Top 10 Analytics Software Tools for Perfect Data Management
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Any data scientist worth their salt knows that the right tools make all the difference in the world. And of all the tools in their arsenal, data management solutions are arguably among the most important. After all, the first step to drawing insights from data is to ensure that said data is properly orchestrated and organized.
Without that crucial phase, it doesn’t matter how good your data analytics software is. You’re going to miss crucial information and overlook valuable insights as a matter of course. With that in mind, here’s some of the best analytics software for data management — solutions to help your company even further enhance its analytics capabilities.
Primarily geared towards inbound marketing, HubSpot is nevertheless a powerful all-in-one analytics platform, one which can easily serve as a benchmark for other tools. Alongside powerful user analytics, it’s equipped with robust reporting functionality. Through an intuitive dashboard, it also allows you to organize and visualize all of your marketing assets and the data associated with them.
Developed as a tool for visually modeling, designing, and optimizing databases in MySQL environments, MySQL Workbench also provides a comprehensive suite of features to assist in database migration, configuration, and backup. It provides you with everything you could possibly require for more effective relational database management. Workbench pairs an intuitive visual interface with powerful and comprehensive functionality including color syntax highlighting, extensive database schema, and execution history.
A cloud-based data visualization and analytics solution specialized in business intelligence, Looker is designed to connect directly to databases without requiring extensive configuration or additional downloads. This allows it to capture information from multiple sources, including embedded analytics and application data. Captured data is presented in such a way that reports are easy to understand even without analytics expertise.
As one of the world’s leading data integration platforms, the Java-based Talend is capable of seamlessly processing millions of records in realtime. In addition to data integration and preparation, the ETL tool includes a stitch data loader that makes it easier to juggle multiple data management requirements. Talend also offers a data governance solution that centralizes data into a single hub which can then be accessed as needed by multiple stakeholders.
In 2019, Talend was named a Leader in Gartner’s Magic Quadrant for Data Integration Tools and Data Quality Tools
5. Apache Spark
The open-source Apache Spark is a high-performance processing engine that has gained a well-deserved reputation as the gold standard for big data management. With support for a multitude of programming languages and frameworks, Spark is incredibly flexible, capable of running on Kubernetes, Apache Mesos, Hadoop, standalone, or in a cloud instance. As one of the Apache Software Foundation’s most successful projects, Spark is supported by a passionate, active development community.
Standardization is the name of the game for Profisee, a cloud-native data management solution that automates consolidation, orchestration, and data hygiene across an entire business. Built as much for ease of use as it is for scalability, Profisee is incredibly flexible, simple to integrate, and highly customizable. In short, it has the potential to be an invaluable addition to your analytics company’s toolkit, a great value add for your customers, or both.
No piece on data management would be complete without at least a passing mention of Oracle. The Oracle Data Management Suite is an incredibly powerful platform that enables seamless governance, compliance, standardization, and visibility across an organization’s entire data ecosystem. It’s built on a principle known as master data management, eliminating duplicate data and consolidating information from across an organization into a single, authoritative source.
Named as a leader in Gartner’s Magic Quadrant for nearly ten consecutive years, Tableau is a data management and visualization platform that translates raw data into a more intuitive, understandable format. It essentially democratizes data analytics, creating visualizations through functions such as data blending and real time analysis. Capable of seamlessly integrating with a wide array of different systems, Tableau also offers incredibly powerful data exploration functionality.
Google offers what is quite possibly the largest suite of data analytics and data management solutions on the market. Available primarily via the Google Cloud Platform, Google’s analytics and management tools are capable of connecting to a wide range of different data sources, including several of the tools mentioned on this list. These are all tied together by an intuitive workflow manager.
Scalable, agile, reliable, and flexible, Alooma is a powerful data integration platform that shatters data silos by consolidating them into BigQuery. Armed with built-in ETL functionality, it’s designed to give users full visibility into and control of their data, supported by tools that allow a business to effortlessly monitor data quality and integrity. It’s also built with security and redundancy in mind, helping to ensure that data is not lost or compromised during processing.
Where Does CloudShare Come Into the Picture?
Although many of the data management tools we’ve described above are designed to be intuitive, that doesn’t mean they’re without any sort of learning curve. If your organization is to make the most of them, you need to ensure that you have effective training and onboarding practices in place. CloudShare can help with that.
As the number one platform for hands-on virtual training, we’ll help you incorporate engaging, active learning directly into your software training. Through a combination of instruction and practice, we’ll help you provide your people with a deep and thorough understanding of every software tool in your arsenal, no matter how complex.
Whether you’re looking to improve customer training, enhance internal skills and knowledge, or arm your data scientists with a sandbox in which they can experiment, we’ve got you covered.
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