What we do

Data science meets design thinking

We identify opportunities for AI in your business and deliver solutions that transform day-to-day operations. This is about the practical application of data platforms, data science and machine learning to create real tools you’ll use every day to improve efficiency, effectiveness and, ultimately, create more value.

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How we Solve

Identifying the challenge

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:

Data platform

“I have all this data and I don’t know what to do with it.”

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.

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Data science

“I know data science can boost our business, but where do I start?”

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.

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Machine Learning operations

“We’ve got great machine learning pilots, how can we get them live?”

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.

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Self-service Business Intelligence

“Manual data processing is so time consuming, how can we get insight faster?"

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.

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Delivery

Three stages for successful delivery

1. Prime

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 brainstorm 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.

2. Prove

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. We also work closely with end users to understand how they will use the output and to tackle any concerns around trust. The key to this phase is speed and learning fast. Our bespoke engineering and data science components and tools allow us to deliver a proof of value in weeks not months.

3. Productionise

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. As we’ve done this many times before and learnt a lot along the way our toolkit is constantly evolving to ensure we’re best placed to take advantage of the latest cloud capabilities. As a result we can operationalise solutions faster and with greater reliability.