Data & Analytics trends 2024

With 2024 already into month 2; it's time to take a look at what the future holds for data analytics. Big Data continues to be a powerful and intimidating tool for businesses. According to a recent study, more than 95% of them encounter difficulties in managing their data.??

In this article, we present to you the new data analysis techniques to know (and master) for 2024!??

5 essential data analysis trends for 2024

1. Integrating AI and machine learning into data analysis

Artificial intelligence (AI) and machine learning (ML) are expected to be increasingly integrated into Business Intelligence systems , thus optimizing data analysis , predictive analysis and decision-making.??

The main interest of these two technologies is to automate the work of data analysts . Particularly interesting for very large volumes of data, they will prove more effective in predicting trends, identifying patterns and anticipating market fluctuations.??

Amazon, for example, has started using AI-based algorithms to analyze its delivery data and minimize the time between ordering and receiving a product. In the manufacturing sector, predictive maintenance powered by AI also makes it possible to predict and anticipate breakdowns several days in advance.??

2. Stream processing will enable real-time data analysis

The rise of Big Data has largely been accompanied by that of connected objects, such as sensors, intelligent home automation, etc. But until recently, businesses were often unable to process these enormous volumes of data in real time. This resulted in analysis errors, increased latency and unusable data because it was obsolete.??

One survey even found that companies only use 57% of the data they collect. This is expected to change in 2024 thanks to real-time data stream processing solutions. This market should even be worth more than 52 billion dollars in 2027 (for 20 billion in 2023).??

One of the main sectors to benefit from these new solutions will be banking. Banking establishments will indeed be able to integrate real-time data (such as ATM withdrawals) into their various services, particularly credit. Apache Flink is a good example of a Big Data streaming solution. Available as open source, it can process data streams in just a few milliseconds.?

3. Data as a Service for Autonomous Data Management?

The data-as-a-service (or DaaS) market is expected to grow by almost 300% over the next 5 years . It includes Cloud-based tools that facilitate the collection, management and analysis of data.? They allow companies to empower and internalize their use of Big Data without having to create their own solutions.?

The main advantage of Daas is obviously to significantly reduce the investment of organizations in data analysis. They will also be able to make decisions in a more agile way thanks to tools that can be read even by people without expertise in this area.??

AWS, Microsoft Azure and Google BigQuery already offer DaaS options. But young startups have also emerged in this sector , in particular to meet the specific needs of certain verticals of activity. Tetrascience , for example, is a Data cloud provider that is aimed specifically at scientific laboratories to allow them to harmonize all their data.??

DaaS builds on another trend in data analysis for the years to come: the democratization of Big Data. In the future, the power of this tool, once largely reserved for experts, can be placed in the hands of any employee or entrepreneur.??

4. Optimize data storage using data lakes and lakehouses

Companies wishing to deepen their data analysis will also have to manage the task of storing ever-increasing volumes of data. One of the main solutions to emerge on this front is that of data lakes or data lake houses.??

Data lakes allow businesses to store raw, semi-structured or structured data. They respond somewhat to the principle of “ store your data now and analyze it later ”. In fact, these infrastructures make it possible to overcome both size and delay constraints in the storage and retrieval of data.??

This is particularly the case for data lake houses, which combine the scalability and flexibility of data lakes and the management capabilities of data warehouses. The technology nevertheless remains in its infancy and it is predicted that this market will experience growth of more than 30% in the next 2 years.??

5. New governance in data analysis

Given the ethical and regulatory issues surrounding Big Data, experts predict that companies should pay close attention to data governance in the coming months. According to Gartner, we should go from 10 to 74% of the world's population protecting their personal data (within the framework of privacy laws). New legislative frameworks are in fact being adopted in China (the PIPL), in Canada (the PIPEDA) and even in the United States (even if the country is the furthest behind in this area).???

In order to reassure their users and maintain their market share, companies will therefore have to commit to offering more protection . They will impose on themselves (if only to avoid sanctions) stricter controls and increased transparency in the way they collect and analyse data.

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