Technology has spread its usage all over the world. There is no field left which has not yet experienced the touch of technology. Having said that it has now become easy to analyze all the data that you have and derive the maximum benefit out of this information.
There are data analysis tools that provide all the information that you need and also retrieve crucial details that are missing. We went through some such crucial data analyzing tools and prepared a list of the best ones available.
You can go through this list and understand the features and benefits of each one of them. Once you are done you can comfortably choose one that you think fits your need the most.
Plus you can also get to know how each one of them can make your data analysis tasks simplified and use that information.
1. Tableau Public
Bloggers are starting conversations about topics from global health to policy to sports. By adding data to the conversation, bloggers can bring a new perspective to a topic.
Tableau public lets bloggers publish data visualizations on any topic and make them interactive so readers can dig in. Save and store your visualizations on your tableau public profile.
We give you 10gb of space. Personalize your profile and connect with other authors. Share your visualizations with the world via social media. Or embed them on a site or blog. Your published vizzes are always live and interactive.
Tableau public was used during the initial onboarding stages of the tableau platform (when we were making decisions about what dashboard program to implement organization-wide).
It was used across the entire organization in different capacities: as an analyst, I primarily worked on requirements gathering for dashboards from clients and internal teams and then built the dashboards to the gathered specifications. Data engineers built APIs and data pipelines to provide the requisite data to test the platform.
Clients and internal team members used the tableau public to view and present different reports based on the data from different platforms. Tableau public, like tableau (corporate or enterprise), is a dashboard program meant to work with large datasets and visualize and present that data in digestible formats.
In this sense, it addresses the issues of understanding the story data tells: its viz capabilities are great for summarizing data (say, monthly spending performance), and identifying trends using different visualizations.
OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another, and extending it with web services and external data.
Review: Software Advice
Allows working with a substantial amount of data powerful yet relatively simple scripting allows to re-apply past actions
Doesn’t seem to be actively supported/maintained old interface makes it unintuitive to deal with files no real automation features
RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics
Rapidminer offers a robust and very powerful integrated suite of tools and features, with each component parading a user-friendly interface that helps users realize massive productivity gains right from the get-go.
Its visual workflow designer tool offers users with an easy to use visual environment that enables them to design, create, and deploy analytics processes, visual presentations, and models without breaking a sweat.
With rapidminer, uncluttered, disorganized, and seemingly useless data becomes very valuable. The system simplifies data access and manager, allowing you to access, load, and evaluate all sorts of data, including texts, images, and audio tracks. Rapidminer lets you structure them in a way that it is easy for you and your team to comprehend.
Knime, the Konstanz information miner, is a free and open-source data analytics, reporting and integration platform. Knime integrates various components for machine learning and data mining through its modular data pipelining concept
Pros: Doesn’t require coding, programming skills to perform data mining.
Cons: Visualization capability may not appeal to some.
Overall: Performed data mining and predictive analytics on this software. Its easy to master and customer support is good.
NodeXL Basic is a free and open-source network analysis and visualization software package for Microsoft Excel 2007/2010/2013/2016.
NodeXL Pro is a fee-based fully featured version of NodeXL that includes access to social media network data importers, advanced network metrics, and automation.
What do you like best?
With NodeXL, you can import social data from a variety of sources (I use mostly for Twitter search data), and, in the same tool, analyze the data and create social network visualizations.
I like the fact that it has everything in one space – it imports the data to an Excel spreadsheet and you can analyze the date using the plugin. You can also add your own data (import from other sources, or type it in as a spreadsheet) and use it only to analyze social data / create visualizations.
What do you dislike?
I removed one point because it is not supported on Excel for Mac – I have to run Windows on a virtual machine to get access to the plugin, which makes my machine a little bit slower than ideal.
Recommendations to others considering the product
Use the trial and see if you can get used to the tool.
What business problems are you solving with the product? What benefits have you realized?
It helps a lot with social network analysis for Twitter events, and also brand centered networks.
6. Google Fusion Tables
When talking about Data Analytics tools for free, here comes a much cooler, larger, and nerdier version of Google Spreadsheets.
An incredible tool for data analysis, mapping, and large dataset visualization, Google Fusion Tables can be added to business analytics tools list. Ranked among the top 10 Data Analytics tools, Google Fusion Tables is fast gaining popularity.
7. Google Search Operators
It is a powerful resource which helps you filter Google results instantly to get the most relevant and useful information.
- Uses of Google Search Operators.
- Fast filtering of Google results.
- Google’s powerful data analysis tool can help discover new information or market research.
This search operator is perfect when you are performing research for an upcoming blog post. You can easily find blogs which contain a certain term within the blog title and identify topics where your new blog post may be most useful to readers.
Solver is a Microsoft Excel add-in program you can use for what-if analysis. Use Solver to find an optimal (maximum or minimum) value for a formula in one cell — called the objective cell — subject to constraints, or limits, on the values of other formula cells on a worksheet.
My organization is using it for optimization problems related to raw material and finished goods for their shipments and production planning purposes. It is used only in my function- sales and operations planning. This tool is useful for the company who wants to build the optimization model to excel for quick strategic and tactical decisions. The solver is easy to understand and learn.
My company is using solver for product optimization, manufacturing plan, and raw material planning. It helps a business to understand the financial estimates across the distribution network. It helps to provide a feasible solution for production to produce what product at what location in an optimized manner.
9. Dataiku DSS
Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build and deliver their own data products more efficiently.
With dataiku dss, converting raw data into useable, real-time predictions requires only one interface from start to finish. Users can explore, wrangle, and prepare without worrying about issues regarding format, storage, accessibility, and the like.
The platform boasts of more than 25 connectors, with the option to create your own. Users can access all kinds of data anytime – from big or small, structured or unstructured, internal or external.
Dataiku’s quick columns view allows users to see the quality of data in real-time – from duplicates and invalids, to completeness and accuracy, to distribution and outliers. Full statistical summaries are also provided with just one click.
Sisense’s bi software makes it easy to instantly reveal business insights from complex data – any data source, any size. Sisense is focused on redefining every aspect of business analytics to make it easy for anyone to discover business insights.
Get up and running instantly, so you can discover answers on the fly & deliver ROI in just weeks or months. You have seriously large data coming from many different sources, and you can’t get the answers you need.
That’s why sisense’s data analytics software is built to help business users take complex data and instantly create insights that drive your entire business.
Pros: High performance with huge amounts of data and multiple tables, scalability for thousands of users, very simple to create and publish advanced data models and dashboards, no need to pre-aggregate data, no need to use additional ETL tools or to create an external and expensive data warehouse. Support is fantastic, if required they would literally access your server and take control of mouse and keyboard to help you solve any problem or to create anything.
Cons: There is always room to improve and Sisense use to release new versions and functionalities every quarter or so, but I can say I am pretty satisfied and impressed so far.
Overall: Our company developed an OEM and white-labelled version of Sisense, providing advanced analytics mostly for retail companies in Brazil. Our clients are very satisfied with our solution, and we are growing fast supported by Sisense.
Knime Analytics Platform is the leading open solution for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures.
With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist.
Pros: Knime desktop is a powerful tool for building analytical workflows. The visual interface is extremely helpful. They also have extensions to integrate other tools like R and Python into the workflows. Best of all you can share your workflows with others – great for reproducible research. There are built-in tools for many types of supervised and unsupervised machine learning. The desktop application is a free and open source. The support community on the Knime website is very active and responsive. To extend the features you can purchase Knime server.
Cons: Like any new tool there is a learning curve. However, they have lots of videos, examples and an active support community. There are some features that are not intuitive, such as how to use flow variables. In general, I have found that I use R much less now and do most of my analysis in Knime. It is primary drag and drops and requires little to no coding.
Overall: We were able to build a reproducible workflow for analyzing our data and creating actionable insights.
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.
If you have questions about R like how to download and install the software, or what the license terms are, please read our answers to frequently asked questions before you send an email.
Review: CLASS CENTRAL
In this course, you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox.
Pros: Software is user friendly and can be understand with a minimum supervision
Cons: at this stage, everything is doing well using this software
Pentaho’s new integration with Hitachi Content Platform improves hybrid and multicloud management and uncovers new business solutions with unstructured data.
We rolled out Pentaho within one of our business units as a way to get at trapped data from an old home-grown CRM system that had been recently updated to .net. Pentaho quickly got us access to the data and the built-in ETL tools were easy and quick to use and learn. It enabled a team of two to provide enough data, reporting and analytical dashboards to support over $1.5B in sales.
Talend is the leading open source integration software provider to data-driven enterprises. Our customers connect anywhere, at any speed. From ground to cloud and batch to streaming, data or application integration, Talend connects at big data scale, 5x faster and at 1/5th the cost.
We are using Talend for the second time in our company for an ETL project. This software has been used once before in a previous project with Big Data and Mapr. It is said to be powerful but is full of strange bugs and the company is encountering problems with the maven repository. Sometimes it freezes a lot even on a powerful PC (I7 laptop).