Best Data Analytics Tools - For Data Wrangling, Analysis, and Visualization!
The best data analytics tools can cleanse, transform, wrangle, and even visualize your siloed data. Here are some credible options for you to have all those capabilities for yourself.
“Data is the new gold,” is a marketing line by Salesforce. And, if you are an aspiring data analyst, data scientist, marketing analyst, or anyone who deals with data or wants to learn to deal with it, you must’ve heard this quote. But to uncover that gold, we need the best data analytics tools. Read More
14 Best Data Analytics Tools to Uncover Siloed Data in 2024!
Redash
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Frustrated with siloed data and limited access to insights? Well, look no further than Redash. The tool provides a gateway to democratize data analysis. It is an open-source platform that empowers everyone in your organization. This can range from technical analysts to business users that unlock the power of data.
This is among the data analyst tools that go beyond storing information. The tool delivers a user-friendly interface for anyone to write queries, create stunning visualizations, and share data-driven insights.
Redash even allows you to effectively collaborate with colleagues. It helps explore trends that are hidden within your data and make informed decisions all within Redash’s secure and scalable environment. So, if you are a small business or a large enterprise, the tool puts the power of data analysis at your fingertips.
Why we picked it?
Metabase
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Metabase is an open-source business intelligence (BI) tool that empowers everyone in your organization. It helps you ask questions and discover insights related to them via the company’s database regardless of technical background.
Forget complex SQL queries, with this one of the most amazing tools for data analytics, you get a user-friendly interface with drag and drop visual builder. In fact, you can craft insightful questions by exploring data via various visualizations and build beautiful dashboards to share your findings with your team.
Metabase delivers a culture of data-driven decision-making. It turns information overload into actionable knowledge. Start turning your questions into answers and unlock the true potential of your data with Metabase.
Why we picked it?
IBM Cognos
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IBM Cognos Analytics is a powerful and comprehensive suite that delivers business intelligence. The tools empower organizations of all sizes to transform raw data into actionable insights that drive strategic decision-making.
Unlike simpler reporting tools, Cognos Analytics also delivers a set of robust features that cater to a wide range of users. In fact, business analysts can use data modeling capabilities to create a single source of truth. Also, data scientists can utilize it for uncovering hidden patterns.
Furthermore, it delivers user-friendly dashboards and reports for executives and managers. It helps them monitor key performance indicators (KPIs) for marketing, sales, social media, product development, etc., and even gain real-time insight into business health.
So, if you are looking for an in-depth exploration of clear concise visualization, IBM Cognos delivers a scalable and secure platform to unlock true value.
Why we picked it?
Oracle Analytics Cloud
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In an increasing pool of company data, finding insights can be a daunting task. Oracle Analytics Cloud (OAC) emerges as an intelligent tool that offers a secure and scalable cloud-based platform for comprehensive business analytics.
OAC can empower a wide range of users ranging from business analysts to data scientists. It can explore and analyze information via a user-friendly interface.
Furthermore, if you are looking to connect various data sources, build interactive dashboards, or use built-in machine learning for insights, OAC delivers a handful set of tools.
The tool eliminates the need for complex infrastructure management and allows its users to focus on extracting value from your data. It helps collaborate with colleagues, discover hidden trends, and make data-driven decisions that propel business forward. Basically, you get all this with a secure and powerful environment of Oracle Analytics Cloud.
Why we picked it?
PreviousBag relevant views, higher downloads & real shares for your app
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Data Analytics Tools - Suitable Industries!
Our data analytics tools list comprises tools that are suitable for all sorts of businesses, be they small or big. However, each tool shines in particular areas and industries. Figuring out those, we have created a table below for you to make an effective decision.
| Tool | Scalability | Data Source Connectivity | Visualization Capabilities | Self-Service Analytics | Advanced Analytics Features |
|---|---|---|---|---|---|
| Microsoft Power BI | Good | Excellent | Excellent | Good | Moderate |
| SAP BusinessObjects | Moderate | Good | Good | Moderate | Good |
| IBM Cognos | Moderate | Good | Good | Moderate | Good |
| Oracle Analytics Cloud | Good (with configuration) | Excellent | Excellent | Good | Good (integrations needed) |
| RapidMiner | Moderate | Good | Good | Moderate | Excellent |
How We Created the List of the Best Data Analytics Tools & Software?
To provide the best data analytics tools for our users, we underwent certain factors and came up with our criterion. Let’s extrapolate:
1. Understanding User Requirements
- Data Types: Professionals often struggle with analyzing structured and unstructured data. So, the list should have had tools that deal with both these types of data.
- Data Volume and Complexity: Factors like the amount of data, its simplicity and complexity, and advanced analytics techniques determined the list. Our data analytics tools list, therefore, comprises tools that are capable of providing the necessary scalability in terms of data.
- Analysis Goals: The tools selected should be capable of providing different kinds of insights. For example, descriptive statistics, trends, forecasting, or machine learning.
2. Technical Considerations
- User Skills: Different data analysts have different levels of technical expertise. So, the data analytics tools added should cater to both advanced users as well as beginners.
- Data Integration: We considered the integration capability of the tools with different data sources, for example, databases, cloud storage, etc.
- Scalability: Are the tools capable of handling your current and future data volumes?
3. Other Important Factors:
- Security and Compliance: We considered that the security of the tool does meet the required data privacy and regulatory compliance needs.
- Cost: Keeping in mind the licensing fees, training costs, and ongoing maintenance, we considered the limitations and features provided by these data analytics software tools.
- Vendor Support: Is the customer support responsive and helpful? Is documentation good enough?
By considering each of these factors, we were able to create our data analytics tools list. Basically, we tried to cater to general as well as some specific needs. This helped us create a list that is ideal for both beginners and advanced users.
Common Types of Data Analytics Tools
Data analytics tools come in various types. Each of these types can sort different tasks within the data analysis workflow. So, here’s a breakdown of some common types:
1. Data Wrangling and Transformation Tools:
- These data analytics tools clean, prepare, and transform raw data into usable formats for analysis.
- Examples: OpenRefine, Trifacta Wrangler, and Python libraries like Pandas.
2. Data Visualization Tools:
- These are data and analytics tools that help create charts, graphs, and other visual representations of your data to communicate insights effectively.
- Examples: Tableau, Power BI, QlikView, and Google Data Studio (Looker Studio).
3. Statistical Analysis Tools:
- These tools for data analytics allow you to perform statistical analyses on your data. For example, calculating means, medians, correlations, and conducting hypothesis tests.
- Examples: R, SAS, Python libraries like SciPy and Statsmodels.
4. Programming Languages:
- While, these are not strictly tools, programming languages like Python and R offer powerful libraries and frameworks. These can be utilized for data analysis, data manipulation, and complex modeling.
- These languages provide a lot of flexibility and customization but, they require programming knowledge.
5. Business Intelligence (BI) Tools:
- These are comprehensive platforms. They combine data visualization, data warehousing, reporting, and sometimes even basic analytics capabilities.
- These are often targeted towards business users and provide user-friendly interfaces for data exploration and creating reports.
- Examples: Microsoft Power BI, Qlik Sense, SAP BusinessObjects.
6. Machine Learning and Artificial Intelligence (AI) Tools:
- These tools for data analysis allow you to build and train machine learning models for tasks like classification, prediction, and anomaly detection.
- Some data visualization and BI tools incorporate basic learning functionalities.
- Examples: TensorFlow, Scikit-learn (Python library), RapidMiner, etc.
7. Data Warehousing and Data Lake Tools:
- These are not analytical tools themselves. However, they play a crucial role in storing and managing large datasets for analysis.
- Data warehouses store structured data for querying and reporting. Contrarily, data lakes store both structured and unstructured data for broader exploration.
- Examples: Amazon Redshift, Microsoft Azure Synapse Analytics, and Google BigQuery.
Top Data Analytics Tools - Pitting the Contender Against Each Other!
To better understand the best data analysis tools, we identified the top 5 tools and compared them against each other. Here’s the table:
| Tool | Scalability | Data Source Connectivity | Visualization Capabilities | Self-Service Analytics | Advanced Analytics Features |
|---|---|---|---|---|---|
| Microsoft Power BI | Good | Excellent | Excellent | Good | Moderate |
| SAP BusinessObjects | Moderate | Good | Good | Moderate | Good |
| IBM Cognos | Moderate | Good | Good | Moderate | Good |
| Oracle Analytics Cloud | Good (with configuration) | Excellent | Excellent | Good | Good (integrations needed) |
| RapidMiner | Moderate | Good | Good | Moderate | Excellent |
Explanation:
- Microsoft Power BI: It delivers powerful and user-friendly features. However, scalability can be a big issue for extremely large datasets.
- SAP BusinessObjects: It is an established platform with good capabilities across all areas. However, scalability for big data might be limited.
- IBM Cognos: Another established BI platform with good functionality, but again, scalability for big data is a concern.
- Oracle Analytics Cloud: The tool delivers strong data handling and visualization. However, advanced analytics might require additional configuration or integration with tools.
- RapidMiner: It is not suitable for big data analysis, much like other tools. However, it excels in data mining and machine learning tasks.
Remember, this table of tools for data analysts is simply a starting point. You need to consider your own specific needs and priorities to evaluate tools that are suitable for you.
Conclusion
The importance of having the best data analytics tools at your disposal is no hidden truth. Our intent with this list was to help you find a relevant tool that can integrate easily with your cloud storage and in-house databases.
These tools can help you find insights that can help you tackle the current competition in the market and entice new customers. With us saying this, we hope that this list may have provided you with some relevant top data analytics tools as options. Options that will help you maximize efforts for your niche and use case.
Also, on an ending note, do you have a data analytics tool or any digital product that can help people? If yes, get your product listed. At MobileAppDaily, we create listicles for the best digital products in different categories. Products in these listicles are placed on relevant rankings. Being a part of this ecosystem can help you with getting new customers, branding, and more credibility in the market. So, don’t wait, and become a part of MAD (MobileAppDaily).
And get the attention of the right audience with our Strategic Marketing Solutions.
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Sakshi Kaushik is a wordsmith extraordinaire who transforms complex technical jargon into captivating, must-read articles. Armed with a Masters in Economics, Sakshi dissects intricate topics with the precision of a seasoned expert. Her insights have graced prestigious platforms like Hackernoon, Ecowiser, and Medium, captivating readers and tech aficionados alike. With a career spanning influential companies like Teleperformance, Finex, and SparxIT Solutions, Sakshi is well-versed in navigating both the keyboard and the boardroom.
In addition to her extensive experience, Sakshi holds HubSpot certifications in Digital Advertising and Content Marketing, and has earned further credentials from UpGrad, Coursera, and Great Learning. Dedicated to sharing her expertise with mobile app developers and tech enthusiasts, Sakshi's passion shines through her writing. When she's not crafting compelling content, she enjoys diving into thrilling novels and exploring diverse worlds.
