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Data & Artificial Intelligence

Leading organisations understand the value of data used to deliver exceptional customer experiences, enable new business, and optimise operations.

We help engineer your data future, through building the next generation of data-driven products, platforms and culture that allow you to unlock new sources of value. We combine applied machine learning solutions with human-centred thinking and our heritage of software engineering excellence to embed lasting differentiated capabilities in your organisation.

Summary: Guiding the evolution of data mesh with fitness functions


We recently hosted an insightful discussion on data mesh with Zhamak Dehghani and Dave Colls. The audience asked some great questions during the session around a few key topics. One of our presenters, Dave Colls, has captured these in this summary.


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Active learning loops


At ThoughtWorks we're starting to see patterns of how people integrate various mixes of labelled and unlabelled data into systems. We're finding it needs careful planning and explicit loops to manage the different data flows. There are many questions though; At what point do you decide to introduce models around unlabelled data? How do you validate that it's worth the cost? These are all interesting questions that change from system to system!


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Metric-driven Data Architectures


It is common for organisations to use multiple BI tools, such as Tableau and Power BI, in order to meet the specific reporting needs of different teams. However, when crucial business metrics (such as monthly recurring revenue and customer churn rate) are defined and maintained in multiple locations, serious risks surrounding their discoverability, agreement, and correctness can arise.


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Synthetic Data


Of course real data is necessary for testing and building data-driven products and features, but it is far from sufficient. At ThoughtWorks, we’ve had a lot of success in early stage discovery and planning by simulating products with synthetic data. This allows us to test in a lightweight way many aspects of a product, just not its actual predictive or optimisation performance. 


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ML CI/CD: Should we pay attention?



Not long ago, the biggest focus in AI/Machine Learning (ML) was research & development. Since ML has become mainstream in many digital applications, there has been a shift in focus to productionisation of ML models and products. 


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Want help to unlock your data potential?

Our practice leads

Sue Visic

Director, Data & AI Practice

Australia

David Colls

Director, Data & AI Practice

Australia

Karen Davis

Lead Data Consultant

Simon Aubury

Principal Data Engineer