Data landscape: optimized, decentralized, agile and secure

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Learn some key aspects of the data landscape and what to consider to lead the data economy and be truly data-forward.
Understanding the current data analytics and management landscape is a fundamental task for all organizations today, as it is the main input for most operations. Now, what are the main trends that are being seen in data-forward businesses ?



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The 4 fundamental aspects of data management
Broadly speaking, the four main guidelines observed over the course of the year are linked to data management, display and the decentralized structuring of information.

Metadata management: Innovations related to information botim database and scalability seek improvements in management capacity in different aspects of metadata. First, open table formats are emerging as a standard for structured storage in data lakes. Second, the notion of Git for data is taking hold, encouraging the implementation of best practices in the development and production of information. Finally, the increase in demand for DataOps teams aims to achieve joint and controlled management of information that has different revisions: recalculation for algorithms and Machine Learning models, replacements of operating systems or elimination of a subset of data for regulatory reasons. All with the focus on achieving automated data management .


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Data Catalogs: The need for a data catalog is necessary for companies of all sizes and is emerging as a standard to be adopted, mainly through open source projects.

Decentralization of information: The growth in the number of diverse data platforms within the same organization is facilitated, among other reasons, by the expansion of cloud architecture. In this sense, the “data mesh” appears to be an increasingly relevant tool for making information available at its source for any user.

Agile methodologies: This year, the adoption of DataOps and MLOps for Data Engineering and Machine Learning teams will deepen amid a growing demand for integration and automation tools. The DataOps approach creates agile and optimized data analysis pipelines in an iterative and incremental manner. MLOps operations, on the other hand, refer to best practices for the execution of Artificial Intelligence, specifically Machine Learning models.