First Data Project? Go Tandem! (AVISIA at Play)

Dataiku Company, Scaling AI Anaïs Kassapian

If you were going skydiving for the first time, you’d definitely want to go tandem and be attached to an experienced instructor to make sure things go right. For data projects, while the risk isn’t life-and-death, it’s still high for a lot of businesses who are facing increased competition, rising costs and dipping revenues — so why not partner up?

tandem skydiving

Often one of the fastest ways for companies to get experts working hand-in-hand with the data team on a first project is to work with one of Dataiku’s certified partners. Partners have been there and done it all before, so they can jump-start projects, provide tips and tricks to the data team, and transfer the knowledge of years of experience working with users everywhere on a wide range of data projects.

We talked to AVISIA (a Dataiku partner since 2016 with the highest number of certified consultants that has lots of experience working on data projects like sales forecasting for large retail players, recommendation engines for the banking and insurance world, and real-time scoring applications for pure digital players) about the top two ways companies can jump-start their data projects.

Embrace Reusability

headshot of Fabrice Simon AVISIA data scientist and consultant

Fabrice Simon is a data scientist and consultant at AVISIA, and for him, embracing knowledge management in general — particularly the ability to reuse parts of a data project - is critical to success. Data science tools or platforms (like Dataiku) can help make this happen.


In my experience, it is easier to maintain projects with Dataiku because it is easy for a new person to look at the steps of an old project and adapt them to a new need.”

Indeed, as a consultant, it’s key for Fabrice to be able to empower his clients after he leaves an assignment. But even for companies outside of consulting, inevitably there is going to be some employee churn, so you must be confident that the know-how around an analytics project will not simply walk out the door in case a team member leaves.

As an aside, as a data scientist, Fabrice is also a fan of the visual machine learning features in Dataiku. Though Fabrice is a coder (he’s a big fan of integrated Python and R notebooks), he recommends that even technical folks take advantage of the visual capabilities of Dataiku for standard options.

The manual parameterization of options, such as sampling, features, and models, allows me not to waste time coding and saves me time interpreting the results.”

Focus on Operationalization

headshot of Caroline Jarry AVISIA data scientist

Caroline Jarry, an AVISIA data scientist, is especially focused on value, which means taking projects out of a design environment and putting them into the live business environment of the organization. She has a recommendation for organizations concerned with generating value from their data projects, which is to industrialize workflows using scenarios, launched either manually or automatically.

 Dataiku can automate sections or even the entirety of workflows, and this automation can simply be scheduled (e.g., daily/weekly) or based on scenarios (e.g., when a new batch of data arrives, or when certain metrics are triggered). Caroline emphasizes that this can done for whatever part of the workflow you need.

Rebuild your project from data preparation, or re-score your data with your model, or even retrain the model itself using new and updated data.”

Whether you are using Dataiku or not, operationalization is the key piece of data projects - without it, teams might work on projects that never show any business value at all.

A large part of our mission here at Dataiku is bringing together the data community both within our product and out in the world. Whether it’s technology partners or consultants that help bring a data-driven approach to the far-reaching corners of the world, partners are an essential part of our community — talk to AVISIA or find a Dataiku Partner.

You May Also Like

Adoption of Generative AI in Retail & CPG: The Time Is Now

Read More

How Will AI Shape the Future of Life Sciences Organizations?

Read More

Maximize Your Data Potential Beyond Spreadsheets

Read More

Deloitte Electrified: Designing a Twin of the Electric Grid

Read More