Skip to:
Share
Looking for the best BI solutions? You're in the right place.
We tested 17 BI tools to find the answer.
This list is 100% unbiased, and we haven’t received any payment from these companies.
So let's jump in!
BI solutions were created to help people make use of data and make meaningful decisions. At least, that was the idea.
If your team spends countless hours just trying to format your data in a meaningful and insightful way, you're wasting resources and labor costs.
As the company grows, data starts piling up. That's a good thing. Sales analytics can open up many opportunities. The more data, the better. The problem lies in what you do with this data.
Excel works well in the beginning. But as your data and customer base grow, you can't simply keep everything in Excel.
Initially, you might have a few customers, and doing math in your head is more than possible. Eventually, you adopt new tools and start testing different marketing and sales channels.
There comes a breaking point where you can't keep up with the volume of information. This is when most people start looking for BI solutions.
Not everyone knows how to code to extract insights or configure growth metrics, so the solution must be simple, user-friendly, and non-technical.
It should enable anyone to retrieve the information they need, regardless of their expertise level. After all, it's YOUR data. You own it and should be able to use it effectively.
Therefore, we decided to test every popular BI solution out there to determine which ones meet all these criteria and are worth your time and money.
But first things first…
What is Business Intelligence (BI)?
Business Intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions.
It simple words BI solution is a software that allows you to transform data into actionable intelligence that informs an organization's business decisions. You can connect your data to it (for example from your database) and then visualize it in the way so you can focus on things that will actually help the company meet the business goals.
Why Businesses Need BI Solutions
BI solutions because turn a jumble of data into clear insights that can guide decisions. Instead of guessing, companies can see exactly where to save money, where to invest, and if their strategies are working. BI makes sense of data fast, so businesses don't waste time or miss out on chances to grow. It's about making smart choices based on facts, not just feelings.
Data Visualization & Analytics: Due to visual analytics, BI tools simplify complex data sets, making it easier to perform revenue analytics, identify patterns, trends, and insights.
Efficient Data Management: It's not just about having data - it's about managing it efficiently. Proper BI setups prevent data loss as it moves across different systems and tools you use, ensuring consistency and accuracy.
Data-driven Decision Making: BI shifts the decision-making process from gut feeling to data-driven. This reduces the risks associated with instinct-based decisions and focuses on concrete, measurable metrics.
Our Experiment: Testing 17 BI Solutions
It took us a couple of weeks to first find the most usable and respected BI solutions. We’ve gone through Google searches, Reddit discussions, and Twitter feeds, to come up with a list of 17 credible BI tools.
Well, testing them wasn't straightforward at all. About half didn't offer free trials, so we had to schedule calls, explain our company's requirements, and undergo a sales qualification process before having a chance to play around and apply the solutions themselves.
Eventually, we compiled our findings in a spreadsheet. After a couple of brainstorm hours, we rated each tool on a scale of 1-10, based on four key criteria:
Price: Affordability and value for money.
Ease of Use: User-friendliness and ease of learning.
Flexibility: Customization options and adaptability to various use cases.
Security: Robustness of data protection and privacy features.
Probably the #1 thing that stood out to us was the fact that if a BI tool is well-established, it likely offers similar functionalities to PowerBI or Tableau, with like little to no innovation. Yes, there are some exceptions here and there. For instance, the transportation sector greatly benefits from Alteryx's advanced data science capabilities. But, by and large, many tools mirror the offerings of the big two.
Another thing is almost all of them require a lot of up-front time investment to set the things up properly. None of them are going to give you insights right out of the box. Also each of them requires you to learn how to actually use it which is another nuance to keep in mind when picking the tool.
Here’re some statistics we found for 2021 if you want to visually understand how the BI market works.
Top 3 Winners
Alright, time to talk the winners. There is going to be no surprises. It is what it is. PowerBI and Tableau rule this market and they are indeed the best compared to others. Don’t overthink this. If you need a reliable solution - these are your go to
Let’s dive deeper into why Tableau and PowerBI stand out, why community both likes them and hates them, and discuss their significant drawbacks, which are structurally challenging to overcome.
And I apologize if you were looking for a comprehensive review of each tool in this article. We might release some series of videos later, showing the interface, features and capabilities of each of these tools. Otherwise it would have taken me to write a whole book, not an article.
Closing our top 3 list is Datalynx – and not just because we're writing this article. We developed Datalynx in response to the inflexibilities and wonky user interfaces of current BI solutions. But more on that later. First, let's give credit to the tools that rightfully earn their spots.
Power BI
Let’s address the elephant in the room first. You've probably heard of PowerBI. If you haven't, it's the leading BI solution in the market, like Excel on steroids. Excel is great in calculations but falls short in visualizing large data sets. PowerBI is designed to make it easy to create charts, graphics, and analytical drill-downs. This visual approach helps in viewing large data sets differently to gain insights.
As an end-user, you'll find it helpful after its proper setup and implementation, which requires significant time investment from your engineers. However, if you use Microsoft for everything, PowerBI seamlessly integrates with existing Microsoft infrastructure, with zero to no connectivity issues and built-in high security.
You can also use PowerBI for free to test everything out. It's a nice bonus, as you don't have to commit before trying it.
Pros:
Dynamic Reporting: PowerBI lets you dynamically create sustainable reports, saving significant time by automating regular summaries.
Speeds up Decision Making: With daily trend visibility, it accelerates decision-making compared to monthly summaries.
Data Manipulation: PowerQuery, a UI-based ETL tool, allows you to extract and manipulate data directly in PowerBI.
Extensive Resources: Being widely recognized, PowerBI has heaps of resources, documentation, and online help.
Python and R Integration: This is highly beneficial for your engineering team during setup.
Office 365 Integration: PowerBI integrates smoothly with Office 365.
Cons:
Learning Curve: You need to learn DAX and M code to fully leverage PowerBI. These are programming languages used within PowerBI for data analysis and manipulation.
User Training Required: Even if you're not the one setting it up, learning to use the tool itself is necessary, as it's not entirely intuitive.
Limited Predictive Capabilities: It primarily answers "What?" question and locked in to descriptive intelligence, lacking storytelling or “Why?” analytics. Which is an issue of all BI solutions to be honest.
Limited User Input Capabilities: PowerBI is not ideal for tasks requiring extensive user input beyond basic filtering.
Not Workflow-Friendly: For tasks like list management or manual data additions, Excel might be a better choice.
Inconsistent for One-Off Reports: It's not the best choice for constantly changing or one-time reports.
Sharing and Permission Issues: You will most likely struggle with data sharing and permission levels along with complex pricing structures.
Why You Should or Should Not Use PowerBI
In summary, it does a really good job in creating consistent reports for basic to complex KPIs. It's ideal if you have the resources for development and maintenance, substantial data history, and time for team training. However, for tasks requiring extensive user interaction, one-off reports, or if you're not ready to tackle its learning curve, you might look somewhere else. But again these are general BI limitations and not something PowerBI specific.
Tableau
Tableau stands out as probably the best visualization software available. It's fantastic for creating advanced visuals, offering fast access to data insights. Once you're familiar with your data and understand Tableau's functionality, you can swiftly create on-the-fly complex dashboards.
However, the challenge emerges for beginners, especially those who aren't very technical. Despite a good setup, drawing new charts or extracting new information isn't straightforward. The steep learning curve is rewarding for those who persevere but daunting for newcomers.
Like other BI tools, Tableau allows you to connect your data and build any chart using metrics and dimensions. Its edge lies in customization and flexibility, especially when combined with Python and R. This blend enables Tableau to produce far more powerful visuals than PowerBI or other tools.
Data analysts particularly love Tableau. While PowerBI is the business world's preference, Tableau is the go-to for a broader range of needs like study, business, research, etc.
Pros
Stunning Visualizations: Tableau offers the best in the business for creating visually impressive reports.
Large Data Handling: It manages datasets with millions of records far better than its competitors.
Collaboration Tools: With Tableau Server or Cloud, you can have published datasets used by multiple users, creating a "source of truth" in a non-SQL environment.
Python & R Integration: Offers extensive integration capabilities, including with other web services.
Cons
Steep Learning Curve: Mastering Tableau takes significant time and effort.
Complex Workarounds: Often requires complicated methods to perform basic tasks.
Server Management and Cost: Updates, server management, and overall costs can be burdensome.
Expensive Licensing: High cost can be a barrier for many organizations.
Time-Intensive Administration: Site administration can be laborious.
Performance Issues: Storing detailed data can lead to storage issues, server congestion, and long loading and query times.
Misleading “No Code” Solution: The simplicity of its interface can be deceiving, as understanding the data is still complex and essential.
The biggest drawback is that most stakeholders don't need the level of detail Tableau offers. They typically prefer standard charts and occasional ad-hoc reports, often exported to Excel for further manipulation. Tableau's design for slicing granular data, while flexible, often proves unnecessary and counterproductive.
Why you should or should not use it
In short, Tableau is a powerful tool that requires time and resources to master. If you have the time and budget, and your work environment doesn’t live in Office 365 - it might be worth the investment. It's ideal for detailed data analysis and creating high-quality visuals. But make sure to consider the steep learning curve, the necessity of technical understanding, and the potential overkill for simpler data representation needs.
The Big BI Infrastructure Problem in 2023
BI's original mission was to empower non-technical users to perform tasks that historically required technical knowledge. In a nutshell, any BI solution is an interface layer between users and coding languages like SQL and Python.
Early vendors such as SAP, Siebel, and JD Edwards introduced this BI concept in the 1970s. Excel and later PowerBI adopted and enhanced this technology. Users take a dataset, transform it, and plot it on a chart, all through an intuitive interface.
But to make these tools really usable, companies had to add many new features, resulting in versions cluttered with complex functionalities. This wasn't by design but a structural challenge in moving away from this model.
Many tried to overcome these limitations using different approaches, but they've largely been unsuccessful. Coding remains the most effective tool for data analysis. Those without coding skills still struggle a lot, even though these tools are better than having no tools at all.
Then came Generative AI in November 2022. Despite some unjustified hype, AI is really good in one area: processing language. It can act as a bridge between various entities, clarifying concepts in math, English grammar, SQL, Python, or chart trends.
This opens up the possibility of using AI to intuitively and user-friendly craft code in plain English, enabling users to achieve results without navigating complex BI interfaces. We talked about some other applications of Gen AI in Sales in this article.
In November 2022, we knew that this was what we want to build. It was also the right time to address PowerBI and Tableau's limitations, such as mapping, pricing, and lack of diagnostic intelligence. These tools don't explain business trends or predict upcoming challenges. Filling these gaps is now our commitment.
Meet Datalynx - A new BI Infrastructure
What if you could talk to your data? Imagine not having to configure measures and dimensions in Tableau. Instead, you could simply tell Tableau what you need, and it would deliver. What if you could build reports block by block using just… words? That’s exactly what Datalynx offers.
When taking notes, some people prefer paper and pen, while others type on a laptop. This preference also applies to Business Intelligence. Previously, when you needed to uncover data stories and identify patterns to inform your ideas and strategies, programming and BI tools were your only options. But now, things have changed. You no longer need to rely solely on traditional methods. You still can, but it's no longer a necessity.
We've streamlined the entire BI configuration process into a single chat interface. No more hacky methods to achieve your goals – simply tell the AI what you need, and it will do it for you. Plus, you can tweak and adjust as much as you want.
Why you should or should not use it
Think of working with data like building with LEGO pieces. If you want the freedom to build a castle with unlimited LEGO pieces, then Datalynx is the tool for you.
But if you're comfortable with a pre-existing LEGO pack with a defined set of pieces, traditional BI tools might suffice. They provide structured, but limited, ways of exploring data.
Datalynx isn't a replacement for other BI solutions. Rather, it's a different toolbox, offering a more effective and user-friendly approach for those committed to staying data-driven.Our goal is to enhance the data experience, making it accessible and engaging for everybody in the company, without the constraints of conventional BI tools.
Pros
Streamlined Data Analysis: Receive accurate answers to complex data queries in minutes. The AI understands your questions and delves into the data, providing insights like a conversation with an expert.
Centralized Data Management: Integrates seamlessly with your entire tech ecosystem, including CRMs, databases, product analytics, marketing platforms, and other tools.
Diagnostic & Predictive Analytics: Uncover answers to "why" questions even before your team does. Datalynx also considers external factors, like news events, impacting your business for a comprehensive performance analysis.
Flexible Data Construction: Construct tailored data analysis like building with Lego blocks, catering to specific needs.
Story-Driven Data Presentation: Data comes with narratives, making it easy for your team to understand their implications without additional analysis.
Pre-Curated Analysis Options: Datalynx analyzes your company data and suggests a range of analyses, removing the guesswork of starting from scratch. Now, anyone on your team can access actionable insights, no data expertise needed.
Cons
Limited Visualization Capabilities: Results include charts, but they don't compare with what Tableau offers. This might change in the future, but it's not a feature as of now.
No Python or R Integration: While Datalynx can write R or Python scripts, it doesn't support custom coding. About 90% of tasks can be accomplished with AI assistance, but custom report coding without AI isn't possible.
No ETL (Extract, Transform, Load): If your database is disorganized and requires significant transformation before analysis, the AI might struggle to understnad your structure. This leads to extensive mapping to explain column and table meanings, which is not ideal.
Harnessing Data for Competitive Advantage
As data volumes from various sources like ERP systems and APIs continue expanding, modern BI platforms leverage machine learning and artificial intelligence to help business users integrate and prepare these raw data sources for analysis.
The BI tools above stand out for the numerous options they provide across desktop, mobile, and email to create customizable alerts, graphs, and reports tailored to the evolving business questions across sales, customer behavior, supply chain analytics and more.
By using automation, algorithms and natural language functionality to surface outliers and inefficiencies, great BI software gives people throughout an organization — not just analysts and IT departments — the ability to unlock insights and gain a competitive advantage.
Industry leaders turn to the key features in top-rated BI platforms like data preparation, big data analytics, and data visualization to get more value from growing data volumes. The various sources feeding these modern solutions with raw data include Oracle, Salesforce, cloud apps, on-premise databases and more, which are then integrated via APIs into centralized business intelligence platforms.
According to Gartner’s latest evaluation of BI vendors, options like Microsoft Power BI exemplify best practices that enable almost any business user to leverage data and new technologies in meeting needs, finding answers to their questions, and propelling their organization to the next level with insightful intelligence.
Conclusion
We've done a lot of analysis for you to better understand what the BI market has to offer and what to expect. Hopefully, it was helpful. As you can see, there's no one-size-fits-all solution. It really depends on your goals, your team's technical skills, and the specific challenges you're trying to tackle.
PowerBI and Tableau, with their depth and versatility, are great for those ready to dive into the more technical aspects of data analysis. They're like the heavy-duty tools in your BI toolbox, ideal for detailed, customizable insights.
But remember, complexity isn't always necessary. That's where something like Datalynx comes in. It's for those who want to keep things simple and intuitive, offering a more conversational approach to data analysis.
At the end of the day, choosing the right BI tool is about finding that sweet spot where the tool's functionalities align perfectly with your business objectives and data strategy. The goal is to make your data work for you, driving informed decisions and strategic growth. So, assess your needs, weigh your options, and choose the tool that will turn the data into your most valuable asset.
Looking for the best BI solutions? You're in the right place.
We tested 17 BI tools to find the answer.
This list is 100% unbiased, and we haven’t received any payment from these companies.
So let's jump in!
BI solutions were created to help people make use of data and make meaningful decisions. At least, that was the idea.
If your team spends countless hours just trying to format your data in a meaningful and insightful way, you're wasting resources and labor costs.
As the company grows, data starts piling up. That's a good thing. Sales analytics can open up many opportunities. The more data, the better. The problem lies in what you do with this data.
Excel works well in the beginning. But as your data and customer base grow, you can't simply keep everything in Excel.
Initially, you might have a few customers, and doing math in your head is more than possible. Eventually, you adopt new tools and start testing different marketing and sales channels.
There comes a breaking point where you can't keep up with the volume of information. This is when most people start looking for BI solutions.
Not everyone knows how to code to extract insights or configure growth metrics, so the solution must be simple, user-friendly, and non-technical.
It should enable anyone to retrieve the information they need, regardless of their expertise level. After all, it's YOUR data. You own it and should be able to use it effectively.
Therefore, we decided to test every popular BI solution out there to determine which ones meet all these criteria and are worth your time and money.
But first things first…
What is Business Intelligence (BI)?
Business Intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions.
It simple words BI solution is a software that allows you to transform data into actionable intelligence that informs an organization's business decisions. You can connect your data to it (for example from your database) and then visualize it in the way so you can focus on things that will actually help the company meet the business goals.
Why Businesses Need BI Solutions
BI solutions because turn a jumble of data into clear insights that can guide decisions. Instead of guessing, companies can see exactly where to save money, where to invest, and if their strategies are working. BI makes sense of data fast, so businesses don't waste time or miss out on chances to grow. It's about making smart choices based on facts, not just feelings.
Data Visualization & Analytics: Due to visual analytics, BI tools simplify complex data sets, making it easier to perform revenue analytics, identify patterns, trends, and insights.
Efficient Data Management: It's not just about having data - it's about managing it efficiently. Proper BI setups prevent data loss as it moves across different systems and tools you use, ensuring consistency and accuracy.
Data-driven Decision Making: BI shifts the decision-making process from gut feeling to data-driven. This reduces the risks associated with instinct-based decisions and focuses on concrete, measurable metrics.
Our Experiment: Testing 17 BI Solutions
It took us a couple of weeks to first find the most usable and respected BI solutions. We’ve gone through Google searches, Reddit discussions, and Twitter feeds, to come up with a list of 17 credible BI tools.
Well, testing them wasn't straightforward at all. About half didn't offer free trials, so we had to schedule calls, explain our company's requirements, and undergo a sales qualification process before having a chance to play around and apply the solutions themselves.
Eventually, we compiled our findings in a spreadsheet. After a couple of brainstorm hours, we rated each tool on a scale of 1-10, based on four key criteria:
Price: Affordability and value for money.
Ease of Use: User-friendliness and ease of learning.
Flexibility: Customization options and adaptability to various use cases.
Security: Robustness of data protection and privacy features.
Probably the #1 thing that stood out to us was the fact that if a BI tool is well-established, it likely offers similar functionalities to PowerBI or Tableau, with like little to no innovation. Yes, there are some exceptions here and there. For instance, the transportation sector greatly benefits from Alteryx's advanced data science capabilities. But, by and large, many tools mirror the offerings of the big two.
Another thing is almost all of them require a lot of up-front time investment to set the things up properly. None of them are going to give you insights right out of the box. Also each of them requires you to learn how to actually use it which is another nuance to keep in mind when picking the tool.
Here’re some statistics we found for 2021 if you want to visually understand how the BI market works.
Top 3 Winners
Alright, time to talk the winners. There is going to be no surprises. It is what it is. PowerBI and Tableau rule this market and they are indeed the best compared to others. Don’t overthink this. If you need a reliable solution - these are your go to
Let’s dive deeper into why Tableau and PowerBI stand out, why community both likes them and hates them, and discuss their significant drawbacks, which are structurally challenging to overcome.
And I apologize if you were looking for a comprehensive review of each tool in this article. We might release some series of videos later, showing the interface, features and capabilities of each of these tools. Otherwise it would have taken me to write a whole book, not an article.
Closing our top 3 list is Datalynx – and not just because we're writing this article. We developed Datalynx in response to the inflexibilities and wonky user interfaces of current BI solutions. But more on that later. First, let's give credit to the tools that rightfully earn their spots.
Power BI
Let’s address the elephant in the room first. You've probably heard of PowerBI. If you haven't, it's the leading BI solution in the market, like Excel on steroids. Excel is great in calculations but falls short in visualizing large data sets. PowerBI is designed to make it easy to create charts, graphics, and analytical drill-downs. This visual approach helps in viewing large data sets differently to gain insights.
As an end-user, you'll find it helpful after its proper setup and implementation, which requires significant time investment from your engineers. However, if you use Microsoft for everything, PowerBI seamlessly integrates with existing Microsoft infrastructure, with zero to no connectivity issues and built-in high security.
You can also use PowerBI for free to test everything out. It's a nice bonus, as you don't have to commit before trying it.
Pros:
Dynamic Reporting: PowerBI lets you dynamically create sustainable reports, saving significant time by automating regular summaries.
Speeds up Decision Making: With daily trend visibility, it accelerates decision-making compared to monthly summaries.
Data Manipulation: PowerQuery, a UI-based ETL tool, allows you to extract and manipulate data directly in PowerBI.
Extensive Resources: Being widely recognized, PowerBI has heaps of resources, documentation, and online help.
Python and R Integration: This is highly beneficial for your engineering team during setup.
Office 365 Integration: PowerBI integrates smoothly with Office 365.
Cons:
Learning Curve: You need to learn DAX and M code to fully leverage PowerBI. These are programming languages used within PowerBI for data analysis and manipulation.
User Training Required: Even if you're not the one setting it up, learning to use the tool itself is necessary, as it's not entirely intuitive.
Limited Predictive Capabilities: It primarily answers "What?" question and locked in to descriptive intelligence, lacking storytelling or “Why?” analytics. Which is an issue of all BI solutions to be honest.
Limited User Input Capabilities: PowerBI is not ideal for tasks requiring extensive user input beyond basic filtering.
Not Workflow-Friendly: For tasks like list management or manual data additions, Excel might be a better choice.
Inconsistent for One-Off Reports: It's not the best choice for constantly changing or one-time reports.
Sharing and Permission Issues: You will most likely struggle with data sharing and permission levels along with complex pricing structures.
Why You Should or Should Not Use PowerBI
In summary, it does a really good job in creating consistent reports for basic to complex KPIs. It's ideal if you have the resources for development and maintenance, substantial data history, and time for team training. However, for tasks requiring extensive user interaction, one-off reports, or if you're not ready to tackle its learning curve, you might look somewhere else. But again these are general BI limitations and not something PowerBI specific.
Tableau
Tableau stands out as probably the best visualization software available. It's fantastic for creating advanced visuals, offering fast access to data insights. Once you're familiar with your data and understand Tableau's functionality, you can swiftly create on-the-fly complex dashboards.
However, the challenge emerges for beginners, especially those who aren't very technical. Despite a good setup, drawing new charts or extracting new information isn't straightforward. The steep learning curve is rewarding for those who persevere but daunting for newcomers.
Like other BI tools, Tableau allows you to connect your data and build any chart using metrics and dimensions. Its edge lies in customization and flexibility, especially when combined with Python and R. This blend enables Tableau to produce far more powerful visuals than PowerBI or other tools.
Data analysts particularly love Tableau. While PowerBI is the business world's preference, Tableau is the go-to for a broader range of needs like study, business, research, etc.
Pros
Stunning Visualizations: Tableau offers the best in the business for creating visually impressive reports.
Large Data Handling: It manages datasets with millions of records far better than its competitors.
Collaboration Tools: With Tableau Server or Cloud, you can have published datasets used by multiple users, creating a "source of truth" in a non-SQL environment.
Python & R Integration: Offers extensive integration capabilities, including with other web services.
Cons
Steep Learning Curve: Mastering Tableau takes significant time and effort.
Complex Workarounds: Often requires complicated methods to perform basic tasks.
Server Management and Cost: Updates, server management, and overall costs can be burdensome.
Expensive Licensing: High cost can be a barrier for many organizations.
Time-Intensive Administration: Site administration can be laborious.
Performance Issues: Storing detailed data can lead to storage issues, server congestion, and long loading and query times.
Misleading “No Code” Solution: The simplicity of its interface can be deceiving, as understanding the data is still complex and essential.
The biggest drawback is that most stakeholders don't need the level of detail Tableau offers. They typically prefer standard charts and occasional ad-hoc reports, often exported to Excel for further manipulation. Tableau's design for slicing granular data, while flexible, often proves unnecessary and counterproductive.
Why you should or should not use it
In short, Tableau is a powerful tool that requires time and resources to master. If you have the time and budget, and your work environment doesn’t live in Office 365 - it might be worth the investment. It's ideal for detailed data analysis and creating high-quality visuals. But make sure to consider the steep learning curve, the necessity of technical understanding, and the potential overkill for simpler data representation needs.
The Big BI Infrastructure Problem in 2023
BI's original mission was to empower non-technical users to perform tasks that historically required technical knowledge. In a nutshell, any BI solution is an interface layer between users and coding languages like SQL and Python.
Early vendors such as SAP, Siebel, and JD Edwards introduced this BI concept in the 1970s. Excel and later PowerBI adopted and enhanced this technology. Users take a dataset, transform it, and plot it on a chart, all through an intuitive interface.
But to make these tools really usable, companies had to add many new features, resulting in versions cluttered with complex functionalities. This wasn't by design but a structural challenge in moving away from this model.
Many tried to overcome these limitations using different approaches, but they've largely been unsuccessful. Coding remains the most effective tool for data analysis. Those without coding skills still struggle a lot, even though these tools are better than having no tools at all.
Then came Generative AI in November 2022. Despite some unjustified hype, AI is really good in one area: processing language. It can act as a bridge between various entities, clarifying concepts in math, English grammar, SQL, Python, or chart trends.
This opens up the possibility of using AI to intuitively and user-friendly craft code in plain English, enabling users to achieve results without navigating complex BI interfaces. We talked about some other applications of Gen AI in Sales in this article.
In November 2022, we knew that this was what we want to build. It was also the right time to address PowerBI and Tableau's limitations, such as mapping, pricing, and lack of diagnostic intelligence. These tools don't explain business trends or predict upcoming challenges. Filling these gaps is now our commitment.
Meet Datalynx - A new BI Infrastructure
What if you could talk to your data? Imagine not having to configure measures and dimensions in Tableau. Instead, you could simply tell Tableau what you need, and it would deliver. What if you could build reports block by block using just… words? That’s exactly what Datalynx offers.
When taking notes, some people prefer paper and pen, while others type on a laptop. This preference also applies to Business Intelligence. Previously, when you needed to uncover data stories and identify patterns to inform your ideas and strategies, programming and BI tools were your only options. But now, things have changed. You no longer need to rely solely on traditional methods. You still can, but it's no longer a necessity.
We've streamlined the entire BI configuration process into a single chat interface. No more hacky methods to achieve your goals – simply tell the AI what you need, and it will do it for you. Plus, you can tweak and adjust as much as you want.
Why you should or should not use it
Think of working with data like building with LEGO pieces. If you want the freedom to build a castle with unlimited LEGO pieces, then Datalynx is the tool for you.
But if you're comfortable with a pre-existing LEGO pack with a defined set of pieces, traditional BI tools might suffice. They provide structured, but limited, ways of exploring data.
Datalynx isn't a replacement for other BI solutions. Rather, it's a different toolbox, offering a more effective and user-friendly approach for those committed to staying data-driven.Our goal is to enhance the data experience, making it accessible and engaging for everybody in the company, without the constraints of conventional BI tools.
Pros
Streamlined Data Analysis: Receive accurate answers to complex data queries in minutes. The AI understands your questions and delves into the data, providing insights like a conversation with an expert.
Centralized Data Management: Integrates seamlessly with your entire tech ecosystem, including CRMs, databases, product analytics, marketing platforms, and other tools.
Diagnostic & Predictive Analytics: Uncover answers to "why" questions even before your team does. Datalynx also considers external factors, like news events, impacting your business for a comprehensive performance analysis.
Flexible Data Construction: Construct tailored data analysis like building with Lego blocks, catering to specific needs.
Story-Driven Data Presentation: Data comes with narratives, making it easy for your team to understand their implications without additional analysis.
Pre-Curated Analysis Options: Datalynx analyzes your company data and suggests a range of analyses, removing the guesswork of starting from scratch. Now, anyone on your team can access actionable insights, no data expertise needed.
Cons
Limited Visualization Capabilities: Results include charts, but they don't compare with what Tableau offers. This might change in the future, but it's not a feature as of now.
No Python or R Integration: While Datalynx can write R or Python scripts, it doesn't support custom coding. About 90% of tasks can be accomplished with AI assistance, but custom report coding without AI isn't possible.
No ETL (Extract, Transform, Load): If your database is disorganized and requires significant transformation before analysis, the AI might struggle to understnad your structure. This leads to extensive mapping to explain column and table meanings, which is not ideal.
Harnessing Data for Competitive Advantage
As data volumes from various sources like ERP systems and APIs continue expanding, modern BI platforms leverage machine learning and artificial intelligence to help business users integrate and prepare these raw data sources for analysis.
The BI tools above stand out for the numerous options they provide across desktop, mobile, and email to create customizable alerts, graphs, and reports tailored to the evolving business questions across sales, customer behavior, supply chain analytics and more.
By using automation, algorithms and natural language functionality to surface outliers and inefficiencies, great BI software gives people throughout an organization — not just analysts and IT departments — the ability to unlock insights and gain a competitive advantage.
Industry leaders turn to the key features in top-rated BI platforms like data preparation, big data analytics, and data visualization to get more value from growing data volumes. The various sources feeding these modern solutions with raw data include Oracle, Salesforce, cloud apps, on-premise databases and more, which are then integrated via APIs into centralized business intelligence platforms.
According to Gartner’s latest evaluation of BI vendors, options like Microsoft Power BI exemplify best practices that enable almost any business user to leverage data and new technologies in meeting needs, finding answers to their questions, and propelling their organization to the next level with insightful intelligence.
Conclusion
We've done a lot of analysis for you to better understand what the BI market has to offer and what to expect. Hopefully, it was helpful. As you can see, there's no one-size-fits-all solution. It really depends on your goals, your team's technical skills, and the specific challenges you're trying to tackle.
PowerBI and Tableau, with their depth and versatility, are great for those ready to dive into the more technical aspects of data analysis. They're like the heavy-duty tools in your BI toolbox, ideal for detailed, customizable insights.
But remember, complexity isn't always necessary. That's where something like Datalynx comes in. It's for those who want to keep things simple and intuitive, offering a more conversational approach to data analysis.
At the end of the day, choosing the right BI tool is about finding that sweet spot where the tool's functionalities align perfectly with your business objectives and data strategy. The goal is to make your data work for you, driving informed decisions and strategic growth. So, assess your needs, weigh your options, and choose the tool that will turn the data into your most valuable asset.
Looking for the best BI solutions? You're in the right place.
We tested 17 BI tools to find the answer.
This list is 100% unbiased, and we haven’t received any payment from these companies.
So let's jump in!
BI solutions were created to help people make use of data and make meaningful decisions. At least, that was the idea.
If your team spends countless hours just trying to format your data in a meaningful and insightful way, you're wasting resources and labor costs.
As the company grows, data starts piling up. That's a good thing. Sales analytics can open up many opportunities. The more data, the better. The problem lies in what you do with this data.
Excel works well in the beginning. But as your data and customer base grow, you can't simply keep everything in Excel.
Initially, you might have a few customers, and doing math in your head is more than possible. Eventually, you adopt new tools and start testing different marketing and sales channels.
There comes a breaking point where you can't keep up with the volume of information. This is when most people start looking for BI solutions.
Not everyone knows how to code to extract insights or configure growth metrics, so the solution must be simple, user-friendly, and non-technical.
It should enable anyone to retrieve the information they need, regardless of their expertise level. After all, it's YOUR data. You own it and should be able to use it effectively.
Therefore, we decided to test every popular BI solution out there to determine which ones meet all these criteria and are worth your time and money.
But first things first…
What is Business Intelligence (BI)?
Business Intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions.
It simple words BI solution is a software that allows you to transform data into actionable intelligence that informs an organization's business decisions. You can connect your data to it (for example from your database) and then visualize it in the way so you can focus on things that will actually help the company meet the business goals.
Why Businesses Need BI Solutions
BI solutions because turn a jumble of data into clear insights that can guide decisions. Instead of guessing, companies can see exactly where to save money, where to invest, and if their strategies are working. BI makes sense of data fast, so businesses don't waste time or miss out on chances to grow. It's about making smart choices based on facts, not just feelings.
Data Visualization & Analytics: Due to visual analytics, BI tools simplify complex data sets, making it easier to perform revenue analytics, identify patterns, trends, and insights.
Efficient Data Management: It's not just about having data - it's about managing it efficiently. Proper BI setups prevent data loss as it moves across different systems and tools you use, ensuring consistency and accuracy.
Data-driven Decision Making: BI shifts the decision-making process from gut feeling to data-driven. This reduces the risks associated with instinct-based decisions and focuses on concrete, measurable metrics.
Our Experiment: Testing 17 BI Solutions
It took us a couple of weeks to first find the most usable and respected BI solutions. We’ve gone through Google searches, Reddit discussions, and Twitter feeds, to come up with a list of 17 credible BI tools.
Well, testing them wasn't straightforward at all. About half didn't offer free trials, so we had to schedule calls, explain our company's requirements, and undergo a sales qualification process before having a chance to play around and apply the solutions themselves.
Eventually, we compiled our findings in a spreadsheet. After a couple of brainstorm hours, we rated each tool on a scale of 1-10, based on four key criteria:
Price: Affordability and value for money.
Ease of Use: User-friendliness and ease of learning.
Flexibility: Customization options and adaptability to various use cases.
Security: Robustness of data protection and privacy features.
Probably the #1 thing that stood out to us was the fact that if a BI tool is well-established, it likely offers similar functionalities to PowerBI or Tableau, with like little to no innovation. Yes, there are some exceptions here and there. For instance, the transportation sector greatly benefits from Alteryx's advanced data science capabilities. But, by and large, many tools mirror the offerings of the big two.
Another thing is almost all of them require a lot of up-front time investment to set the things up properly. None of them are going to give you insights right out of the box. Also each of them requires you to learn how to actually use it which is another nuance to keep in mind when picking the tool.
Here’re some statistics we found for 2021 if you want to visually understand how the BI market works.
Top 3 Winners
Alright, time to talk the winners. There is going to be no surprises. It is what it is. PowerBI and Tableau rule this market and they are indeed the best compared to others. Don’t overthink this. If you need a reliable solution - these are your go to
Let’s dive deeper into why Tableau and PowerBI stand out, why community both likes them and hates them, and discuss their significant drawbacks, which are structurally challenging to overcome.
And I apologize if you were looking for a comprehensive review of each tool in this article. We might release some series of videos later, showing the interface, features and capabilities of each of these tools. Otherwise it would have taken me to write a whole book, not an article.
Closing our top 3 list is Datalynx – and not just because we're writing this article. We developed Datalynx in response to the inflexibilities and wonky user interfaces of current BI solutions. But more on that later. First, let's give credit to the tools that rightfully earn their spots.
Power BI
Let’s address the elephant in the room first. You've probably heard of PowerBI. If you haven't, it's the leading BI solution in the market, like Excel on steroids. Excel is great in calculations but falls short in visualizing large data sets. PowerBI is designed to make it easy to create charts, graphics, and analytical drill-downs. This visual approach helps in viewing large data sets differently to gain insights.
As an end-user, you'll find it helpful after its proper setup and implementation, which requires significant time investment from your engineers. However, if you use Microsoft for everything, PowerBI seamlessly integrates with existing Microsoft infrastructure, with zero to no connectivity issues and built-in high security.
You can also use PowerBI for free to test everything out. It's a nice bonus, as you don't have to commit before trying it.
Pros:
Dynamic Reporting: PowerBI lets you dynamically create sustainable reports, saving significant time by automating regular summaries.
Speeds up Decision Making: With daily trend visibility, it accelerates decision-making compared to monthly summaries.
Data Manipulation: PowerQuery, a UI-based ETL tool, allows you to extract and manipulate data directly in PowerBI.
Extensive Resources: Being widely recognized, PowerBI has heaps of resources, documentation, and online help.
Python and R Integration: This is highly beneficial for your engineering team during setup.
Office 365 Integration: PowerBI integrates smoothly with Office 365.
Cons:
Learning Curve: You need to learn DAX and M code to fully leverage PowerBI. These are programming languages used within PowerBI for data analysis and manipulation.
User Training Required: Even if you're not the one setting it up, learning to use the tool itself is necessary, as it's not entirely intuitive.
Limited Predictive Capabilities: It primarily answers "What?" question and locked in to descriptive intelligence, lacking storytelling or “Why?” analytics. Which is an issue of all BI solutions to be honest.
Limited User Input Capabilities: PowerBI is not ideal for tasks requiring extensive user input beyond basic filtering.
Not Workflow-Friendly: For tasks like list management or manual data additions, Excel might be a better choice.
Inconsistent for One-Off Reports: It's not the best choice for constantly changing or one-time reports.
Sharing and Permission Issues: You will most likely struggle with data sharing and permission levels along with complex pricing structures.
Why You Should or Should Not Use PowerBI
In summary, it does a really good job in creating consistent reports for basic to complex KPIs. It's ideal if you have the resources for development and maintenance, substantial data history, and time for team training. However, for tasks requiring extensive user interaction, one-off reports, or if you're not ready to tackle its learning curve, you might look somewhere else. But again these are general BI limitations and not something PowerBI specific.
Tableau
Tableau stands out as probably the best visualization software available. It's fantastic for creating advanced visuals, offering fast access to data insights. Once you're familiar with your data and understand Tableau's functionality, you can swiftly create on-the-fly complex dashboards.
However, the challenge emerges for beginners, especially those who aren't very technical. Despite a good setup, drawing new charts or extracting new information isn't straightforward. The steep learning curve is rewarding for those who persevere but daunting for newcomers.
Like other BI tools, Tableau allows you to connect your data and build any chart using metrics and dimensions. Its edge lies in customization and flexibility, especially when combined with Python and R. This blend enables Tableau to produce far more powerful visuals than PowerBI or other tools.
Data analysts particularly love Tableau. While PowerBI is the business world's preference, Tableau is the go-to for a broader range of needs like study, business, research, etc.
Pros
Stunning Visualizations: Tableau offers the best in the business for creating visually impressive reports.
Large Data Handling: It manages datasets with millions of records far better than its competitors.
Collaboration Tools: With Tableau Server or Cloud, you can have published datasets used by multiple users, creating a "source of truth" in a non-SQL environment.
Python & R Integration: Offers extensive integration capabilities, including with other web services.
Cons
Steep Learning Curve: Mastering Tableau takes significant time and effort.
Complex Workarounds: Often requires complicated methods to perform basic tasks.
Server Management and Cost: Updates, server management, and overall costs can be burdensome.
Expensive Licensing: High cost can be a barrier for many organizations.
Time-Intensive Administration: Site administration can be laborious.
Performance Issues: Storing detailed data can lead to storage issues, server congestion, and long loading and query times.
Misleading “No Code” Solution: The simplicity of its interface can be deceiving, as understanding the data is still complex and essential.
The biggest drawback is that most stakeholders don't need the level of detail Tableau offers. They typically prefer standard charts and occasional ad-hoc reports, often exported to Excel for further manipulation. Tableau's design for slicing granular data, while flexible, often proves unnecessary and counterproductive.
Why you should or should not use it
In short, Tableau is a powerful tool that requires time and resources to master. If you have the time and budget, and your work environment doesn’t live in Office 365 - it might be worth the investment. It's ideal for detailed data analysis and creating high-quality visuals. But make sure to consider the steep learning curve, the necessity of technical understanding, and the potential overkill for simpler data representation needs.
The Big BI Infrastructure Problem in 2023
BI's original mission was to empower non-technical users to perform tasks that historically required technical knowledge. In a nutshell, any BI solution is an interface layer between users and coding languages like SQL and Python.
Early vendors such as SAP, Siebel, and JD Edwards introduced this BI concept in the 1970s. Excel and later PowerBI adopted and enhanced this technology. Users take a dataset, transform it, and plot it on a chart, all through an intuitive interface.
But to make these tools really usable, companies had to add many new features, resulting in versions cluttered with complex functionalities. This wasn't by design but a structural challenge in moving away from this model.
Many tried to overcome these limitations using different approaches, but they've largely been unsuccessful. Coding remains the most effective tool for data analysis. Those without coding skills still struggle a lot, even though these tools are better than having no tools at all.
Then came Generative AI in November 2022. Despite some unjustified hype, AI is really good in one area: processing language. It can act as a bridge between various entities, clarifying concepts in math, English grammar, SQL, Python, or chart trends.
This opens up the possibility of using AI to intuitively and user-friendly craft code in plain English, enabling users to achieve results without navigating complex BI interfaces. We talked about some other applications of Gen AI in Sales in this article.
In November 2022, we knew that this was what we want to build. It was also the right time to address PowerBI and Tableau's limitations, such as mapping, pricing, and lack of diagnostic intelligence. These tools don't explain business trends or predict upcoming challenges. Filling these gaps is now our commitment.
Meet Datalynx - A new BI Infrastructure
What if you could talk to your data? Imagine not having to configure measures and dimensions in Tableau. Instead, you could simply tell Tableau what you need, and it would deliver. What if you could build reports block by block using just… words? That’s exactly what Datalynx offers.
When taking notes, some people prefer paper and pen, while others type on a laptop. This preference also applies to Business Intelligence. Previously, when you needed to uncover data stories and identify patterns to inform your ideas and strategies, programming and BI tools were your only options. But now, things have changed. You no longer need to rely solely on traditional methods. You still can, but it's no longer a necessity.
We've streamlined the entire BI configuration process into a single chat interface. No more hacky methods to achieve your goals – simply tell the AI what you need, and it will do it for you. Plus, you can tweak and adjust as much as you want.
Why you should or should not use it
Think of working with data like building with LEGO pieces. If you want the freedom to build a castle with unlimited LEGO pieces, then Datalynx is the tool for you.
But if you're comfortable with a pre-existing LEGO pack with a defined set of pieces, traditional BI tools might suffice. They provide structured, but limited, ways of exploring data.
Datalynx isn't a replacement for other BI solutions. Rather, it's a different toolbox, offering a more effective and user-friendly approach for those committed to staying data-driven.Our goal is to enhance the data experience, making it accessible and engaging for everybody in the company, without the constraints of conventional BI tools.
Pros
Streamlined Data Analysis: Receive accurate answers to complex data queries in minutes. The AI understands your questions and delves into the data, providing insights like a conversation with an expert.
Centralized Data Management: Integrates seamlessly with your entire tech ecosystem, including CRMs, databases, product analytics, marketing platforms, and other tools.
Diagnostic & Predictive Analytics: Uncover answers to "why" questions even before your team does. Datalynx also considers external factors, like news events, impacting your business for a comprehensive performance analysis.
Flexible Data Construction: Construct tailored data analysis like building with Lego blocks, catering to specific needs.
Story-Driven Data Presentation: Data comes with narratives, making it easy for your team to understand their implications without additional analysis.
Pre-Curated Analysis Options: Datalynx analyzes your company data and suggests a range of analyses, removing the guesswork of starting from scratch. Now, anyone on your team can access actionable insights, no data expertise needed.
Cons
Limited Visualization Capabilities: Results include charts, but they don't compare with what Tableau offers. This might change in the future, but it's not a feature as of now.
No Python or R Integration: While Datalynx can write R or Python scripts, it doesn't support custom coding. About 90% of tasks can be accomplished with AI assistance, but custom report coding without AI isn't possible.
No ETL (Extract, Transform, Load): If your database is disorganized and requires significant transformation before analysis, the AI might struggle to understnad your structure. This leads to extensive mapping to explain column and table meanings, which is not ideal.
Harnessing Data for Competitive Advantage
As data volumes from various sources like ERP systems and APIs continue expanding, modern BI platforms leverage machine learning and artificial intelligence to help business users integrate and prepare these raw data sources for analysis.
The BI tools above stand out for the numerous options they provide across desktop, mobile, and email to create customizable alerts, graphs, and reports tailored to the evolving business questions across sales, customer behavior, supply chain analytics and more.
By using automation, algorithms and natural language functionality to surface outliers and inefficiencies, great BI software gives people throughout an organization — not just analysts and IT departments — the ability to unlock insights and gain a competitive advantage.
Industry leaders turn to the key features in top-rated BI platforms like data preparation, big data analytics, and data visualization to get more value from growing data volumes. The various sources feeding these modern solutions with raw data include Oracle, Salesforce, cloud apps, on-premise databases and more, which are then integrated via APIs into centralized business intelligence platforms.
According to Gartner’s latest evaluation of BI vendors, options like Microsoft Power BI exemplify best practices that enable almost any business user to leverage data and new technologies in meeting needs, finding answers to their questions, and propelling their organization to the next level with insightful intelligence.
Conclusion
We've done a lot of analysis for you to better understand what the BI market has to offer and what to expect. Hopefully, it was helpful. As you can see, there's no one-size-fits-all solution. It really depends on your goals, your team's technical skills, and the specific challenges you're trying to tackle.
PowerBI and Tableau, with their depth and versatility, are great for those ready to dive into the more technical aspects of data analysis. They're like the heavy-duty tools in your BI toolbox, ideal for detailed, customizable insights.
But remember, complexity isn't always necessary. That's where something like Datalynx comes in. It's for those who want to keep things simple and intuitive, offering a more conversational approach to data analysis.
At the end of the day, choosing the right BI tool is about finding that sweet spot where the tool's functionalities align perfectly with your business objectives and data strategy. The goal is to make your data work for you, driving informed decisions and strategic growth. So, assess your needs, weigh your options, and choose the tool that will turn the data into your most valuable asset.
Discover the 'why' behind your data today
Think about the last time you had a business question. How long did it take to answer it?
Discover the 'why' behind your data today
Think about the last time you had a business question. How long did it take to answer it?
Discover the 'why' behind your data today
Think about the last time you had a business question. How long did it take to answer it?