Our mission

We design Data Analytics Solutions

We create impact by building solutions that collect, analyse and present data to decision-makers. We select projects that are meaningful for our client or the society, and have expertise in sectors like energy, environment, healthcare, public health and transportation.

Our philosophy

We are passionate about designing and building analytical solutions. We like to take a top-down approach, focusing first on impact, then on engineering and finally on building the solutions.

We are very much hands-on and like to spend time with our clients to understand their needs. On the technical side, we code a lot ourselves, for instance for prototypes and smaller-scale projects.

We like to work in tight integration with our clients, typically as part of their analytics or engineering team.

Our method

We follow an approach similar to architecture firms: we are heavily involved in the initial phases of the project, to understand the needs and design the solution. Since we are not tied to software vendors or development shops, we can provide independent advice to our clients and follow execution on their behalf. Our areas of activity are the following.

Research

Together with our client, we identify the value we want to create. Based on that, we design a measurable impact metric and build a business case1.

In parallel, we research the organization’s decision-making process, the user’s needs, and identify where exactly the solution would fit in the company. In the process, we also flag missing skills and training needs.

Additionally, we determine the data situation since absence of data or quality issues can change how we design the solution.

Finally, we assess external constraints such as sector regulations, data regulations2 and how they apply to key topics such as data security and processing.

Design

Based on the results from the research phase, we create a product roadmap that describes how the features and components of the solution will provide benefits for the users. Its key elements are:

  1. The data collection process3.
  2. The data processing pipeline and data storage4.
  3. The advanced analytics (or data science) algorithms5.
  4. Visualisation features6.

Based on this roadmap, we also produce a set of blueprints and make technological choices for:

  1. The application, which includes the 4 elements mentioned above.
  2. The software platform that will run the solution, be it small data (traditional databases) or big data platforms (parallelized)7.
  3. The infrastructure that will run the software platform, whether on-premise or Cloud, we optimise for simplicity and standard components8.
  4. The automation process, allowing to deploy all the components very rapidly on a variety of systems9.

Build

The final step is to actually build the solution. We like to develop initial prototypes ourselves, which allows for early client feedback.

The next step is to iteratively build the production-grade solution and progressively integrate feedback.

From here we work with the engineering team as “product managers”, using two different models: (a) our client’s in-house team develops the solution, and we are tightly integrated as part of the team, or (b) a third-party engineering firm takes over the heavy lifting or the specialised tasks, and we act as an architect would towards a general contractor.

In parallel, we work on embedding the solution in the company. This means gathering continuous feedback, training the teams and measuring the impact achieved. Sometimes it may require course corrections to ensure the value initially envisioned is captured.

About data@work

Amaury Anciaux founded data@work in 2015, after 6 years at McKinsey&Company, first as a strategy consultant, then as the general manager of one of the pricing analytics solutions. From there he kept the passion to help clients and continued to develop the technology expertise required to build solutions.

More information is available on Linkedin.

Projects and clients