintenance costs, or business acceptance and usage, we test the riskiest assumptions early on. We build, train and test machine learning models using historic data to prove its ability to predict at an accuracy that will deliver value. The key to this phase is speed and learning fast. Our engineering and data science capabilities allow us to deliver a proof of value in weeks—not months.
Scale and sustain value: Once we’re confident in our approach, it's time to make it real. Data transformation, model training and prediction are all automated, repeatable and reliable. Xu our unique automated test framework, gives stakeholders confidence in the solution while notifying operations teams of any issues. Our Elements of Engineering provide the tools and templates to productionise fast while minimising unexpected hiccups. We can operationalise solutions at speed and with greater reliability.