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Data
Achieve more with your data thanks to the Axxes data consultants! The exponential growth of data in recent years also explains why it has become so important. The real challenge, in addition to data collection, is interpretation and developing actions based on these insights. Business decisions that can generate a lot of added value for your organisation rely on the right data insights.
At present, we are at the beginning of an era in which data will be crucial. Many companies have already taken steps in recent years, including the implementation of AI and the required algorithms. Does this mean they can already reap the fruits of their data-centric policy? Not quite yet.
The way in which companies process data plays an important role in the optimisation of business processes and activities. The objectives vary and can include anything from making well-considered strategic decisions to discovering new opportunities or working more efficiently. Our data consultants approach data based on their technical expertise, to respond to business needs. They develop analyses and models that can improve business processes and activities tremendously. Together, we create a roadmap in line with the goals that have been set.
1/ Data engineering
These technical profiles are responsible for the collection, structuring and combination of all the data generated within an organisation, to gain access to meaningful information in a comprehensible way.
- Collect
- Structure
- Migrate
- Gain insights
- Achieve objectives
2/ Data science and analysis
Our data scientists and analysts use technologies such as R and Python pull the right information from large amounts of data. They build statistic or machine learning models to describe, predict and explain phenomena and draw conclusions that offer added value for your company.
Best practice
1. Not from scratch
As is the case in so many other areas of expertise, you don’t need to start from scratch with data. We use various architecture models that will put you on the path towards your intended goals. We also have frameworks such as scikit-learn, spark, Databricks, AWS SageMaker, Azure ML Studio, etc, for data analysis and machine learning.
2. Scaling
Start with a data project with a comprehensible scope, which responds to a specific business need. It will give you a better idea of how the business works and earn trust which, in turn, facilitates scaling.
3. Tools
Working with the right technical tools will help you successfully launch your project and gain the necessary insights. Depending on the objectives and the available data, this can include low-level technology such as Python or a high-level service such as Azure ML Studio, using an extended data warehouse or a data lake, enabling you to choose whether you want to work locally or in the cloud.
4. Security
Make sure that you process your data securely.
