Tech Tools to Streamline Your Organization's Data Initiatives


By Sierra Powell,

Regardless of whether your organization is a non-profit or a for-profit company, certain aspects of your company rely on innovation. That may mean the most high-speed system for batching payments from thousands of sources at one time to keep impeccable track of data. Continue reading to discover tech tools to streamline your organization's data initiatives.
Although companies don’t want their systems logged down by data or their employees working overtime to keep up with data, it is no secret that data is what drives a company's success. However, the benefits of having strong data tools include:
  • Helps companies make informed decisions
  • Prevents increasing the work to get information
  • Help find solutions to problems
  • Provides backup for debates
  • Improves customer service
For data-driven initiatives, companies rely heavily on certain tech tools. Some of the best for to assist organizations in the analytics process are:

1. Julia

Created in 2018, Julia is a system that combines high-level language with ultimate performance in calculations. It was designed to meet a company's data needs to avoid writing programs in another language and converting them to execution.
In addition to being a leader in data analytics, Julia’s benefits are:
  • Julia has a math friendly syntax for non-programmers
  • Julia is fast
  • Julia has an automatic memory management system
  • Julia processes with a multi-core processor

2. Manta

If your organization is looking for a system that will decrease problem-solving by almost 98%, Manta is the one. Recognized by Data Management Planning Guide and Top Czech Startups by Forbes, Manta delivers its clients a first-class product that gathers, sorts, and stores automated data lineage across several environments.
In addition to building a world-class map that shows the flow of data, it delivers that data through channels that technical and non-technical users understand. Manta supports an organization's governance because it provides users with a complete, accurate, and comprehensive reporting system that is easy to understand.

3. D3.js

This JavaScript library helps organizations create custom data visualization in a web browser. D3 stands for Data-Driven Documents, and it uses Scalable Vector Graphics and HTML. The makers of this program describe it as a flexible tool that requires the minimum amount of effort to generate.
As a tool for data initiatives, this system gives users complete control over visualization and customizing it. With more than 30 modules and 1,000 different visualization methods, this system is easy to use by data science teams and administrative staff.

4. Matplotlib

Matplotlib is a plotting library that organizations use to read, import, and visualize data in their analytics applications. Matplotlib allows data scientists and employees to visualize the data in their analytics applications.
Matplotlib uses hierarchy and has a simple set of plotting functions for users. It was designed to allow users to build their own visualizations with high-level commands. For data initiatives, Matplotlib is beneficial because:
  • It is simple to use and great for beginners
  • It provides high-quality images
  • It is compatible with formats such as png, pdf, and others

5. scikit-learn

As a learning library for Python, scikit-learn is built on the sciPy and NumPy scientific computing libraries. That means, as its name implies, it works best in sorting data for math, science, and engineering. As a supporter of numerous algorithms and models, scimitar-learn offers functionality for selection and evaluation and data preprocessing and later its transformation.
Released in 2010, this program started as a Google Summer of Code project in 2007 and became available universally in 2010. Other features of scikit-learn for an organization's data initiatives are:
  • Scikit-learn has supervised learning algorithms
  • Scikit-learn has various methods to check the accuracy of models
  • Scikit-learn has superior extracting features
  • Scikit-learn helps solve supervised learning problems


As you have read, organizations looking for tech tools have many options to streamline their data initiatives. Five great tools are listed above. Depending on your company's needs, one of them may be optimal for your success.

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