Skilled data analysts know best open source data analytics software and different types of data analytics tools that can be used for a variety of corporate and business objectives. Here I will share my experience about the best data analytics software after testing more than ten top tools from the market
If you’re looking at what is the best data analytics software and you want a fully functional solution, then look no further. The data analytics tools listed here will give you all of the functionality that you need to handle even the most complex analysis problems. All of these tools are open source, so if you’re working with a budget, and are trying to decide between free ones or paid versions — then I have good news for you: a number of them have free versions available.
SAS
- SAS is a statistical software suite widely used for BI (Business Intelligence), data management, and predictive analysis.
- SAS is proprietary software, and companies need to pay to use it. A free university edition has been introduced for students to learn and use SAS.
- SAS has a simple GUI; hence it is easy to learn; however, a good knowledge of the SAS programming knowledge is an added advantage to use the tool.
- SAS’s DATA step (The data step is where data is created, imported, modified, merged, or calculated) helps inefficient data handling and manipulation. SAS’s data analytics process is as shown:
- SAS’s Visual Analytics software is a powerful tool for interactive dashboards, reports, BI, self-service analytics, Text analytics, and smart visualizations.
- SAS is widely used in the pharmaceutical industry, BI, and weather forecasting.
- Since SAS is a paid-for service, it has a 24X7 customer support to help with your doubts.
- Google, Facebook, Netflix, Twitter are a few companies that use SAS.
- SAS is used for clinical research reporting in Novartis and Covance, Citibank, Apple, Deloitte and much more use SAS for predictive analysis
To know more about SAS you could visit here.
Sisense
Sisense simplifies business analytics for complex data. Powered by In-Chip and Single Stack technologies Sisense delivers unmatched performance, agility and value, eliminating much of the costly data preparation traditionally needed with business analytics tools and providing a single, complete tool to analyze and visualize large, disparate data sets without IT resources. Sisense’s expertise in complex data includes both large data sets and data derived from multiple, disparate sources. Sisense leverages In-Chip analytics to dramatically improve business users’ access to advanced analytics on low-cost, commodity machines without the need for special data warehouse tools or dedicated IT staff. One-click formulas offer…
Overview
Features
•Join data from multiple sources
•Drag & drop joining of multiple data sources
•Build interactive dashboards with no tech skills
•Share interactive dashboards
•Provide users the freedom to query data in real-time
What is best?
• Single-Stack™ architecture
• Sisense innovative In-Chip™ engine
• Groundbreaking. Powerful. Simple.
What are the benefits?
• IoT, machine learning, and AI in cutting edge BI platform
• Easily and quickly get the answers they need from complex data
• Easy to connect your data, get up and running instantly and deliver fast results
Bottom Line
Sisense leverages In-Chip analytics to dramatically improve business users’ access to advanced analytics on low-cost, commodity machines without the need for special data warehouse tools or dedicated IT staff. One-click formulas offer the most popular statistical functions, such as correlation, covariance and various distributions, directly from Sisense dashboards.
Sisense for Cloud Data Teams
Sisense for Cloud Data Teams formerly Periscope Data is an end-to-end BI and analytics solution that lets you quickly connect your data, then analyze, visualize and share insights. Periscope Data can securely connect and join data from any source, creating a single source of truth for your organization. Perform BI reporting and advanced analytics operations all from one integrated platform. Communicate insights more effectively by selecting from Periscope Data’s wide range of visualization options (including standard charts, statistical plots, maps and more) and instantly share real-time insights via direct linking, email or Slack. Periscope Data is a data analysis tool that unifies business data across multiple…
Overview
Features
•One Tool For All Your Data Needs
•Enable data experts to answer more complex questions, quickly.
•SQL Editor Built By Experts, For Experts
•Advanced Analytics With Python And R
•Intuitive Drag-and-Drop Interface
What is best?
•Enable data experts to answer more complex questions, quickly.
•Intuitive Drag-and-Drop Interface
•Ensure Control, Consistency and Trust
What are the benefits?
•One Tool For All Your Data Needs
•Tailored For Every Workload
•Combine all of your data sources into a single platform for powerful analysis and insights
Bottom Line
Periscope Data is a data analysis tool that unifies business data across multiple different data sources.
Python
- Python was initially designed as an Object-Oriented Programming language for software and web development and later enhanced for data science. Python is the fastest-growing programming languages today.
- It is a powerful Data Analysis tool and has a great set of friendly libraries for any aspect of scientific computing.
- Python is free, open-source software, and it is easy to learn.
- Python’s data analysis library Pandas was built over NumPy, which is one of the earliest libraries in Python for data science.
With Pandas, you can just do anything! You can perform advanced data manipulations and numeric analysis using data frames.
Pandas support multiple file-formats; for example, you can import data from Excel spreadsheets to processing sets for time-series analysis. (By definition – Time-series analysis is a statistical technique that analyses time series data, i.e., data collected at a certain interval of time)
Pandas is a powerful tool for data visualizing, data masking, merging, indexing and grouping data, data cleaning, and many more.
To know more about Pandas, checkout Python Pandas Tutorials.
- Other libraries, such as Scipy, Scikit-learn, StatsModels, are used for statistical modeling, mathematical algorithms, machine learning, and data mining.
- Matplotlib, seaborn, and vispy are packages for data visualization and graphical analysis
- Python has an extensive developer community for support and is the most widely used language
- Top Companies that use Python for data analysis are Spotify, Netflix, NASA, Google and CERN and many more
R
- R is the leading programming language for statistical modeling, visualization, and data analysis. It is majorly used by statisticians for statistical analysis, Big Data and machine learning.
- R is a free, open-source programming language and has a lot of enhancements to it in the form of user written packages
- R has a steep learning curve and needs some amount of working knowledge of coding. However, it is a great language when it comes to syntax and consistency.
- R is a winner when it comes to EDA(By definition – In statistics, exploratory data analysis(EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods).
- Data manipulation in R is easy with packages such as plyr, dplyr, and tidy.
- R is excellent when it comes to data visualization and analysis with packages such as ggplot, lattice, ggvis, etc.
- R has a huge community of developers for support.
- R is used by
- Facebook – For behavior analysis related to status updates and profile pictures.
- Google – For advertising effectiveness and economic forecasting.
- Twitter – For data visualization and semantic clustering
- Uber – For statistical analysis
To know more about R you can visit here:
Chartio
Platform: Chartio
Description: Chartio is a cloud-based data discovery platform that lets you create charts and interactive dashboards. The product features a proprietary, visual version of SQL that enables any user to explore, transform and visualize data via a flexible drag-and-drop interface. There is no need to build data models in advance. Chartio includes a set of pre-built connections to data sources like Amazon Redshift, Google BigQuery and Snowflake, while also enabling direct access to CSVs and Google Sheets.
Domo
Platform: Domo
Related products: Domo Everywhere, Domo integration Cloud
Description: Domo is a cloud-based, mobile-first BI platform that helps companies drive more value from their data by helping organizations better integrate, interpret and use data to drive timely decision-making and action across the business. The Domo platform enhances existing data warehouse and BI tools, and allows users to build custom apps, automate data pipelines, and make data science accessible for anyone across the organization through automated insights that can be easily shared with internal or external stakeholders.
Hitachi Vantara
Platform: Pentaho Platform
Related products: Lumada Data Services, Pentaho Data Integration
Description: Hitachi’s Pentaho analytics platform allows organizations to access and blend all types and sizes of data. The product offers a range of capabilities for big data integration and data preparation. The Pentaho platform is purpose-built for embedding into and integrating with applications, portals, and processes. Organizations can embed a range of analytics, including visualizations, reports, ad hoc analysis, and tailored dashboards. It also extends to third-party charts, graphs and visualizations via an open API for a wider selection of embeddable analytics.
BigID
Our score: 8.7User satisfaction: 97%
A modern data intelligence platform that enables enterprises to discover, manage, and protect data critical to their business. Powered by machine learning, the software allows for more efficient, consistent, and scalable data governance across any data in the cloud or data centers. It makes it possible for companies to know their data and take action for protection, privacy, and perspective.
ThoughtSpot
Key Insight: While not as well known as some other data analytics software vendors, ThoughtSpot offers a next-generation “search first” tool that earns it a berth as a leader in the market.
ThoughtSpot offers any number of compelling features, particularly an AI-based recommendation system that leverages crowdsourcing. Additionally, sources for its query options range from a legacy provider like Microsoft to a “new kid on the block” like Snowflake.
But most attractive of all, ThoughtSpot’s calling card in a crowded market is its search-based query interface. Users can input a complex analytics query – by typing or speaking – and the ThoughtSpot platform uses augmented analytics to offer insight. Impressively, it can handle large data queries, with many users sifting through more than a terabyte of information. All of this is accomplished – from comparative analysis to anomaly detection – with no software code required. So business staff can data mine without the help of experts.
Pros:
- The search interface allows easy queries of complex questions, analyzing billions of data rows with artificial intelligence.
- Founded in 2012 as a growing company, the company has ridden the wave of enterprise analytics to a solid niche in the analytics sector.
- Well regarded for its ability to scale and handle ever-larger query loads.
Cons:
- Without the large product portfolio of some vendors, users will need to bring their own related tools, like
Conclusion:
Data analytics allows you to examine, evaluate and interpret data in order to draw useful conclusions and make informed decisions. In this article, we’ll consider the best open source data analytics software and the best data analytics tools for business in your review so that you can figure out what will work best for your company.