In this article, we have explained the five top trends that data and analytics professionals need to watch. Read this detailed article to know.
For years, data and analytics have been a dominant force in increasing organisations’ speed, transparency, and decision-making capability, and they will continue to be so in 2023 and beyond.
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App Tracking and Third-Party Cookies Continue to Crumble
One simple truth of 2022 is that when an app or website offers them the option to track them across the web, the vast majority of users simply say no. Consumers will instinctively choose essential cookies if given the option, or will decline app tracking when prompted by iOS, and keep in mind that this is just consumers. Safari and Firefox (and, soon, Google Chrome) are also proving to be hostile environments for third-party cookies. Apple’s iOS continues to raise the bar on privacy, and with GDPR and CCPA fully implemented, the days of widespread cookies and tracking are coming to an end.
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Deep Learning Will Continue to Grow, With Further Advancements
We now have deep learning-powered tools and systems for deciphering these clicks and the stories they tell about users and visitors. For the first time, transformer models and encoder systems can analyse and semantically interpret product catalogues, content, and ads beyond what they are becoming available.
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New practices and data privacy regulations will help platforms thrive
Changing our business practices to address ethical and privacy concerns will be difficult in the short term. It is causing a sea change in how publishers, businesses, and platforms view and use internal data. This will result in a significant improvement in the ability of websites and apps to personalise the user experience and use their insights to improve direct loyalty programmes, marketing, and advertising.
This may only be good in the long run. Teams that fully commit to this strategy will see increased engagement, organic growth, and retention.
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Mlops Accelerates AI Adoption
Even though computing power and massive datasets have made it easier to create AI models, the harsh reality is that only 53% of AI Proof of Concepts make it to production. Even fewer can provide the intended, measurable business value. MLOps, also known as AI Engineering, is emerging as a dominant trend for defining the best practices and processes for bringing machine learning models into production and operationalizing them in real-world contexts.
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Data Governance Ensures Data Security and Availability
Due to data management and security challenges, a large amount of data remains unused. Furthermore, due to poor data quality and availability, 30% of total enterprise time is spent on non-value-added tasks. Mature data governance accelerates the data management process and aids in measuring and understanding how well businesses manage their data. A strong data governance policy will enable businesses to maximise data value, manage the risk of data misuse, and ensure that the right people have access to the right data.
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