8-data-trends-to-follow-in-2022-–-money

8 Data Trends to follow in 2022 – Money

8 trend data to follow in 2022

Edouard Beaucourt, Director France, Snowflake


Data has been identified as the new “black gold” for businesses for several years now and clearly massive growth in data volumes has been the metatrend of recent years. With the democratization of machine learning, organizations have more and more the possibility of quickly adopting automation to better exploit in real time the gigantic amount of data which they are today able to collect. The emergence of data in the cloud opens the door to many possibilities that can allow businesses and organizations of all sizes to leverage and value their data quickly and easily. Here are some trends that will mark the year 2021. To take advantage of the strong growth, variety and availability of data, businesses and organizations of all sizes will increasingly turn to automation. The continued adoption of greater automation of the world of data operations is expected to strengthen over the coming months, which of course includes devops and autoML (Automated Machine Learning) functionality. The processing of data slow and laborious will fade away to allow companies to quickly draw conclusions. The trend today is to analyze data in real time. As this trend grows, however, businesses and organizations are going to need more automation in order to keep pace and manage seamless data pipelines. If automation is not used, we will see data piling up behind the pipeline. These bottlenecks will prevent the data from being usable. Automation has now passed the stage of craze. Next year we’ll see more real-world use cases and real-world results. The technology exists and is ready to support workloads. The cloud will accelerate and widen the differences between companies. Growing companies that are already building and developing in the cloud are now more easily equipped with artificial intelligence and machine learning models, without having to develop in this field. Data Science becomes a real asset strategic and democratized. No matter the industry, companies know that their competitors have dedicated teams who easily adopt AI and ML in their organizations. The tools have recently evolved dramatically, making it easier for organizations to use automation and data science. This should result in an increase in the use of Data Science in all sectors, as it will no longer be necessary to have so many specialists in a given team. Data sharing should be on the rise this year and accelerate. The emergence of this universe of data that surrounds us only makes it easier to share it. Last year we talked about the phenomenon of BYOD (Bring your own device) which was impacting companies. Today, we see the emergence of the collaborative notion which gives organizations the possibility of “contributing their own data”. This possibility offers companies the ability to share their data, while keeping control of it in complete security while also sharing and exchanging machine learning models. A real breakthrough! Beyond data sharing, 2021 should see the emergence of monetization of these and Machine Learning models. It is also likely that we will see more collaboration between organizations to develop and innovate with new ML models. The health sector, for example, could take advantage of these advances to improve patient care and services. Finally, it seems to me that next year will be a pivotal year for the structuring of an ethical and secure environment for data . Algorithms are made up of code that is biased. While we are able to automate, clearly not everything can be. Data governance is therefore essential to allow us to objectively examine the production, the process and to ensure the balance of these. We are at a crossroads on the subject and the 06 the next few months should see real advances both in terms of expert ethical practices and around guarantees of protection and confidentiality. To read also: The many paradoxes of teleworking in France

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