What is a data science platform and how does it benefit your organization? Ryax answers your questions and gives you the keys to evaluate the different data science platforms available on the market.
More than a tool, a data science platform is an ally allowing the company to extract the potential of its data. As software solutions multiply, making a choice and defining the important criteria to select or develop such a platform can be difficult...
What is a data science platform?
A data science platform is a software environment that allows the integral processing of data. The platform generally provides a data access point shared by all data science functions and users.
A data science platform promotes harmonized, fast, and efficient data processing. It also standardizes the way the company processes data.
The data science platform therefore aims to enable data processing models to be put into production in an efficient manner while guaranteeing scalability and security.
What is the purpose of a data science platform?
Data exploitation has become a strategic priority for many companies. It is therefore necessary to be equipped with adequate tools to enable this exploitation. The data science platform has several advantages:
- Enable effective collaboration between data engineers, data scientists, data analysts, data architects and other functions related to data science;
- To propose a coherent and harmonized visualization tool understood by all the actors concerned within the company;
- Limit the risks of errors and disparities at the level of data sources and the data itself.
The data science platform is often in perfect adequacy with DevOps and DataOps techniques that are currently highly acclaimed.
More generally, the exploitation of data proves to be useful at different levels for the company:
- To allow a more targeted and adapted marketing;
- Detecting fraud and intrusion attempts;
- Optimize logistics flows and supply chain, ensure effective management of stocks and supplies;
- Promote predictive maintenance and reduce operating costs.
Why use a data science platform?
When it comes to putting data processing models into production, statistics speak for themselves. Figures for projects that never go into production vary by source, but most often flirt with the 80% mark.
The associated costs can quickly get out of control. In addition, technological developments follow one another at a breakneck pace. Companies need to be agile and adapt quickly. To do this, it is essential to create a corporate culture around data and ensure collaboration and communication. In practice, we also see that many organizations spend an inconsiderate amount of time on simple tasks that could be easily automated.
While the desire to exploit data seems to be omnipresent, the result in practice does not yet deliver all its promises. The discipline is new, the lack of skills is glaring, and companies are struggling to adapt in real time.
This is a major challenge for many companies that are still based on rigid structures. Nevertheless, there are also many opportunities for small and medium-sized companies. Indeed, if they do not have the financial means and resources of CAC 40 companies, they have the advantage of size. This facilitates rapid evolution. The existence of data science platforms offered by third parties also allows these companies to exploit and make the most of their data.
What are the important elements of a data science platform?
A data science platform must adapt to the organization and not the other way around. Therefore, there are several evaluation criteria to find the right platform for you. Some of them are limited, others have been specifically designed with the Big Data environment in mind.
Here is an overview of the main elements to consider before making your decision.
- Support throughout the entire data processing chain from the design phase (Proof of Concept, PoC);
- Horizontal scalability and data security, existence of backup functions;
- Data storage solutions (data lakes, data warehouses, datamarts, etc.);
- Compatibility with hybrid architectures (cloud, edge, serverless, etc.);
- Provision of analytical tools and integration of machine learning and deep learning algorithms;
- Compatibility with other software and numerous computer languages and data processing protocols;
- Open source character allowing the platform to be adapted to the needs of the company.
- Ease of use of the platform for all those who need to use or view the data;
- Integration of DevOps and DataOps principles and techniques.
In general, the use of a data science platform should not be considered as restrictive or burdensome. If this is the case, the objective is not achieved. The data science platform adapts to the company and not the other way around.
Is it necessary to create one's own data science platform or to use third-party software?
Creating a data science platform internally requires the mobilization of significant resources. In addition to the need to create a robust system, it will then be necessary to keep it up to date, guarantee its security and scalability. For this reason, the majority of companies, including very large enterprises, will prefer to focus on their core business and partner with an external supplier.
Currently, many data processing software applications exist, but the market remains extremely fragmented. Ryax has created an intuitive, efficient, and effective data science platform. Our open source platform allows each company to adapt our software according to its needs and priorities. In addition, we ensure stability, security, and scalability in order to offer you a reliable partner for the long term.
Our goal is to provide you with a complete product that facilitates and optimizes the processing of your data flows. Thanks to our SaaS, your data becomes a competitive advantage that can be used at all levels of the company and you can benefit from an edge over your competitors. As our start-up is based in France, we ensure a fast and efficient follow-up. We have at heart to satisfy our customers and constantly improve our solution.
The Ryax Team.