Autor: Stiven Garcia

A few years ago, the undeniable use of tools like Power BI, Qlik, and Tableau, among others, was unforeseen, as there was no organizational culture that regarded data as a highly useful source of knowledge. Even just a few years ago, when data began to gain strength in informed decision-making, highly developed data analytics tools, methodologies, and data-focused strategies were not readily available. Today, we have a different landscape; we have an enormous array of data-focused tools that allow us to work with and respond to every business question we may pose.

At Wadua, we understand that the world doesn't stand still, especially the world of technology. Therefore, we want to introduce you to some of the most interesting and exciting trends currently developing.

DataOps:

This practice seeks collaborative data management between data integration and automation across all areas of the organization, achieving optimization in the design, development, and maintenance of data-driven applications. DataOps originates from DevOps, which combines development and system management; however, DataOps focuses entirely on data processing to achieve automation, speed, and accuracy in its processing. All of this results in organizations eliminating data duplication by improving processing, creating a data strategy by promoting collaboration and data availability across departments, and achieving greater operational efficiency by implementing automation technologies.

Augmented Analytics:

This is one of the trends you'll hear about most frequently as it significantly facilitates extracting information from datasets, leveraging machine learning and artificial intelligence in conjunction with natural language processing integration, enabling direct user interaction with the data. Examples of this include Microsoft Copilot, Gemini (Google), and ChatGPT.

Data Mesh Architecture:

This is a decentralized approach to managing and accessing a company's or organization's data. It involves dividing stored data into business fragments where a team is responsible for creating data analysis products. The main characteristics of this setup include independent data management by each area of the organization, a self-service platform where members of each area can access and use data, centralized control where non-domain-dependent information is collected and controlled (e.g., permission management).

 

Data Lakehouse:

This is a storage structure that combines all the strengths of a data lake and a data warehouse. Its development is necessary given the exponential growth of data generated and used for data analysis, thus facilitating machine learning, business intelligence, and predictive statistics. All this comes with the benefit of the low storage differentials of data lakes, offering the organized structure of a data warehouse.

Edge Computing:

This trend develops an effective strategy for collecting data from different devices thanks to low-latency connectivity, enabling a high level of performance.

As the name suggests, this concept is used at the outer edges of the data process, i.e., on devices close to users and not in a centralized data center. The aim is for applications that respond quickly to specific events or collect data, such as in IoT, to do so with superior speed.

Data Stitching through Metadata:

Also known as data fabric, this is a system that separates information from metadata, an architecture that helps improve the processing of large volumes of data from different sources by grouping them under the same management system. This allows organizations to manage all their data easily and seamlessly in a single space where they can access and use it without any inconvenience due to tool incompatibility.

These are some trends currently being implemented in the world of data analytics. With their constant use, data analysis has transitioned from being an additional tool that could add value to organizational processes to being a fundamental tool for understanding these processes. It enables the acquisition and generation of relevant knowledge that facilitates and guides decision-making in organizations. At Wadua, we are interested in all new concepts, tools, and developments for data analytics. We understand them and incorporate them to offer you the best analytical solution, from people to people.

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