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I remember when I started my data analytics career 5 years ago, I had the luxury to use Tableau to prepare business reports.
It was never ideal, but I thought it was much cooler than using Excel. I could dump in a huge CSV into it and in a few hours have a nice dashboard.
Recently, it made me think that it wasn't always like that. Even spreadsheet software was an insane leap from the paper-based methods and calculators that were popular in the 60s.
So I spent a couple of days researching this topic.
In this article, I'd like to share what the BI journey looked like back then, how it looks right now, and what happened in between.
The Early Days of Business Intelligence
The concept of business goes way back to ancient times, even before recorded history. People have always traded goods and services, and it always had some information tracking involved.
Now, as you can imagine, with time, the amount of information was growing as well as the available means to collect this information. So eventually, writing everything down on paper and using a calculator stopped being efficient.
That's not to say computers changed the business analytics nutshell. The core of what companies were looking to analyze and discover in their data remained the same. What really changed was the way how this data turned into an accessible pool of knowledge.
The Introduction of Spreadsheets in the 1970s and 1980s
Since the day of the first computers in 1946, it was clear that a lot of business operations were about to change. That's actually around the time when the term "Business Intelligence" was coined by Hans Peter Luhn, an engineer at IBM.
It did take some time for the first spreadsheet program to be developed. VisiCalc was the software of this kind, and it basically allowed moving 80% of paper writings to the computer. It was originally released by VisiCorp on October 17, 1979, and was available only for Macintosh.
But the way it looked and operated is far away from our common understanding of BI. It literally looked like a spreadsheet boxed in the terminal. Yet, the value it started to bring was tremendous.
Now it didn't take long for Microsoft to catch up and release Excel 6 years later. It was built upon the foundation laid by VisiCalc, adding more features, including the ability to create various types of charts and graphs to visualize the data directly from the spreadsheet.
The Rise of BI and Data Visualization Tools
Interestingly, for the next 20 years from 1980 to the 2000s, there were not many breakthroughs happening in this field. Engineers working on Excel and other similar tools like Lotus 1-2-3 were consistently improving the software, adding new features, making it more user-friendly, etc.
But this was happening primarily because computers were becoming better, hence the software was reflecting the greater capabilities opened up for it.
The big innovation happened with Tableau emerging in 2003. It was a completely new approach to translating users' requests to find out some information into the actual information retrieved and delivered in a nice way.
If before you had to put together a table in Excel and then overlay some chart on it, with Tableau you could create different chart types by actually manipulating the data of your dataset and then plotting it on the x and y axis.
It was huge because if you wanted to change something, you didn't have to update the source table. Instead, you just needed to update the current chart's configs, which resulted in its wide adoption among businesses.
The Basis of BI Tools In Simple Words
I'd like to touch on the paradigm that Tableau introduced. The whole idea behind their approach was the ability to translate the interface options like dimensions and metrics you drag and drop on the chart into queries running on its data engine.
Which is a way more user-friendly way of retrieving the data from the database using SQL. This allowed millions of non-technical business owners to retrieve insights about their business operations without being so heavily reliant on their engineers.
Cool, right?
Microsoft Releasing PowerBI
We know how conservative and slow Microsoft is when it comes to innovation. BI was no exception.
To be honest, Excel was and still is doing a great job of analyzing data. It's extremely popular. But its limitations were pushing more and more people to move to Tableau, especially companies with complex data needs.
Visual Basic was the primary way to manipulate Excel data, but it was completely against the concept Tableau introduced.
So finally, in 2015, they decided to release an "advanced" excel that was pretty much a combination of Tableau and Excel. And they called it PowerBI.
Now this was huge. Primarily because Excel was still dominating in the corporate world, while Tableau was more preferred by data scientists and analysts working on a very broad spectrum of areas.
Therefore, all the businesses started upgrading to PowerBI like crazy. It was a great move from Microsoft, no doubt.
P.S.
By no means do I imply that BI revolved around these two tools whatsoever. There are many other great tools that were built specifically to resolve this data need problem. Sisense, Qlik, Looker, just to name a few.
I actually have an article on the best BI tools, as we tested 85% of them. You can read more about it here.
New Technology, The Same Infrastructure
Now, reflecting on this, it first seemed to me as great progress. But as I started diving deeper, I realized that at its core, PowerBI has the same identical principle of operating with the data as Tableau.
And while comparing this progress to what was happening between the 1950s and 2000s, and the insane progress the programs made every decade, it didn't make quite a lot of sense to me.
I understand that the incremental value of improving the tools in 2010 is drastically different from the one 50 years ago, as the pace at which hardware was getting up to the next level with every new update was totally wild.
But 2000 and 2015 is quite a lot of time. Apparently, the BI trend is very bonded to the broader technologies that are being developed in the market.
The primary improvements in that time were related to speed, memory, efficiency of computers and programs, hence the tools remained the same but faster, lighter, and more efficient. It reminds me of Henry Ford's words about faster horses.
A Chance For A New Infrastructure with AI
Now, recently we had a great breakthrough in the generative AI field. November 30, 2022, was the date when OpenAI launched ChatGPT to the public.
That's not the only thing. What really happened was that the technology behind it finally became really smart. The old GPT models were not even close compared to the released one.
It was the first model when a computer could actually talk to you. It might seem that the computer has reasoning and feelings. Like it's an actual person. (Even if it's not).
So, as you can imagine, a vast majority of industries' engineers scratched their heads and started thinking about how to leverage this technology in a way that would benefit their use case.
And this wasn't something that BI technologies got away with. Big corps, like Salesforce, Microsoft, and Google, started thinking about how they can make their BI tools powered by AI.
First of all, for marketing, of course, and then for the actual value for users.
The Issue with Big Techs Making BI Technologies AI-Powered
The answer is in the title. I think the big mistake (or rather a forced mistake) leading BI companies made is they started applying AI on TOP of the existing infrastructure.
In other words, they make AI utilize whatever they've been building for the last 20 years to create reports and do different kinds of analysis instead of utilizing AI's capacity to actually do it himself.
Well, I don't think they have much choice. These companies are so much rooted into the existing infrastructure and tech that it would be very hard to rebuild everything favoring AI.
And I know, much of you might disagree, but I believe that when such a groundbreaking thing is happening in the cutting edge of software, you should, must utilize it to its max potential. And you can't do it by simply plugging it on top of what you already have to make it "better".
Not in all cases though, adding AI on top is a bad idea. In my opinion, it depends on the type of product you have. If AI adds new functionality and significantly enhances some features of your product, then it's a great example of when adding AI on top will actually bring a lot of value for users in the long term.
For example, Intercom (a customer support chatbot solution) is now working on adding AI to resolve some tickets automatically by actually chatting with the customer and using the documentation the company has. That's a very cool way to improve the product.
On the other hand, adding AI to configure the metrics and dimensions is kind of helpful, but it's basically the way to prompt AI to use the interface for you, which sets so many limitations on the model since the UI was initially built for humans, not robots.
Data Analysis Using Words Will Replace AI Technologies
Imagine if you could analyze data by simply talking to the data. Literally asking a question and getting the answer you need. Asking a follow-up and getting results in seconds.
This is how the new infrastructure should look. Now you can not only ask descriptive kinds of questions (What?) but also Diagnostic ones (Why?) and very soon, suggestive ones (What should I do?).
You can't achieve it by simply plugging AI into your BI tool. You need a new tool. Otherwise, you're missing out on so many opportunities that Gen AI has to offer.
And yes, it might sound very opinionated, but that's what we believe in. That's what companies and executives we talk with every day believe in. That's what the trend is slowly but surely adopting week after week.
If you feel like this approach resonated with you, at Datalynx, for the last 2 years, we have been working on exactly that.
A simple tool that can allow you to analyze your data using words. You can try it completely for free for 7 days here.
The Journey Of BI Technologies
The evolution of business Intelligence technologies has been a fascinating journey.
From the early days of passive network sources and power hybrid microcircuits to the modern era of AI-powered insights, BI has come a long way.
In the 1960s and 70s, companies relied on product categories like resistor networks and precision potentiometers to gather and analyze data. These early tools laid the foundation for what we now know as Business Intelligence software.
The term "Business Intelligence" was coined by Howard Dresner in 1989, serving as an umbrella term for various analytical processes and tools.
As technology advanced, so did the capabilities of BI. Statistical analysis and predictive analytics became more prevalent, enabling decision-makers to gain deeper insights into their businesses (think predicting sales. This was huge when it first became available).
Throughout the 1990s and early 2000s, BI continued to evolve, with the introduction of more sophisticated reporting tools and analytical processing techniques.
Companies began to leverage BI for a wide range of industrial applications, from managing outstanding balances to optimizing operational intelligence.
And now here we are, having AI analyze data for us. Exciting, isn't it? It's clear that BI will continue to play an important role in helping businesses grow faster and stay data-driven.
I remember when I started my data analytics career 5 years ago, I had the luxury to use Tableau to prepare business reports.
It was never ideal, but I thought it was much cooler than using Excel. I could dump in a huge CSV into it and in a few hours have a nice dashboard.
Recently, it made me think that it wasn't always like that. Even spreadsheet software was an insane leap from the paper-based methods and calculators that were popular in the 60s.
So I spent a couple of days researching this topic.
In this article, I'd like to share what the BI journey looked like back then, how it looks right now, and what happened in between.
The Early Days of Business Intelligence
The concept of business goes way back to ancient times, even before recorded history. People have always traded goods and services, and it always had some information tracking involved.
Now, as you can imagine, with time, the amount of information was growing as well as the available means to collect this information. So eventually, writing everything down on paper and using a calculator stopped being efficient.
That's not to say computers changed the business analytics nutshell. The core of what companies were looking to analyze and discover in their data remained the same. What really changed was the way how this data turned into an accessible pool of knowledge.
The Introduction of Spreadsheets in the 1970s and 1980s
Since the day of the first computers in 1946, it was clear that a lot of business operations were about to change. That's actually around the time when the term "Business Intelligence" was coined by Hans Peter Luhn, an engineer at IBM.
It did take some time for the first spreadsheet program to be developed. VisiCalc was the software of this kind, and it basically allowed moving 80% of paper writings to the computer. It was originally released by VisiCorp on October 17, 1979, and was available only for Macintosh.
But the way it looked and operated is far away from our common understanding of BI. It literally looked like a spreadsheet boxed in the terminal. Yet, the value it started to bring was tremendous.
Now it didn't take long for Microsoft to catch up and release Excel 6 years later. It was built upon the foundation laid by VisiCalc, adding more features, including the ability to create various types of charts and graphs to visualize the data directly from the spreadsheet.
The Rise of BI and Data Visualization Tools
Interestingly, for the next 20 years from 1980 to the 2000s, there were not many breakthroughs happening in this field. Engineers working on Excel and other similar tools like Lotus 1-2-3 were consistently improving the software, adding new features, making it more user-friendly, etc.
But this was happening primarily because computers were becoming better, hence the software was reflecting the greater capabilities opened up for it.
The big innovation happened with Tableau emerging in 2003. It was a completely new approach to translating users' requests to find out some information into the actual information retrieved and delivered in a nice way.
If before you had to put together a table in Excel and then overlay some chart on it, with Tableau you could create different chart types by actually manipulating the data of your dataset and then plotting it on the x and y axis.
It was huge because if you wanted to change something, you didn't have to update the source table. Instead, you just needed to update the current chart's configs, which resulted in its wide adoption among businesses.
The Basis of BI Tools In Simple Words
I'd like to touch on the paradigm that Tableau introduced. The whole idea behind their approach was the ability to translate the interface options like dimensions and metrics you drag and drop on the chart into queries running on its data engine.
Which is a way more user-friendly way of retrieving the data from the database using SQL. This allowed millions of non-technical business owners to retrieve insights about their business operations without being so heavily reliant on their engineers.
Cool, right?
Microsoft Releasing PowerBI
We know how conservative and slow Microsoft is when it comes to innovation. BI was no exception.
To be honest, Excel was and still is doing a great job of analyzing data. It's extremely popular. But its limitations were pushing more and more people to move to Tableau, especially companies with complex data needs.
Visual Basic was the primary way to manipulate Excel data, but it was completely against the concept Tableau introduced.
So finally, in 2015, they decided to release an "advanced" excel that was pretty much a combination of Tableau and Excel. And they called it PowerBI.
Now this was huge. Primarily because Excel was still dominating in the corporate world, while Tableau was more preferred by data scientists and analysts working on a very broad spectrum of areas.
Therefore, all the businesses started upgrading to PowerBI like crazy. It was a great move from Microsoft, no doubt.
P.S.
By no means do I imply that BI revolved around these two tools whatsoever. There are many other great tools that were built specifically to resolve this data need problem. Sisense, Qlik, Looker, just to name a few.
I actually have an article on the best BI tools, as we tested 85% of them. You can read more about it here.
New Technology, The Same Infrastructure
Now, reflecting on this, it first seemed to me as great progress. But as I started diving deeper, I realized that at its core, PowerBI has the same identical principle of operating with the data as Tableau.
And while comparing this progress to what was happening between the 1950s and 2000s, and the insane progress the programs made every decade, it didn't make quite a lot of sense to me.
I understand that the incremental value of improving the tools in 2010 is drastically different from the one 50 years ago, as the pace at which hardware was getting up to the next level with every new update was totally wild.
But 2000 and 2015 is quite a lot of time. Apparently, the BI trend is very bonded to the broader technologies that are being developed in the market.
The primary improvements in that time were related to speed, memory, efficiency of computers and programs, hence the tools remained the same but faster, lighter, and more efficient. It reminds me of Henry Ford's words about faster horses.
A Chance For A New Infrastructure with AI
Now, recently we had a great breakthrough in the generative AI field. November 30, 2022, was the date when OpenAI launched ChatGPT to the public.
That's not the only thing. What really happened was that the technology behind it finally became really smart. The old GPT models were not even close compared to the released one.
It was the first model when a computer could actually talk to you. It might seem that the computer has reasoning and feelings. Like it's an actual person. (Even if it's not).
So, as you can imagine, a vast majority of industries' engineers scratched their heads and started thinking about how to leverage this technology in a way that would benefit their use case.
And this wasn't something that BI technologies got away with. Big corps, like Salesforce, Microsoft, and Google, started thinking about how they can make their BI tools powered by AI.
First of all, for marketing, of course, and then for the actual value for users.
The Issue with Big Techs Making BI Technologies AI-Powered
The answer is in the title. I think the big mistake (or rather a forced mistake) leading BI companies made is they started applying AI on TOP of the existing infrastructure.
In other words, they make AI utilize whatever they've been building for the last 20 years to create reports and do different kinds of analysis instead of utilizing AI's capacity to actually do it himself.
Well, I don't think they have much choice. These companies are so much rooted into the existing infrastructure and tech that it would be very hard to rebuild everything favoring AI.
And I know, much of you might disagree, but I believe that when such a groundbreaking thing is happening in the cutting edge of software, you should, must utilize it to its max potential. And you can't do it by simply plugging it on top of what you already have to make it "better".
Not in all cases though, adding AI on top is a bad idea. In my opinion, it depends on the type of product you have. If AI adds new functionality and significantly enhances some features of your product, then it's a great example of when adding AI on top will actually bring a lot of value for users in the long term.
For example, Intercom (a customer support chatbot solution) is now working on adding AI to resolve some tickets automatically by actually chatting with the customer and using the documentation the company has. That's a very cool way to improve the product.
On the other hand, adding AI to configure the metrics and dimensions is kind of helpful, but it's basically the way to prompt AI to use the interface for you, which sets so many limitations on the model since the UI was initially built for humans, not robots.
Data Analysis Using Words Will Replace AI Technologies
Imagine if you could analyze data by simply talking to the data. Literally asking a question and getting the answer you need. Asking a follow-up and getting results in seconds.
This is how the new infrastructure should look. Now you can not only ask descriptive kinds of questions (What?) but also Diagnostic ones (Why?) and very soon, suggestive ones (What should I do?).
You can't achieve it by simply plugging AI into your BI tool. You need a new tool. Otherwise, you're missing out on so many opportunities that Gen AI has to offer.
And yes, it might sound very opinionated, but that's what we believe in. That's what companies and executives we talk with every day believe in. That's what the trend is slowly but surely adopting week after week.
If you feel like this approach resonated with you, at Datalynx, for the last 2 years, we have been working on exactly that.
A simple tool that can allow you to analyze your data using words. You can try it completely for free for 7 days here.
The Journey Of BI Technologies
The evolution of business Intelligence technologies has been a fascinating journey.
From the early days of passive network sources and power hybrid microcircuits to the modern era of AI-powered insights, BI has come a long way.
In the 1960s and 70s, companies relied on product categories like resistor networks and precision potentiometers to gather and analyze data. These early tools laid the foundation for what we now know as Business Intelligence software.
The term "Business Intelligence" was coined by Howard Dresner in 1989, serving as an umbrella term for various analytical processes and tools.
As technology advanced, so did the capabilities of BI. Statistical analysis and predictive analytics became more prevalent, enabling decision-makers to gain deeper insights into their businesses (think predicting sales. This was huge when it first became available).
Throughout the 1990s and early 2000s, BI continued to evolve, with the introduction of more sophisticated reporting tools and analytical processing techniques.
Companies began to leverage BI for a wide range of industrial applications, from managing outstanding balances to optimizing operational intelligence.
And now here we are, having AI analyze data for us. Exciting, isn't it? It's clear that BI will continue to play an important role in helping businesses grow faster and stay data-driven.
I remember when I started my data analytics career 5 years ago, I had the luxury to use Tableau to prepare business reports.
It was never ideal, but I thought it was much cooler than using Excel. I could dump in a huge CSV into it and in a few hours have a nice dashboard.
Recently, it made me think that it wasn't always like that. Even spreadsheet software was an insane leap from the paper-based methods and calculators that were popular in the 60s.
So I spent a couple of days researching this topic.
In this article, I'd like to share what the BI journey looked like back then, how it looks right now, and what happened in between.
The Early Days of Business Intelligence
The concept of business goes way back to ancient times, even before recorded history. People have always traded goods and services, and it always had some information tracking involved.
Now, as you can imagine, with time, the amount of information was growing as well as the available means to collect this information. So eventually, writing everything down on paper and using a calculator stopped being efficient.
That's not to say computers changed the business analytics nutshell. The core of what companies were looking to analyze and discover in their data remained the same. What really changed was the way how this data turned into an accessible pool of knowledge.
The Introduction of Spreadsheets in the 1970s and 1980s
Since the day of the first computers in 1946, it was clear that a lot of business operations were about to change. That's actually around the time when the term "Business Intelligence" was coined by Hans Peter Luhn, an engineer at IBM.
It did take some time for the first spreadsheet program to be developed. VisiCalc was the software of this kind, and it basically allowed moving 80% of paper writings to the computer. It was originally released by VisiCorp on October 17, 1979, and was available only for Macintosh.
But the way it looked and operated is far away from our common understanding of BI. It literally looked like a spreadsheet boxed in the terminal. Yet, the value it started to bring was tremendous.
Now it didn't take long for Microsoft to catch up and release Excel 6 years later. It was built upon the foundation laid by VisiCalc, adding more features, including the ability to create various types of charts and graphs to visualize the data directly from the spreadsheet.
The Rise of BI and Data Visualization Tools
Interestingly, for the next 20 years from 1980 to the 2000s, there were not many breakthroughs happening in this field. Engineers working on Excel and other similar tools like Lotus 1-2-3 were consistently improving the software, adding new features, making it more user-friendly, etc.
But this was happening primarily because computers were becoming better, hence the software was reflecting the greater capabilities opened up for it.
The big innovation happened with Tableau emerging in 2003. It was a completely new approach to translating users' requests to find out some information into the actual information retrieved and delivered in a nice way.
If before you had to put together a table in Excel and then overlay some chart on it, with Tableau you could create different chart types by actually manipulating the data of your dataset and then plotting it on the x and y axis.
It was huge because if you wanted to change something, you didn't have to update the source table. Instead, you just needed to update the current chart's configs, which resulted in its wide adoption among businesses.
The Basis of BI Tools In Simple Words
I'd like to touch on the paradigm that Tableau introduced. The whole idea behind their approach was the ability to translate the interface options like dimensions and metrics you drag and drop on the chart into queries running on its data engine.
Which is a way more user-friendly way of retrieving the data from the database using SQL. This allowed millions of non-technical business owners to retrieve insights about their business operations without being so heavily reliant on their engineers.
Cool, right?
Microsoft Releasing PowerBI
We know how conservative and slow Microsoft is when it comes to innovation. BI was no exception.
To be honest, Excel was and still is doing a great job of analyzing data. It's extremely popular. But its limitations were pushing more and more people to move to Tableau, especially companies with complex data needs.
Visual Basic was the primary way to manipulate Excel data, but it was completely against the concept Tableau introduced.
So finally, in 2015, they decided to release an "advanced" excel that was pretty much a combination of Tableau and Excel. And they called it PowerBI.
Now this was huge. Primarily because Excel was still dominating in the corporate world, while Tableau was more preferred by data scientists and analysts working on a very broad spectrum of areas.
Therefore, all the businesses started upgrading to PowerBI like crazy. It was a great move from Microsoft, no doubt.
P.S.
By no means do I imply that BI revolved around these two tools whatsoever. There are many other great tools that were built specifically to resolve this data need problem. Sisense, Qlik, Looker, just to name a few.
I actually have an article on the best BI tools, as we tested 85% of them. You can read more about it here.
New Technology, The Same Infrastructure
Now, reflecting on this, it first seemed to me as great progress. But as I started diving deeper, I realized that at its core, PowerBI has the same identical principle of operating with the data as Tableau.
And while comparing this progress to what was happening between the 1950s and 2000s, and the insane progress the programs made every decade, it didn't make quite a lot of sense to me.
I understand that the incremental value of improving the tools in 2010 is drastically different from the one 50 years ago, as the pace at which hardware was getting up to the next level with every new update was totally wild.
But 2000 and 2015 is quite a lot of time. Apparently, the BI trend is very bonded to the broader technologies that are being developed in the market.
The primary improvements in that time were related to speed, memory, efficiency of computers and programs, hence the tools remained the same but faster, lighter, and more efficient. It reminds me of Henry Ford's words about faster horses.
A Chance For A New Infrastructure with AI
Now, recently we had a great breakthrough in the generative AI field. November 30, 2022, was the date when OpenAI launched ChatGPT to the public.
That's not the only thing. What really happened was that the technology behind it finally became really smart. The old GPT models were not even close compared to the released one.
It was the first model when a computer could actually talk to you. It might seem that the computer has reasoning and feelings. Like it's an actual person. (Even if it's not).
So, as you can imagine, a vast majority of industries' engineers scratched their heads and started thinking about how to leverage this technology in a way that would benefit their use case.
And this wasn't something that BI technologies got away with. Big corps, like Salesforce, Microsoft, and Google, started thinking about how they can make their BI tools powered by AI.
First of all, for marketing, of course, and then for the actual value for users.
The Issue with Big Techs Making BI Technologies AI-Powered
The answer is in the title. I think the big mistake (or rather a forced mistake) leading BI companies made is they started applying AI on TOP of the existing infrastructure.
In other words, they make AI utilize whatever they've been building for the last 20 years to create reports and do different kinds of analysis instead of utilizing AI's capacity to actually do it himself.
Well, I don't think they have much choice. These companies are so much rooted into the existing infrastructure and tech that it would be very hard to rebuild everything favoring AI.
And I know, much of you might disagree, but I believe that when such a groundbreaking thing is happening in the cutting edge of software, you should, must utilize it to its max potential. And you can't do it by simply plugging it on top of what you already have to make it "better".
Not in all cases though, adding AI on top is a bad idea. In my opinion, it depends on the type of product you have. If AI adds new functionality and significantly enhances some features of your product, then it's a great example of when adding AI on top will actually bring a lot of value for users in the long term.
For example, Intercom (a customer support chatbot solution) is now working on adding AI to resolve some tickets automatically by actually chatting with the customer and using the documentation the company has. That's a very cool way to improve the product.
On the other hand, adding AI to configure the metrics and dimensions is kind of helpful, but it's basically the way to prompt AI to use the interface for you, which sets so many limitations on the model since the UI was initially built for humans, not robots.
Data Analysis Using Words Will Replace AI Technologies
Imagine if you could analyze data by simply talking to the data. Literally asking a question and getting the answer you need. Asking a follow-up and getting results in seconds.
This is how the new infrastructure should look. Now you can not only ask descriptive kinds of questions (What?) but also Diagnostic ones (Why?) and very soon, suggestive ones (What should I do?).
You can't achieve it by simply plugging AI into your BI tool. You need a new tool. Otherwise, you're missing out on so many opportunities that Gen AI has to offer.
And yes, it might sound very opinionated, but that's what we believe in. That's what companies and executives we talk with every day believe in. That's what the trend is slowly but surely adopting week after week.
If you feel like this approach resonated with you, at Datalynx, for the last 2 years, we have been working on exactly that.
A simple tool that can allow you to analyze your data using words. You can try it completely for free for 7 days here.
The Journey Of BI Technologies
The evolution of business Intelligence technologies has been a fascinating journey.
From the early days of passive network sources and power hybrid microcircuits to the modern era of AI-powered insights, BI has come a long way.
In the 1960s and 70s, companies relied on product categories like resistor networks and precision potentiometers to gather and analyze data. These early tools laid the foundation for what we now know as Business Intelligence software.
The term "Business Intelligence" was coined by Howard Dresner in 1989, serving as an umbrella term for various analytical processes and tools.
As technology advanced, so did the capabilities of BI. Statistical analysis and predictive analytics became more prevalent, enabling decision-makers to gain deeper insights into their businesses (think predicting sales. This was huge when it first became available).
Throughout the 1990s and early 2000s, BI continued to evolve, with the introduction of more sophisticated reporting tools and analytical processing techniques.
Companies began to leverage BI for a wide range of industrial applications, from managing outstanding balances to optimizing operational intelligence.
And now here we are, having AI analyze data for us. Exciting, isn't it? It's clear that BI will continue to play an important role in helping businesses grow faster and stay data-driven.
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?