Data visualization, data preparation and machine learning are among the topics Nadia Sakhi is responsible for in her role as Senior Consultant at M2. Nadia has been working at M2 since 2017. We talked to her in detail about the appeal and challenges of her role in the M2 team.
We present an excerpt of our conversation here. You can read the entire conversation and further staff interviews here.
M2 editorial team: Dear Nadia, what experience have you gained in project work with clients?
Nadia: With each project, I have of course learned more about the content. But that is not the only thing. Through project work you also learn to break down technical requirements and "translate" them into implementable specifications. The experience also helps to maintain the balance between concentration on the goal and attention to detail.
M2 editorial team: At which interfaces are you connected with the customers?
Nadia: That varies from project to project. Sometimes I am an integral part of a project team at the customer's site. In other projects, in which the implementation is mostly done by the M2 team, there are workshops - for example at the beginning for the requirements gathering or at the end for the project handover - and we regularly coordinate closely with the customer about the course of the project and next steps. I also give training sessions where I spend only a few days with the customers and I enable them to perform self-service analytics.
"As digitization progresses, the amount of data that can be evaluated will have increased significantly. (...) The focus will increasingly be on making forecasts for the future rather than statements about the past."
M2 editorial team: How would you describe the working atmosphere at M2?
Nadia: Very cooperative and collegial. Everyone is an expert in a special field. The close cooperation always results in exciting synergies that lead to surprising results.
M2 editorial team: What do you think your professional field will look like in five years?
Nadia: The progress of digitalization will have significantly increased the amount of data that can be evaluated. As a result, machine learning will become even more important and the focus will increasingly be on making forecasts for the future rather than making statements about the past.
You can read the entire interview and further employee interviews here.
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