Top 30 Data Analytics Tools

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Data is the new oil. It has become a treasured commodity today for data analytics and has taken on a serious status. With the daily growing data volume, it is now at a scale that no human can deal with the amount manually. Businesses worldwide have found growth in their organizations by incorporating data analytics into their existing technology platforms. 

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The concept of data analytics has evolved over time and will continue to rise. Data analytics has become an important part of managing a business today, where every business owner wants their business to grow and increase its revenue in order to maintain a competitive edge in this ever-changing marketplace, they need to be able to use data effectively.

What is Data Analytics?

Data analytics is the science of studying raw data with the intent of drawing conclusions from it. It is used in multiple industries to allow companies and organizations to make more promising data-driven business decisions. 

Data analytics covers an entire spectrum of data usage, from collection to analysis to reporting. Understanding the process of data analytics is the ultimate power and it will be the future of almost every industry. 

There are multiple types of data analytics including descriptive, diagnostic, predictive, and prescriptive analytics. 

Let’s learn about the different types of data analytics in detail.

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Types of Data Analytics:

1. Descriptive Data Analytics:

Descriptive data analytics is the process of examining data to summarize what is actually happening. It provides a basic understanding of how the business operates and helps to identify which factors are affecting the business and which aren't. It supports the exploration and discovery of insights from your existing data and based on that provides a basic understanding of your business.

2. Diagnostic Data Analytics:

Diagnostic Data Analytics is used to diagnose any business problems. It generally answers the question: why did it happen? Data can be examined manually, or used by an automated system to generate a warning. Diagnostic data analytics is an advanced analytical approach that is used to find the cause of the problem faced by a business.

3. Predictive Data Analytics:

Predictive data analytics is a form of analytics that uses both new and historical data to forecast activities, behavior, and trends. It is used to analyze current data to make predictions about future events. One important use case for predictive analysis is to help retailers understand customer buying patterns and optimize inventory levels to maximize revenues.

4. Prescriptive Data Analytics:

Prescriptive data analytics is the last level of analytics that is performed on the outcome of other types of analytics. It is the process of defining an action based on descriptive and predictive data analytics results. In this stage, different scenarios are analyzed to determine how each scenario is likely to play out given past data. This can help businesses know what action to take for a good outcome.

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These four types of data analysis techniques can help you find hidden patterns in your data and make sense of it. All these types of data analytics are important in other ways and can be used in different business scenarios.

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Importance of Data Analytics:

Data analytics is extremely important for any enterprise and has become a crucial part of every organization's strategy in the past decade. The reason for this is simple: Big data has opened up a world of opportunities for businesses. Data analysts have become essential in helping companies process their huge sets of data for making meaningful decisions.

The benefits offered by analyzing data are numerous some of them are mentioned below: 

• It helps businesses to determine hidden trends and patterns.
• Improves efficiency and productivity of the business by helping them to take data-driven decisions.
• Identifies weaknesses and strengths in the current approach.
• Enhances decision-making, which helps businesses to boost their revenue and helps solve business problems.
• It helps to perform customer behavior analysis accurately to increase customer satisfaction 

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Data analytics lets you know what is working and what can improve. According to experts, the lack of data analysis and usage can result in failed business strategies and also cause loss of customers. So in order to take your business to the next level, one must always adopt data analytics techniques and should be familiar with the steps involved in it.

Data Analysis Process: Steps involved in Data Analytics

Steps in data analytics are a set of actions that can be performed to create useful and functional data. In this section, we will detail the stages involved in data analytics.

1. Understanding Business Requirements

One of the most important factors behind successful data analysis is a proper understanding of the business requirements. An analyst needs to have a clear idea about what kind of problem the business is facing and what can be done to overcome the problem. The other important task is to understand what type of data needs to be collected to solve the given problem.

2. Collecting Data

When it comes to data analytics, it is very important that the right kind of data is collected. After understanding the business problem the analyst should be aware of the type of data to be collected to solve the problem. Data can be collected in many ways, including survey forms, interviews, market research, web crawlers, log files, event log files, and even through social media monitoring apps.

3. Data wrangling 

In data wrangling, data is cleaned and managed so that it can be utilized in order to perform data analysis. This process can involve converting data from one format to another, filtering out invalid or incorrect data, and transforming data so that it can be more easily analyzed. Data wrangling is an important step in data analysis because it can help ensure that the data used in the analysis is of high quality and is in a suitable format.

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There are many steps involved in data wrangling, including 

1. Gathering data from a variety of sources.

2. Cleaning and standardizing the data.

3. Exploring the data to identify patterns and relationships. 

4. Transforming the data into a format that can be used for different tasks. 

5. Saving the wrangled data in a format that can be easily accessed and used in the future.

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The steps involved in data wrangling can vary depending on the type and format of data you are working with, but the final goal is always the same, to transform raw data into a format that is more useful for performing accurate analysis.

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4. Exploratory Data Analysis (EDA):

Exploratory Data Analysis (EDA) is a statistical approach used to achieve insights into data by summarizing its major features. This procedure is used to comprehend the data’s distribution, outliers, trends, and other factors. EDA can be used to select the best-fitting statistical models and input variables for a dataset.

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A typical EDA process might begin with a series of questions, such as

•  What are the primary components of the dataset?
•  What are the most significant variables?

•  Are there any outliers or unusual observations or behaviors? 

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After asking these basic questions, the analyst should then investigate the data visually, using charts such as histograms, scatter plots, and box plots. These visual methods can help to identify features such as trends, and unusual observations. This process of EDA can help to reveal important insights into the data, and can be used to guide further analysis.

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EDA can provide insights that may not be obvious from merely looking at the data itself. Overall, it is an essential tool for data analysis and should be used whenever possible.

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5. Communicating Results:

Communicating results is the final and the most vital aspect of the data analysis life cycle because it allows others to understand the conclusions of the analysis. Results also need to be communicated in a clear and concise way so they can be easily understood by people with less technical acumen as well. Additionally, conveying results allows for feedback and discussion to improve the quality of the findings during the analysis procedure.

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The data analytics life cycle generally goes through these five-step procedures that help to find precise conclusions. But apart from the benefits, some challenges are faced during the data analytics process.

Overall Challenges in Data Analysis:

There can be many types of challenges encountered during the data analysis journey but the two most common challenges are mentioned below:

1. Data issues
2. Data analysis-related issues.

1. Data Issues:

Data-related problems are one such type of issue encountered during the data analysis journey. Some data-related issues are mentioned below:

1. Incorrect or inaccurate data
2. Incomplete data
3. Data that is not timely ingested
4. Unorganized data
5. Irrelevant data
6. Data integration issues
7. Handling large datasets 

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The data team needs to guarantee to provide the correct data and a good and reliable data integration platform should be preferred to ensure correct and timely ingestion of data. A proper ETL tool that provides safe and secure data storage should be selected.

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2. Data Analysis Related Issues:

The data analysis process can be challenging if the data is not well-organized, some challenges are mentioned below:

1. Absence of skills to interpret data.
2. Data cleaning and preparation can be very time-consuming.
3. Choosing the right statistical method can be a challenge.
4. The results of the analysis can be misinterpreted.
5. Communicating the results in a simpler way can be tough 

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To overcome these challenges businesses should use low-code data analytics platforms that will help to save manpower and thus reduce costs. With careful planning and execution, one can easily perform analysis without any hassles. By using the right tools and techniques, businesses can overcome these challenges and make better data-driven decisions.

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Need for Data Analysis Tools:

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In a world where data is continuously being generated, it is becoming hard to make sense of it all without the help of data analysis tools.

There are many reasons why we need data analysis tools. They help us to process, understand, and make use of data effectively. Data analysis tools help us to see patterns and trends in data without actually coding. Nowadays, businesses don't need a highly skilled person to perform the data analysis process in fact they can perform the analysis on their own because of the tools present in the market.

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The data analysis tools in the market can also help to enhance communication and collaboration within your organization through alerts and email functionalities. In some cases, they can also help to automate decision-making processes.

Criteria For Choosing the Right Data Analysis Tool:

There is a wide variety of data analysis tools available in the market but the best-fitted tool for you will depend on the specific data set and the desired business outcome. When choosing a data analysis tool, it is essential to assess the specific features and capabilities of the tool, and the user’s needs should also be considered. For example, if you are looking to perform complex statistical analysis, then a statistical software package would be the best choice. On the other hand, if you are looking to create interactive dashboards, then a no-code data analytics platform would be a more suitable fit.

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Below listed are some criteria that one should consider before choosing the right data analytics platform according to the requirements.

1. No-code Data Analytics Platform: 

No-code data analytics platforms equip users with the capability to quickly analyze data with ease without having to write even a single line of code. This can save users a lot of time and effort by making data analysis more streamlined.

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Some benefits provided by such data platforms are mentioned below:

1. No technical skills required: Analysis of data on these types of platforms can be performed by users of all skill types and different experience levels. Data analysis is made more accessible to individuals which allows them to benefit from it.
2. Supports Different Data types: Wide variety of data can be analyzed be it structured or unstructured, which makes these platforms more versatile.
3. Easy Integration: Easy integration with different sources is one of the best features provided by no-code data platforms.
4. Flexible pricing plans: No-code platforms provide scalability and are proven to be very cost-effective. This feature makes them useful for businesses of all sizes and stature.

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If you are looking for a good and reliable no-code data analytics platform that has all these features then Sprinkle Data is the best option.

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     2. Availability of Different Types of Charts:

Charts can help to picture data, and spot trends and patterns effortlessly. They help to make intricate data more coherent and can help individuals to make better decisions. Charts used with proper statistical techniques can be useful in making predictions about future behavior as well. They also can be used to interpret and find relationships between different variables and are useful in finding outliers in data. Different types of charts can be used to perform accurate analysis, some important chart types include:

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1. Bar/column charts are one of the most typically used chart types and are especially helpful in comparing data points. 
2. Line charts are used for depicting changes over time.
3. Pie charts are helpful in determining proportions across various categories
4. Scatter plots are useful for visualizing relationships between two numerical data points and are primarily used to identify trends and outliers in data.
5. Histograms are used to give information about the data distribution.
6. An area chart is based on a line chart and is primarily used to depict quantitative data by covering the area below the line.
7. Combo Chart is a combination of a line and a bar chart that depicts trends over time.
8. Funnel charts help to portray linear processes with sequential or interconnected phases in the analysis.
9. A map is a geographical chart type used to visualize data point density across different locations.
10. A stacked bar chart is a form of bar chart depicting comparisons of different data categories.

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Charts are an integral part of any data analytics tool and can add meaning to the analysis. They help to communicate the conclusions of the analysis in a concise manner. So always choose a data analysis tool that has these charts with additional attributes like labels, a benchmark value, and different colors to easily differentiate.

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All the chart types mentioned above are available in the Sprinkle Data analytics tool accessible with just a single click. 

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    3. Dashboard With a Good Visual Interface

A dashboard is a visual user interface that provides easy access to key metrics and consists of a sequence of charts, tables, and other visual elements that can be customized and systematized to provide insights into specific datasets with advantages like delivering visibility into an organization's performance in real time. 

The key features that a dashboard should contain are mentioned below:

1. Interactivity: Dashboards with good interactivity permit users to filter and drill down into data for more detailed analysis.
2. Easily Editable layout: Customized dashboard show only the data that is relevant to the analysis.
3. Easy to share: Dashboards that can be easily shared with others to explore and analyze the data.
4. Less Runtime: A data analytics platform whose Dashboards take less time to run should be picked.
5. Monitoring: In case of a dashboard failure proper email alerts should be provided to the user with the reason for the error.
6. User-Friendly Interface: A dashboard with a user-friendly interface like drag and drop functionality is easy to use.
7. Live Dashboard: If you need to track data in real-time a live dashboard is the best option for your business.

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If you are confused about which data analytics platform should be preferred to get all these features then you should prefer Sprinkle Data.

The best dashboard for your needs is the one that must follow all these criteria and will depend on the type of data you need to track, and the level of detail you need to acquire. 

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    4. Cost Efficient:

A cost-effective data analytics platform helps to save money on software and hardware. These tools can help organizations save money in a number of ways. By enabling organizations to understand their data better, these tools can help to recognize zones where costs can be decreased. Moreover, a platform with flexible and scalable pricing plans should be adopted to pay a reasonable price according to the requirements.

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Sprinkle Data has a flexible pricing plan that is fully customizable according to the needs of users enabling them to save costs while performing high-level analytics.

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Ultimately, the best way to choose the right data analysis tool is to consult with experts in the field and try different tools to see which one works best for your specific needs.

Top 30 Data Analytics Tools in 2023

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In this section, we will cover the top 30 in-demand data analytics tools. Along with that, we will be examining the pros and cons of each and every data analytics tool to help you choose the best one according to your requirements.

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1. Sprinkle Data: 

Sprinkle Data is a No-code data pipeline and analytics platform that helps organizations to make more promising decisions and improve their performance. It provides users with insights into their data, helping them determine trends and patterns without writing a single line of code. Additionally, the Sprinkle Data Analytics platform helps users to create custom reports and dashboards.

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PROS:

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1. Basic chart types are present and more chart options can be easily developed based on the client's requirements.
2. The reports and dashboards can be embedded with a shareable URL, primarily for presentation purposes to the end users.
3. In dashboards, users can apply multiple filters through the filter mappings feature.
4. Sprinkle has a user-friendly drag-and-drop dashboard interface that enhances customer experience.
5. The dashboards support drilling up/down on data to uncover detailed information. 
6. Merging multiple reports on the basis of a common column is one of the best features available in this data analytics platform.
7. Other features like cloning the dashboard, easy dashboard sharing, and scheduling emails are also available.
8. Dashboards can process data efficiently ensuring high performance.
9. Two types of layout options are provided in the dashboard namely tab view and grid view.
10. 14-day full-featured trial is available and after the free trial, users can purchase a subscription on a monthly or yearly basis with flexible pricing plans.
11. Restriction of users on dashboards through permissions can be acquired and limiting users on the basis of row-level records using the row-level security feature can also be done.
12. Dashboards in sprinkle can be monitored very easily and the running of dashboards is also automated.
13. Downloading final data from the dashboard can be done with a single click and it supports downloading data in different formats like CSV, PDF, etc.

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CONS:

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1. Dashboards can be made more interactive.
2. Themes can be introduced in dashboards to make them more attractive.

2. Tableau:

Tableau is a data visualization tool that allows users to quickly see and understand data through different charts. It provides a variety of features, making it a popular choice for data analysts and business intelligence professionals. Tableau can help users to see trends, patterns, and outliers in data, and can be used to create highly interactive visualizations.

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PROS:

1. Tableau has a friendly mobile app that supports full functionality as seen in the desktop version.
2. High performance is delivered by tableau even on large data sets.
3. Tableau has a booming community of users from all over the world that helps to resolve user problems.

CONS:

1. The cost of the product is very high and the pricing plans are inflexible.
2. The after-sales support is flawed. The support team of tableau does not investigate and find the root cause of any problem faced by the customer, hence poor customer service is one of the biggest shortcomings of the product.
3. Downloading the final dataset from the dashboard is very time-consuming.
4. User interface is a little tricky and requires a skilled person to use it.
5. Huge datasets loaded in tableau often crash the system resulting in a loss of effort while developing the reports.

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3. Looker:

Looker is a business intelligence (BI) and data analytics platform that helps organizations transform data into insights. It allows users to explore, visualize, and analyze data offering a wide range of integrations, making it a versatile platform for data-driven organizations. It is a powerful tool that allows businesses to see the big picture and make informed decisions.

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PROS:

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1. The UI to create dashboards is simple and intuitive.
2. Collaboration with team members is very easy.
3. The support team of looker is very helpful and solves user queries very efficiently.
4. The drag-and-drop functionality in dashboards makes the platform user-friendly.

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CONS:

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1. The customization options for a graph are very less and can be improved.
2. The platform is not scalable and can be on the costlier side when dealing with small datasets. If your business generates less data then other data analytics tools should be preferred.
3. With high data or complex queries, CPU usage can increase making the platform extremely slow.
4. The charts have limited options and new chart creation according to business requirements takes time.

4. Excel:

Excel is a prevalent choice for data analysis because it is moderately easy to use and offers various features that make data analysis more superficial. Excel can filter data, calculate statistics, and create pivot tables. It also offers a variety of built-in charts and graphs that can be used to visualize data with utmost ease.

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PROS:

1. It can deal with large datasets with ease.
2. The dashboards are very interactive and useful in summarising data.
3. Custom-made templates are available for managing data easily in no time.
4. Microsoft Excel software is easy on your pocket and is considered to be budget-friendly.

CONS:

1. Tutorials or guides regarding the usability of the product are not present.
2. The visual interface of Excel is not visually engaging.
3. The Microsoft Excel application is very large and uses a huge amount of disk space and RAM.
4. The pivot tables on which a chart is plotted sometimes do not refresh automatically.

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5. Sisense:

If you're hunting for a tool that can help you make sense of complicated data sets, then Sisense is an excellent option. With its easy-to-use interface and powerful data visualization capabilities, Sisense can help you uncover insights that can be used to improve your business. It is a seamless and simplified data analysis tool that is used to analyze, investigate and collaborate with or without code.

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PROS:

1. The process of dashboard creation is easy compared with other data analytics tools. 
2. Customer support service is very efficient as they are well responsive and the onboarding process is also very straightforward. 
3. The reports support drill-downs up to many levels and are very interactive.
4. The dashboard in Sisense uses a drag-and-drop feature which is more user-friendly and intuitive to users as compared with other data analytics tools.

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CONS:

1. Maintenance of Dashboards is a little tricky as the user needs to republish it after every minor change.
2. Many pricing plans are available but Sisense still lacks a reliable plan and it is also a costly solution.
3. The graphing options available are not sufficient and better graphing tools can be incorporated.
4. The app size of Sisense is humongous so the system should be chosen accordingly to deliver optimal performance.

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6. GoodData:

Having a data analysis tool like Good Data is essential because the data analytics process can be time-consuming. Good Data makes it easy to collect data from various sources, clean it, and then analyze it using various techniques. The tool offers a wide range of data visualization features and a free trial so users can try out the tool before committing to it.

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PROS:

1. Setting up on GoodData is very simple and efficient.
2. The documentation for the tool is very detailed and comprehensive with plenty of native and user-created examples.
3. It is a cost-effective and easily scalable tool that caters needs of all businesses.
4. The sharing of dashboards with business users is simplified in GoodData.
5. Excellent chart options are provided with drill-down analysis for a deep understanding of data.

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CONS:

1. There are limited customization options for report/dashboard design 
2. Geographic Charts with more diverse options with enhanced functionality can be introduced.
3. For small-scale businesses, the pricing models can be on the costlier side as compared to other data analytics tools.
4. Real-time analytics is not possible in this tool which is one of its major shortcomings.

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7. Qlik

Qlik is a robust data analysis tool that enables users to effortlessly gain insights into their data. With Qlik, users can load data from multiple sources, explore and visualize it in various ways, and create interactive reports and dashboards. It also provides users with the ability to drill down into data to discover detailed information.

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PROS:

1. Dashboards can be easily created in Qlik to fulfill the analytics use cases.
2. Reports and dashboards can be viewed quickly and easily through the Qlik app.
3. Users can analyze data considering all factors and the final results can also be delivered in various file formats.
4. The reports can be customized as per the requirements with minimal effort.

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CONS:

1. The visualization options are limited in the dashboard.
2. There is a limitation in performing advanced analytics capabilities like complex statistical analytical calculation.
3. The interface can be more intuitive and user-friendly.
4. The tokens are on the pricey side, especially for small-scale industries.

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8. Domo Business Cloud:

Domo Business cloud is a low-code data app platform that allows its users to generate actionable insights from data. Domo offers a bunch of features, including data visualization, data analysis, and data sharing. It is a flexible and scalable platform that can be customized to meet the needs of any organization. It is a full self-service analytics platform that saves time and costs.

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PROS:

1. The dashboards in Domo are highly interactive and are relatively easy to manage as compared with other data analytics tools.
2. There are a couple of modules to learn about the basic programs and their functionalities in Domo.
3. Domo has the best mobile app in the market it provides a good mobile experience for the end users.
4. The end user experience is good and the platform has easy navigation to find dashboards.

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CONS:

1. It is difficult to understand the functionality to make more complex charts like gauges, in such cases detailed tutorial videos should be created.
2. For large datasets processing could be slow.
3. The summarization of data via a pivot table has very limited functionalities.
4. The pricing model is very costly and lacks flexibility.

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9. MicroStrategy Analytics:

MicroStrategy analytics is a powerful and easy-to-use enterprise business intelligence and mobility platform that helps organizations of all sizes make better decisions faster and drive revenue growth. It provides users with the ability to access and analyze data from multiple data sources, including relational databases, OLAP cubes, and flat files. It also offers vast features and functionality, including reporting, dashboarding, and data visualization.

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PROS:

1. It has centralized dashboards that are able to perform real-time data analysis
2. It can facilitate quick reporting because of the drag-and-drop functionality provided in dashboards
3. Segmenting a report by filters is really quick and accurate.
4. Multiple widgets are present in one dashboard view, which are created by customizing filters.

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CONS:

1. This platform proves to be inefficient when dealing with large datasets having millions of rows and encounters timeout issues while exporting data in excel format. 
2. The interface is not unified, it has two interfaces for some of the features.
3. A better user interfaces for the product can be developed.
4. The graph options are not as robust as compared with other data analysis tools.

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10. TIBCO Spotfire:

TIBCO Spotfire is a data visualization tool that helps users see and comprehend data in new ways as it provides AI-powered data insights. Spotfire allows users to create custom visualizations, dashboards, and reports to better understand their data. Spotfire also provides a way for users to collaborate on data projects and share their findings with others.

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PROS:

1. It is relatively easy to create highly customizable dashboards efficiently in Spotfire.
2. It is capable of performing real-time analytics with accuracy by providing AI-infused insights into data.
3. Its mobile device support is exceptional which gives anyone access with utmost ease.
4. Interactivity in visualizations helps to perform the in-depth analysis

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CONS:

1. Unlike other data analytics tools, python native visualizations are not supported in Spotfire, and integration with Jupyter Notebook is also absent.
2. Some charts in Spotfire have cluttered arrangements, some refinement in UI would be recommended.
3. Data loading speed for large amounts of data is very slow.
4. The data filter does not may many features and functionalities as provided by other data analytics tools.

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11. IBM Cognos Analytics:

IBM Cognos Analytics is a powerful, next-generation business intelligence platform that enables organizations to turn data into insights. It provides users with self-service capabilities to explore data, create and share dashboards and reports, and collaborate to make better decisions. IBM Cognos Analytics also offers enterprise-grade scalability, security, and governance to its users.

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PROS:

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1. AI-powered reporting helps to find accurate insights which enhance the business performance.
2. The pricing plans offered are affordable and highly scalable.
3. The printing of reports after creation is very easy and clear.
4. It helps users to share reports easily and has integration with different platforms.

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CONS:

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1. The number of visualizations present in the platform should be expanded.
2. The platform does not support multidimensional analysis.
3. The dashboards are very bland, the introduction of themes and colors can make the dashboards more attractive.
4. In case of any job failure, a clear error propagation is not there, so sometimes it gets tough to debug an error.

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12. Python:

Python is a universal language that can be used for data analytics. It has many libraries that can be used for data manipulation, analysis, and visualization. Python is also easy to learn, making it a good choice for those new to data analytics and data science.

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PROS:

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1. Python consists of several libraries like pandas, matplotlib, and seaborn to facilitate efficient in-depth data analysis with detailed information.
2. The documentation provided by python is extremely vast and detailed, that gives information about each and every function.
3. The graphs created using are highly customizable by code to fulfill the business needs.
4. The processing time for python scripts is very low and can efficiently work with heavy data volumes.

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CONS:

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1. The code that is used to create charts can be very complex to understand.
2. To perform data analysis in python a highly skilled data analyst would be required.
3. The number of functions and libraries in python is more in number which can be very overwhelming to a new user.

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13. RapidMiner:

RapidMiner is a data mining software that helps users to perform various data mining tasks such as regression, classification, and clustering using R and Python. The software is designed for data scientists and business analysts, and its user-friendly interface makes it easy for both groups with many reporting and dashboarding features available.

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PROS:

1. It helps to reduce the analyst's work as performing all types of transformations and calculations for data preparation is done with no code and helps to save up to 70% of data transformation time.
2. Product training is very easy as the product is very intuitive and easy to use, even the community of RapidMiner is very active.
3. For educational purposes, the platform is free with Educational License that lasts for a year.
4. As this platform makes use of machine learning so multiple predictive analytic models are present to cater to any need.

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CONS:

1. Dashboard and report creation is a challenging task.
2. The software is not efficient in terms of processing power as the CPU consumes a lot of processing power.
3. Fewer graph options are available to visualize data.
4. Integration capabilities with third-party applications can be smooth.

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14. SPLUNK:

Splunk is a valuable tool for any data-driven organization that wants to improve its decision-making and operations. It can help you monitor and analyze data from various sources, including system logs, application logs, and network traffic. Splunk can also be used to create custom dashboards and reports.

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PROS:

1. This data analytics tool has a live dashboard option that facilitates real time data analysis efficiently.
2. A user can easily create custom dashboards with ease and the alert monitoring feature is accurate.
3. The community is highly interactive and can solve your queries in minutes.
4. Multiple types of plugins to support specific applications are present and are very robust.

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CONS:

1. Understanding the product needs some good training as it is a little bit complex, hence it has a steep learning curve.
2. When a large volume of data is ingested the platform tends to become slow as compared to other data analytics tools.
3. The dashboard graphics are very bland and simple they can be improved.
4. The pricing plans are not as scalable as provided by other data analytics tools and sometimes can cause confusion in understanding the pricing model.

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15. ThoughtSpot:

The ThoughtSpot data analytics platform helps organizations make better decisions by providing instant access to data and insights. It combines the power of search and artificial intelligence to make data analytics easy for everyone. ThoughtSpot is the fastest way to get the insights you need to make better decisions for your business.

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PROS:

1. ThoughtSpot provides easy access to data facilitating ad-hoc analysis for quick reporting.
2. The platform provides many features like search and real-time notification delivery.
3. Embedding process for the dashboards for the end users is very smooth.
4. The content filter in the dashboard is accurate.

CONS:

1. Creating calculations to facilitate the business use cases can sometimes be inefficient and time-consuming.
2. The drill down specific to dashboards can be improved.
3. Additional color representations should be introduced in the charts to enhance the end-user experience.
4. Extensive training is required for new users to get acquainted with the platform as compared to other data analytics tools.

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16. Google Data Studio:

Google Data Studio is a free tool that allows you to create interactive dashboards and  data visualizations. It provides easy integration with data sources like Google applications like google ads, google sheets, etc. With easy collaboration and sharing features google data studio is the best free data analytics tool in the market.

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PROS:

1. The tool is free to use which makes it helpful for SMEs to reduce costs on analysis.
2. If your business generates its data from GA then it is one of the best data analytics tools in the market because of its seamless integration with GA.
3. Live data connection is one of the best and most powerful features of the platform.

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CONS:

1. It is a free tool so some additional costs for integration with third-party apps are charged.
2. If there are more users to whom you want to schedule the report then Google data studio should not be preferred as it offers a limited number of recipients for scheduled reporting.
3. Absence of easy training materials can cause problems, especially to first time users
4. On-premise deployment facility is not available as offered by other data analysis tools in the market.

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17. Grafana:

Grafana is an open-source data visualization and monitoring tool that allows users to query, visualize, and alert on metrics and logs no matter where they are stored. With Grafana, you can create beautiful dashboards and graphs to help you understand your data better.

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PROS:

1. If you deal with large volumes of data every day then it is the best analytics tool as it is capable of graphing lots of data.
2. The monitoring and alerting capabilities of the platform are very accurate and efficient. 
3. Free version of Grafana is also available.

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CONS:

1. In dashboards multiple graphs are present but currently it does not provide log analysis.
2. Some settings for the platform cannot be configured from UI
3. There is a steep learning curve when dealing with dashboards hence can cause problems and confusion to new users.
4. The user experience can be improved.

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18. Redash:

Redash is an open-source data exploration and visualization tool that makes it easy to query, visualize, and share data from any data source. Redash is designed to be easy to use, easy to set up, and easy to share that can help query your data in any language and visualize it to find the underlying patterns in your data.

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PROS:

1. The UI is very user-friendly helping users to create dashboards quickly.
2. Dashboard sharing with different users can be done easily with alert and monitoring capabilities.
3. Dashboard resizing allows you to easily resize the dashboard by dragging it according to one’s needs.
4. The setup instruction provided in the documentation is easy to understand.

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CONS:

1. The tool does not provide good customizing capabilities.
2. High technical knowledge of SQL is required when dealing with data ingestion.
3. Filters are not present while fetching data from different sources which increases the loading time.

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19. KNIME:

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KNIME is a free source software for analyzing data that offers a wide range of features for preprocessing, visualization, modeling, and deployment. KNIME is easy to use and scalable, making it a great choice for data scientists of all levels of experience. It also has a variety of visualization options that make it easy to understand data at all levels.

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PROS:

1. This platform has an open architecture and no license fee is required.
2. It is open-source software that has integration with python and R is a plus for users who are well acquainted with these languages.
3. The community is good and reliable that provides many feedbacks for the workflows.

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CONS:

1. The software takes up lots of space on the desktop which makes it sluggish and slow.
2. There are very few learning resources for users hence has a steep learning curve.
3. It is not an efficient platform when it comes to visualizations and hence it is preferred by data scientists.

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20. PowerBI

Power BI is a cloud-based business intelligence and analytics service that provides a rich set of features for data visualization, analysis, and collaboration. It helps organizations to gain insights from their data and make better decisions. Its easy-to-use self-service environment allows users to create their own reports and dashboards.

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PROS:

1. The visualizations provided in Power BI are diverse that contain dynamic charts of all types which are highly interactive.
2. It has the ability to combine and crush vast volumes of data efficiently.
3. With Power BI it is easy to publish and share data across team members for better collaboration.

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CONS:

1. The sharing of dashboards with different users is a tedious task.
2. The online version should facilitate editing of dashboards to make last-minute quick changes.
3. Pricing plans differ according to the tier models and cost improvement should be done for the premium tier.
4. Power BI desktop is not available for the MAC OS. So if your business operates on Apple machines other data analytics tools should be considered.

21. QuickSight:

QuickSight is a business intelligence service from Amazon. It is designed to make it easy for customers to visualize and analyze their data through a web-based interface that allows users to create, edit, and share dashboards easily. QuickSight can connect to data sources such as Amazon Redshift, Amazon Athena, Amazon RDS, and Amazon S3 and also supports integration with third-party data sources such as Salesforce, Google Analytics, and Twitter.

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PROS:

1. It is completely a cloud-based platform that requires no maintenance and hence helps users to keep their data up to date with utmost ease.
2. Dashboards can be easily created with Quicksight.
3. It is a highly scalable platform that charges users on the basis of platform usage only.
4. It has a very reliable and efficient integration with AWS services.

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CONS:

1. There are very limited options for chart types and does not even provide customization of the graphs.
2. The UI is not as friendly as it should be and is not intuitive to users.
3. Some reports in Quicksight update once in 24 hours, the introduction of real-time data analysis should be there.
4. Sharing reports with other users is a little tricky if the user is not added in the IAM.

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22. Datapine

DataPine is a business intelligence software that helps small and medium businesses make better decisions by turning data into insights. It is a cloud-based platform that offers users a simple drag-and-drop interface to create beautiful visualizations and dashboards from their data. With DataPine, users can connect to any data source, including Excel, CSV, and Google Sheets, and then explore and analyze their data with ease.

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PROS:

1. Within the analyze module the user interface has a drag-and-drop interface that makes the platform intuitive.
2. Dashboards and custom-made templates make reporting easy.
3. Customer support is very efficient and helpful.

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CONS:

1. Absence of tutorials and detailed documentation makes the learning curve very steep for new users.
2. To understand some cutting-edge product features an advanced level of SQL knowledge is required.
3. Due to various features provided by the platform the free trial duration of 14 days is very short.

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23. Klipfolio:

Klipfolio is a cloud-based data visualization and reporting tool that helps businesses track, analyze, and report on data from a variety of sources. Klipfolio gives users the ability to create customized dashboards and reports and provides real-time data updates and alerts. It integrates with a variety of data sources, including web analytics, social media, databases, and spreadsheets.

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PROS:

1. The documentation proved by Klipfolio is very useful for its components and data transformations.
2. Some custom template creation is facilitated by Klipfolio to users that can be used across multiple projects.
3. Customer support is very responsive and solves any problem in no time.
4. New features keep on adding from time to time basis making the data analysis process easier.

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CONS:

1. Lots of coding knowledge is required for users if they are working with Klipfolio.
2. The creation of graphs is a hassle as it may take multiple attempts to create a graph correctly.
3. The UI is good and intuitive but the organization of various components can be improved.
4. The dashboard sharing is password protected to enhance security but still the sharing process is very tough and clunky.

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24. ZOHO Analytics:

Zoho Analytics is a cloud-based business intelligence and data analytics software that enables users to create, collaborate and share data-driven insights. With Zoho Analytics, users can connect to multiple data sources, including Zoho Reports, Zoho Creator, Google Sheets, CSV files, and MySQL databases. It also comes with a built-in machine-learning engine that can be used to automatically identify patterns and correlations in data.

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PROS:

1. The product has many customization options to cater to different needs.
2. The user interface is simple and easy which helps people of all technological senses to use it appropriately.
3. Various type of graphs are present that helps to create diverse dashboards.

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CONS:

1. There are fewer tutorials when it comes to understanding the product.
2. The filtering drop-down functionality in dashboards is quite confusing.
3. The platform has a limitation on data refresh frequency which can be tough especially when you have live data coming in.

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25. Whatagraph:

Whatagraph is a cloud-based reporting marketing tool that enables businesses to automate the creation of stunning visual reports from their data, making it easy to track progress and identify areas for improvement. With Whatagraph, businesses can save time and resources by automating the reporting process and can make data-driven decisions to improve their marketing efforts.

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PROS:

1. It helps to save a lot of time and effort while creating reports for the marketing domain.
2. The easy-to-use interface helps in the creation of dashboards efficiently.
3. The platform also supports scheduled and automated reports.

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CONS:

1. The integration with different data sources is limited 
2. The price is on the costlier side, especially for SMEs and medium-sized enterprises.
3. The domain of reporting is restricted as it only deals with the marketing sector..

26. Query.me

Query.me is an analytics tool that gives you the ability to track, analyze, and compare your website's traffic data in real-time. With Query.me, you can see which pages are most popular, identify which keywords are driving the most traffic, and track your website's conversion rate. Query.me is an essential tool for any website owner who wants to optimize their website for better performance.

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PROS:

1. This platform helps to manage and analyze traffic generated on a website.
2. The creation of charts is very easy on the platform.

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CONS:

1. The visualization options are limited in the platform and cannot be used for in-depth analysis.
2. Dealing with different domains for visualization is limited.
3. Ingestion of data from various sources is limited.

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27. R

R is the most popular statistical modeling, visualization, and data analysis programming language. There are many different ways to analyze data in R, but some of the most popular methods are through the use of the dplyr and ggplot2 packages. These two packages allow users to easily manipulate and visualize data, respectively. Additionally, there are a variety of other packages that can be used for more specific types of data analysis.

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PROS:

1. The R language has a number of packages that helps to create quality plotting and graphing.
2. With packages like shiny and mark down attractive visualizations can be created.
3. Many statistical packages are available that are used to predict the pattern of data.
4. Detailed documentation is provided to users that have a description of every function.

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CONS:

1. R is a complicated language especially for new users to understand.
2. The R language is a little slower as compared to other languages like python.
3. A highly skilled person is required to create a dashboard in R language.

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28. Highcharts:

The Highcharts library comes with all the tools that allow users to see patterns and trends in their data. Built on JavaScript and TypeScript, all the charting libraries work with any back-end database. By using the various features of Highcharts, users can gain valuable insights into their data sets, and the ability to drill down into specific data points and see the underlying data allows users to see the relationships between data points. Overall, Highcharts is incredibly powerful for creating charts for data analytics.

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PROS:

1. It is an easy JavaScript library that can be used to create charts and graphs of different types.
2. Customisation of charts with color schemes is a plus point of the tool.
3. The community is very interactive and a proper solution is also provided to the users efficiently.
4. The documentation for the product is very helpful, especially for new users.

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CONS:

1. It is relatively tough and complex.
2. Calculating some specific values for the charts can be tricky.

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29. Orange:

Orange is an open-source data visualization and data analysis tool that uses machine learning with a large diverse toolbox. It is a powerful tool that can help organizations to improve their performance and optimize their operations. By leveraging the data that is already available, Orange can help organizations to identify trends, understand customer behavior, and make better decisions.

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PROS:

1. It is an open-source platform that can be useful for small-scale organizations to perform basic analysis.
2. The setup is easy for Orange and documentation is provided in case of any blockers faced during the analysis process.
3. Highly interactive Charts of different types are provided to users.

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CONS:

1. It is open-source software so special chart types on customers' requests cannot be created.
2. Sometimes the desktop on which Orange is installed seems to work slowly.
3. Users can perform basic analysis mostly can be used for only educational purposes.

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30. Adverity:

Adverity is a robust platform that enables businesses to collect, analyze and visualize data from multiple marketing channels. By centralizing data from multiple sources, Adverity provides businesses with a comprehensive view of their marketing performance and helps businesses to identify which marketing channels are most effective, track campaign performance over time, and optimize their marketing mix for maximum return on investment.

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PROS:

1. The easy-to-use interface is great for new users.
2. The dashboards are highly customizable that can be used to cater to any business need.

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CONS:

1. The technical documentation of the platform is not provided.
2. The error propagation is not very clear and can be really time-consuming
3. Monitoring of failed jobs capabilities is not available.
4. It is primarily used to perform data analysis specific to the marketing domain.

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Conclusion:

There are many things that can be done with data, such as predicting customer behavior and creating efficient marketing strategies. It's not just companies that benefit from having access to valuable data citizens, students, small businesses and anyone who needs to collect information can use data effectively too.

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If your business generates vast amounts of data every day from multiple sources, wants to make analyzing easier with interactive charts and dashboards, and to facilitate sharing those dashboards in the easiest way possible and in a cost-effective way then Sprinkle Data is the best option among all of the data analytics tools mentioned above. It offers a range of features to help businesses collect, store, and analyze their data effectively.

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