From raw data to a strategic decision: the impact of data and AI platform
- leaperini
- Mar 18
- 2 min read
According to Mordor Intelligence, the data science platforms market is estimated to be worth €10.15 billion in 2024. Today, those data and AI solutions are positioning themselves as an essential tool for ensuring management that is both efficient and reliable. Find out how and why they’re becoming key elements of your strategy.
What is a Data and AI platform
1.1. Definition
A data platform is a technological solution designed to manage the entire data lifecycle. It enables large volumes of data to be centralized, processed and exploited securely.
Today, the integration of AI into Data platforms has become essential. With AI already being used on a large scale in several business applications, it is generating a real craze among users keen to make the most of their data’s full potential.
Functionality
With the explosion of data generated by different IT tools, managing it remains a complex challenge. Thanks to the emergence of Data and AI platforms, the integration of technological building blocks has created a unified, automated ecosystem.
2.1. ETL
An ETL (extract,transform,load) is a technological building block that facilitates the collection, transformation and synchronization of data sources.
By orchestrating workflow, it is possible to automate data processing in real time, while ensuring the fluidity of the process.
2.2. DataViz & BI
DataViz translates data into graphical elements, such as customizable graphics. This visual interpretation of data makes it easier to access and exploit different data streams.
As for business intelligence (BI), its aim is to facilitate business decision-making, through various processes such as data integration, trend analysis and dashboard creation.
2.3. AI Studio
2.3.1. ML Engine
The ML Engine is a system or platform that provides the resources needed to prototype, train and validate machine learning models.
2.3.2. MLOps
MLOps is a set of practices aimed at deploying and maintaining machine learning models reliably and efficiently. These practices are based on DevOps development and machine learning.
Challenges
Data and AI platforms must cope with crucial challenges for business needs:
Ensuring data centralization, security and quality
Guarantee efficient management and total governance of data
Facilitating scalable solutions suited to business needs
Rapid access to information and data, simplifying analysis and exploitation
“Faced with these challenges, companies are looking for more accessible and efficient solutions. This is exactly thaink²’s mission”
What if the promise of data and AI platforms became a more accessible reality? thaink² simplifies data management, Business intelligence and AI to help companies in optimizing the value of their data.
Discover in video how we turn data into a performance lever.
