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Data Management: Common Analysis Tools and Mechanics

The rate of data flow and access for use by businesses and organizations is increasing by the day. According to experts, over 20 zettabytes of data exists in the world today. The use of such data for the benefit of an organization has become paramount for growth.

This is called data management, which involves the collection of data, access to third-party databases, analysis, and presenting results that are sensible. Data management is achievable mainly through the help of analytics tools and mechanics or techniques. Throughout this article, we will be looking at the common tools that are used in data management and the mechanics they use to provide useful results.

Data Management Tools

If you have interacted with data in the past, you probably have heard about Microsoft data management suites, Oracle suites, and Google Cloud among others. Well, these are the most popular on the market today, and this is the case for a good reason. They will manage your data in the best way. Without further discussion, let us have a detailed look at them and a few others.

IBM Data Management Server

This tool has been in operation for quite some time now. We all know that IBM has been a data solution center for many years. This data management tool provides numerous analytics capabilities and lets users export data and reports for use in decision-making.

Microsoft Master Data

Whether you want to organize your company using models or other formats, this tool is flexible. You have the option of choosing Power BI, Azure, or SQL servers to be your primary data management platform. It is all about what works best for you.

Oracle Data Management Suite

If you are looking for a comprehensive suite to manage data in different formats and conduct a series of analyses, this is the option to go for. It is as powerful as you can imagine, which makes it one of the best and most used data management tools in the world.

SAP Data Management Tool

For companies that want to access all their data in a single access platform, this is the way to go. It is useful in both small and big organizations, giving them transactional and analytical capabilities. It is integrated with cloud capabilities, making it easy to access remotely and manipulate the way you want.

Google Cloud Data Management Tool

This is a purely cloud-based option, and it comes with an array of tools for businesses to utilize. The workflow is organized in NoSQL style, BigQuery, and cloud data transfer formats among many others that are introduced through regular updates.

How to Choose the Best Tools and Mechanics

Now that there are numerous tools to be used, it is important to choose well. There are many things to look for before you settle on one. First, consider the amount of data you want to handle at a specific time. There are those that handle large data sets and those that handle only a small amount of data.

Secondly, ensure that you are comfortable with the database type. Cloud-based options are the most popular due to the ease of access. However, if other types work well for you and have other features with benefits for your organization, you can also use them.

The cost is yet another thing to consider. Of course, the best tools are paid since they have many features, but there are also good open-source data management tools.

Final Word

When acquiring data management tools and mechanics for use in your organization, make sure that you have conducted careful research to choose the best. The previous list is not exhaustive since there are many others with great and helpful features. If you are not sure of what to use, it is best to hire an expert for guidance.

Originally posted 2020-08-19 17:48:51. Republished by Blog Post Promoter

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One comment

  1. The defining characteristics for big data are, in addition to their physical volume, and others that emphasize the complexity of the task of processing and analyzing this data. The set of VVV features (volume, velocity, variety – physical volume, the rate of growth of data and the need for their fast processing, the ability to simultaneously process data of various types) was developed by the Meta Group in 2001 in order to indicate the equal importance of data management in all three aspects.
    Initially, the set of approaches and technologies included tools for massively parallel processing of indefinitely structured data, such as NoSQL DBMS, MapReduce algorithms, and Hadoop project tools. In the future, other solutions that provide similar capabilities for processing ultra-large data arrays, as well as some hardware, began to be referred to as big data technologies.

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