More and more companies are concerned not so much with the enormous amount of data they generate (which is also), but with how they can modernize it, or in other words, manage it intelligently, that is: avoiding duplication and inconsistencies, extracting he real value that they contain and using them to make better business decisions. It is useless to have large data repositories, if they are scattered, not updated or not easily understandable.
In order precisely to be more efficient in this field, to have a comprehensive vision and centralized planning and control over the data, in recent years the data center of organizations (almost always integrating them in a hybrid approach with the cloud) has come a whole myriad of solutions that seek to facilitate this task.
In this sense, we are talking about ETL platforms, data lakes with intelligent capabilities, comprehensive platforms for data governance, automation of robotic processes or BI solutions, among many other options. Let’s see below what we can expect from each of these.
Integration Platforms and ETL (Extraction, Transformation and Load)
Abbreviations ETL (“Extraction, Transformation and Load”) represent a crucial process in data management and its integration into systems. ETL is used to move data from multiple sources, transform it into the desired format, and load it to a destination, either a date lake, a database or an application for their analysis.
This process is essential to maintain the quality and integrity of the data. as they flow through the organization. It allows not only its consolidation into a consistent and usable format, but also facilitates reporting, analysis and decision making.
Some of the best known and used platforms in this field are Apache NiFi, Talend, Microsoft Azure Data Factory either Google Cloud Dataflow.
Modern Data Lakes
The new Data Lakes or modern data warehouses are presented as platforms designed to store and analyze large amounts of data in an efficient and scalable manner, allowing companies to perform fast and agile analysis.
Some of its advantages are scalability that allows them to handle large volumes of data, improved performance thanks to capabilities such as parallel processing and column architecture, or simplified management as they are usually managed services. They can be integrated with a wide variety of data analysis tools (including real-time or AI), programming libraries, and query languages such as SQL.
Some of the best-known offers in this area are Amazon Redshift, Google BigQuery either Snowflake.
Data Governance Platforms
Data governance platforms are comprehensive sets of tools, processes, and policies designed to manage, protect, and optimize the use of data in an organization. Data governance focuses on establishing a framework that ensures data quality, integrity, and security, as well as ensuring regulatory compliance and informed decision-making.
Some of its main features include the catalogingso that companies can understand the origin and context of the data; quality tools that help in the detection of errors and inconsistencies; policies of access and security management; Management workflows for approval, review and publication of data; tools that ensure compliance and data auditing; possibilities of automation of governance and its integration with analysis tools, in addition to others for the life cycle management of the data.
tools like Collibra Data Governance and Catalog, IBM InfoSphere Information Governance Catalog either Alation Data Catalog and Governance They are the ones that mainly have “made themselves strong” in this field.
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) solutions are tools and technologies designed to automate repetitive tasks and processes in an organization using robot software or “bots”. These bots can mimic human actions on computer systems and applications, allowing manual and rule-based activities to be automated efficiently and accurately, which can be very helpful in modernizing and managing much more efficiently.
Its implementation helps companies reduce operating costs, improve efficiency and accuracy, and speed up the execution of business processes. Bots can interact in user interfaces in the same way as a human, have the ability to understand business rules and logic that guide their automated behavior, and can integrate into other systems, modern applications, and various sources. Normally RPA solutions are more effective when it comes to working on highly structured and repetitive processes and not so much on tasks that require creativity or complex decision making.
Platforms like UiPath, Automation Anywhere, Blue Prism, WorkFusion or Pega RPA are popular in this field.
Data Visualization and Business Intelligence (BI)
Data visualization and Business Intelligence (BI) are fundamental practices in data analysis and business decision making. They involve graphical and visual representation of complex data to facilitate understanding, spotting patterns, and identifying valuable information.
In data visualization tools, the main objective is from the data that the company has, to be able to effectively communicate ideas, trends and patterns that can be hard to understand using raw data only, for which the simple (and often clever) use of graphs and tables, elements of storytelling visual, interactive representations, etc.
To help companies make better decisions, these tools either include or can be completed with other tools. business intelligence that go a step further for their analysis, include scorecards and control panels of different KPIs, are capable of making ad hoc reports based on specific needs or, using machine learning and AI can venture to do predictive analysis on future results.
In this area, solutions such as Tableau, Power BI and QlikView allow you to create interactive visualizations and control panels to facilitate decision making.
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