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Data Trends for 2021

Data Intelligence - Published on 29 October 2021
By Pierre Baudin

Data Manager

The data science revolution that is dramatically influencing the way companies approach their business and their environment is well underway and 2020 has only accelerated it.

The changes brought about by the combination of big data, statistical methods and data science technologies, make the impossible possible. The progress in the resolution of important problems such as self-driving cars, algorithmic trading programs or the calculation of protein folding are among the most visible signs.

However, the real revolution in data science is the generalization of this field of possibilities, where everyone in an organization can understand data, communicate information from that data, and make more informed decisions with it.

2020 has been a unique year and the Covid-19 crisis has accelerated digital transformation, forcing companies to digitize their processes, modernize their business models and enable access to data to make way for an era driven by data.

In this article, I’d like to invite you to discover the major data trends that are coming in 2021.


Consolidation of data engineering tools

The democratization of data requires the modernization of data infrastructures. Strong investments have been made in this area and a long list of platforms has emerged to solve these data engineering problems.

These tools help companies collect and integrate raw data, ingest and transform it, and store it centrally to produce descriptive and predictive analytics.

Currently, data engineering tools remain fragmented and competing on different pieces of data infrastructure. Over the next year and beyond, consolidation and standardization of various tools and platforms is expected alongside the adoption of cloud, metadata management tools and centralized data governance platforms.

Industrialization of machine learning algorithms

The past year has seen the importance of monitoring models used in production. The change in consumer behavior due to the pandemic has fundamentally changed the nature of the data that powers these models and the value they can produce.

This year and the years ahead will see organizations focus on deploying machine learning at scale. The industrialization of machine learning models will include their seamless integration into the data infrastructure, the development of data engineering capacity specifically for the application of machine learning (MLOps) and governance tools for monitoring models in production.

To also read : What is machine learning?

These upcoming changes are also aimed at creating a closer human-machine feedback loop resulting in increased interaction of services customers' data with machine learning models to produce insights and to inform decisions.

Illustration du concept de machin learning

Democratization of data science with the Jupyter ecosystem

Over the past decade, Jupyter notebooks (https://jupyter.org/) have become a staple in the data scientist's toolbox. The notebook interface that supports more than 40 different languages helps streamline the data science workflow, allowing data professionals to quickly prototype, explore information, and share data stories across the board within their organizations.

The next generation of interactive development environments for data science and powered by Jupyter will further drive the democratization of data like Google Colab which incorporates collaboration features or naas.ai which simplifies the creation of data pipelines with notebooks or Mode which simplifies the transition from SQL to R or Python in a notebook environment and makes analysis easily accessible.

Finally, it will reduce the barriers to entry for working with data, which will allow all members of an organization to access information more easily than ever, create data stories and facilitate collaboration between technical and non-technical experts.


Emergence of augmented analytics

The research of data mastery by companies promises new Business Intelligence tools with increased analysis capacities. Augmented analytics can be referred to as a Business Intelligence approach that uses natural language processing (NLP), graphical analysis, and machine learning to automatically extract insights and stories from data.

💡These features are starting to emerge and the future of Business Intelligence means that actionable and effective insights into data will become ubiquitous across all layers of the business.

Dissemination of data skills in companies and academic courses

The rise of the data culture in the company also brings its share of organizational challenges.

The data literacy skills gap is at the heart of the growing gap between those who thrive and will continue to thrive in the data-driven economy and those who will be sidelined.

As organizations realize the need to become data-driven, data literacy skills will become a requirement in academic curriculum and will also be at the heart of continuous employee education programs to maintain a high level of competitiveness.

Etudiants et thématique de la data

Towards active governance of personal data

As data and its applications become critical to organizations, the importance of data governance to protect the integrity of data as an asset is also becoming essential in business strategy.

The modernization of data infrastructure will allow the active management of data, especially personal data, and will make their management more standardized, thus making them more available, less complex to understand and will protect consumers' rights over their data with greater transparency.

Companies will be able to provide discoverable, reliable, compliant and actionable data for a variety of internal or external end users.

Special mention: Python, a language of choice for data science

Python is one of the most used programming languages in the field of data science and most data scientists use it on a daily basis for the daily tasks of processing and exploring data but also for very advanced applications such as machine learning and deep learning.

🔎 For data scientists and data engineers who need to embed statistical code into production data pipelines or integrate data with web applications, Python is often the ideal choice. It is also ideal for implementing algorithms. In the field of web development, testing, deep learning, Python finds a wider acceptance than most other languages. 

With its technical characteristics and a very large community of developers, Python's place in the field of data will continue to strengthen.

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