Oct 12, 2021
The Third Wave of SaaS
How data will come alive to drive insights for users
This post is extremely short, and captures a simple trend in software while explaining why LightSource feels like such magic.
There are three waves in the world of B2B SaaS software.
SaaS Software Wave 1: Just Digitize it
In the early days of business software, the name of the game was process digitization.
Some team or business function had a workflow. And that workflow is done through a "manual" process, likely using a mix of analog and software tools. Phone, email, excel, mail, fax, PDF, etc. These are all mediums for bare minimum communication.
The first wave of SaaS was extremely simple: take those workflows and move them into a digital product designed to handle them. In procurement, this would be the e-Procurement and e-RFQ systems of the early-2000's.
SaaS Software Wave 2: People Find Insights
Well, now that we have all this data in one place, what can we learn? Now that data is collected in a usable format, people can collect data from different siloes across many users.
People then ask all the questions. What is our spend? What is our supply base? What are we buying?
To answer these questions, the onus is on people to build dashboards, author queries, or often times, do a lot of manual data-scrubbing to remove noise from the signal.
This is a huge value. Tools like SAP and Oracle (with a ton of cost and arduousness), you can generate insights. More recently, there's been a "Cambrian explosion" in data-infrastructure tools. If you've heard the term "data lake" thrown around thousands of times this year, you're not alone...
Organizing data taken out of silos and putting it together, is certainly an important trend happening now -- and not all companies are there yet.
The problem remains: The friction and effort of discovering new insights is still really high. And sometimes you just don't know what to even be looking for.
SaaS Software Wave 3: Data Finds Insights for People
The third wave, and the one LightSource is built around, is when the data presents insights to people.
If you build a truly intelligent and thoughtful system, the user should not be the one asked to make sense of their data. It should just "surface" for them.
My mental image is Magneto from the X-Men movies. Here, Michael Fassbender uses superpowers to summon all the metal from the ground and surrounding area for a fight:
Similarly, 3rd Wave SaaS tools will do just this. Instead of you needing to dig through your data (with query tools that albeit make it easier), your software will just present insights, ideas, and actions directly to you.
OK, SaaS Wave 3 sounds great, but why can't my "Modern Data Stack" already do that?
The answer is both obvious and somewhat counter intuitive: a general data system will always require users to dig for insights because it doesn't know what to look for.
Modern data lakes or warehouses are by-design built to hold information from across any organization within the business. Sales, HR, Finance, Accounting, Warehouse, etc... the list goes on. It’s just data, but has no “meaning.”
A pure data infrastructure system will not have the business-logic or context to know what kinds of insights to drive. By definition it requires a person to create that logic and make those decisions.
By contrast, function-specific software can be designed around specific data types and richer modes of interaction.
Using LightSource as an example, we have sourcing theory embedded directly into the data-structures themselves. We have concepts like `Parts` with relationships to `BOMs` and `Quotes`. Within `Quotes` (which come from `Suppliers`) there are `Raw Materials`, `Machines`, `Labor`, `Markups`, and `Logistics`, again based on `Supplier` locations.
It's pretty clear to see how a coherent infrastructure in terms of data allows for the system to generate insights. If Quotes were simply captured in a flexible data lake, of course it would require a human to create the logic to aggregate benchmarks, should-cost models, etc.
These are all things we can present to the user through LightSource.
That's the 3rd Wave of SaaS, and we're really excited for it.
Questions about the three waves of B2B SaaS software that this article answers
What are the three major waves of B2B SaaS software evolution?
How did early procurement software digitize manual processes?
What's the difference between data lakes and function-specific procurement software?
Why can't modern data stacks automatically surface procurement insights?
How does LightSource represent the third wave of SaaS evolution?
What makes domain-specific software better than general data systems for procurement?
Why do traditional data warehouses still require manual analysis?
How does embedding sourcing theory into data structures create automatic insights?
What's the problem with requiring users to dig through data for procurement insights?
How does intelligent software present insights without user effort?
This post is extremely short, and captures a simple trend in software while explaining why LightSource feels like such magic.
There are three waves in the world of B2B SaaS software.
SaaS Software Wave 1: Just Digitize it
In the early days of business software, the name of the game was process digitization.
Some team or business function had a workflow. And that workflow is done through a "manual" process, likely using a mix of analog and software tools. Phone, email, excel, mail, fax, PDF, etc. These are all mediums for bare minimum communication.
The first wave of SaaS was extremely simple: take those workflows and move them into a digital product designed to handle them. In procurement, this would be the e-Procurement and e-RFQ systems of the early-2000's.
SaaS Software Wave 2: People Find Insights
Well, now that we have all this data in one place, what can we learn? Now that data is collected in a usable format, people can collect data from different siloes across many users.
People then ask all the questions. What is our spend? What is our supply base? What are we buying?
To answer these questions, the onus is on people to build dashboards, author queries, or often times, do a lot of manual data-scrubbing to remove noise from the signal.
This is a huge value. Tools like SAP and Oracle (with a ton of cost and arduousness), you can generate insights. More recently, there's been a "Cambrian explosion" in data-infrastructure tools. If you've heard the term "data lake" thrown around thousands of times this year, you're not alone...
Organizing data taken out of silos and putting it together, is certainly an important trend happening now -- and not all companies are there yet.
The problem remains: The friction and effort of discovering new insights is still really high. And sometimes you just don't know what to even be looking for.
SaaS Software Wave 3: Data Finds Insights for People
The third wave, and the one LightSource is built around, is when the data presents insights to people.
If you build a truly intelligent and thoughtful system, the user should not be the one asked to make sense of their data. It should just "surface" for them.
My mental image is Magneto from the X-Men movies. Here, Michael Fassbender uses superpowers to summon all the metal from the ground and surrounding area for a fight:
Similarly, 3rd Wave SaaS tools will do just this. Instead of you needing to dig through your data (with query tools that albeit make it easier), your software will just present insights, ideas, and actions directly to you.
OK, SaaS Wave 3 sounds great, but why can't my "Modern Data Stack" already do that?
The answer is both obvious and somewhat counter intuitive: a general data system will always require users to dig for insights because it doesn't know what to look for.
Modern data lakes or warehouses are by-design built to hold information from across any organization within the business. Sales, HR, Finance, Accounting, Warehouse, etc... the list goes on. It’s just data, but has no “meaning.”
A pure data infrastructure system will not have the business-logic or context to know what kinds of insights to drive. By definition it requires a person to create that logic and make those decisions.
By contrast, function-specific software can be designed around specific data types and richer modes of interaction.
Using LightSource as an example, we have sourcing theory embedded directly into the data-structures themselves. We have concepts like `Parts` with relationships to `BOMs` and `Quotes`. Within `Quotes` (which come from `Suppliers`) there are `Raw Materials`, `Machines`, `Labor`, `Markups`, and `Logistics`, again based on `Supplier` locations.
It's pretty clear to see how a coherent infrastructure in terms of data allows for the system to generate insights. If Quotes were simply captured in a flexible data lake, of course it would require a human to create the logic to aggregate benchmarks, should-cost models, etc.
These are all things we can present to the user through LightSource.
That's the 3rd Wave of SaaS, and we're really excited for it.
Questions about the three waves of B2B SaaS software that this article answers
What are the three major waves of B2B SaaS software evolution?
How did early procurement software digitize manual processes?
What's the difference between data lakes and function-specific procurement software?
Why can't modern data stacks automatically surface procurement insights?
How does LightSource represent the third wave of SaaS evolution?
What makes domain-specific software better than general data systems for procurement?
Why do traditional data warehouses still require manual analysis?
How does embedding sourcing theory into data structures create automatic insights?
What's the problem with requiring users to dig through data for procurement insights?
How does intelligent software present insights without user effort?
This post is extremely short, and captures a simple trend in software while explaining why LightSource feels like such magic.
There are three waves in the world of B2B SaaS software.
SaaS Software Wave 1: Just Digitize it
In the early days of business software, the name of the game was process digitization.
Some team or business function had a workflow. And that workflow is done through a "manual" process, likely using a mix of analog and software tools. Phone, email, excel, mail, fax, PDF, etc. These are all mediums for bare minimum communication.
The first wave of SaaS was extremely simple: take those workflows and move them into a digital product designed to handle them. In procurement, this would be the e-Procurement and e-RFQ systems of the early-2000's.
SaaS Software Wave 2: People Find Insights
Well, now that we have all this data in one place, what can we learn? Now that data is collected in a usable format, people can collect data from different siloes across many users.
People then ask all the questions. What is our spend? What is our supply base? What are we buying?
To answer these questions, the onus is on people to build dashboards, author queries, or often times, do a lot of manual data-scrubbing to remove noise from the signal.
This is a huge value. Tools like SAP and Oracle (with a ton of cost and arduousness), you can generate insights. More recently, there's been a "Cambrian explosion" in data-infrastructure tools. If you've heard the term "data lake" thrown around thousands of times this year, you're not alone...
Organizing data taken out of silos and putting it together, is certainly an important trend happening now -- and not all companies are there yet.
The problem remains: The friction and effort of discovering new insights is still really high. And sometimes you just don't know what to even be looking for.
SaaS Software Wave 3: Data Finds Insights for People
The third wave, and the one LightSource is built around, is when the data presents insights to people.
If you build a truly intelligent and thoughtful system, the user should not be the one asked to make sense of their data. It should just "surface" for them.
My mental image is Magneto from the X-Men movies. Here, Michael Fassbender uses superpowers to summon all the metal from the ground and surrounding area for a fight:
Similarly, 3rd Wave SaaS tools will do just this. Instead of you needing to dig through your data (with query tools that albeit make it easier), your software will just present insights, ideas, and actions directly to you.
OK, SaaS Wave 3 sounds great, but why can't my "Modern Data Stack" already do that?
The answer is both obvious and somewhat counter intuitive: a general data system will always require users to dig for insights because it doesn't know what to look for.
Modern data lakes or warehouses are by-design built to hold information from across any organization within the business. Sales, HR, Finance, Accounting, Warehouse, etc... the list goes on. It’s just data, but has no “meaning.”
A pure data infrastructure system will not have the business-logic or context to know what kinds of insights to drive. By definition it requires a person to create that logic and make those decisions.
By contrast, function-specific software can be designed around specific data types and richer modes of interaction.
Using LightSource as an example, we have sourcing theory embedded directly into the data-structures themselves. We have concepts like `Parts` with relationships to `BOMs` and `Quotes`. Within `Quotes` (which come from `Suppliers`) there are `Raw Materials`, `Machines`, `Labor`, `Markups`, and `Logistics`, again based on `Supplier` locations.
It's pretty clear to see how a coherent infrastructure in terms of data allows for the system to generate insights. If Quotes were simply captured in a flexible data lake, of course it would require a human to create the logic to aggregate benchmarks, should-cost models, etc.
These are all things we can present to the user through LightSource.
That's the 3rd Wave of SaaS, and we're really excited for it.
Questions about the three waves of B2B SaaS software that this article answers
What are the three major waves of B2B SaaS software evolution?
How did early procurement software digitize manual processes?
What's the difference between data lakes and function-specific procurement software?
Why can't modern data stacks automatically surface procurement insights?
How does LightSource represent the third wave of SaaS evolution?
What makes domain-specific software better than general data systems for procurement?
Why do traditional data warehouses still require manual analysis?
How does embedding sourcing theory into data structures create automatic insights?
What's the problem with requiring users to dig through data for procurement insights?
How does intelligent software present insights without user effort?
Ready to change the way you source?
Try out LightSource and you’ll never go back to Excel and email.
Ready to change the way you source?
Try out LightSource and you’ll never go back to Excel and email.
Ready to change the way you source?
Try out LightSource and you’ll never go back to Excel and email.
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