The Challenges SaaS Companies Face with Their Data
You have live dashboards for your product - you're tracking metrics, and your growth potential is beginning to look promising…but something feels “not quite right”.
When multiple teams give you different metrics, and everyone seems to be delaying the product decision-making process because of a lack of trust in the data, the leadership team begins to ask, “Which metric is ‘actually’ correct?”
It’s at this stage that many SaaS companies realize their modern data infrastructure is not able to keep pace with their growth.
If you're a head of data or VP of Data, the information in this guide will help you achieve the following:
- Understand why product analytics infrastructure becomes complex as it grows
- How quickly are fast-growing SaaS companies developing their modern data systems
- What will your roadmap look like in terms of improving the reliability, speed, and trustworthiness of your data
In the end, the product you provide as a SaaS company will only be as “good” as the quality of the underlying data driving that product.
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Some Unique Challenges to SaaS Data
SaaS companies operate in a fundamentally different data environment from traditional companies.
1. High event volume
Users create data every time they interact with your product. For example:
- Clicking something
- Creating a session
- Using your products
- Making an actual purchase
When this happens, you create massive event streams and have high event ingestion requirements.
2. Real-time expectations
Your product teams need:
- Instant feedback on the usage of features
- Real-time dashboards
- Instant Insight
Both your batch and real-time processing approaches need to be combined.
3. Rapid product iteration
SaaS companies that have rapidly developed their products continuously add new features and functionality to their products. Therefore, they frequently add new events to the system or change their existing data model and events, often on a monthly or quarterly basis.
When you don't have a clear structure for how data will be identified, tagged, and transformed into actionable insights, you will have:
- Broken data pipelines
- Inconsistent metrics
4. Data fragmentation
Data from a SaaS company's product (product data) is stored in various locations, including:
- Product databases
- Marketing tools
- CRM systems
As a result, it becomes incredibly difficult for
The Missed Opportunities
When launching a SaaS company, most companies have:
- Basic tracking tools
- Basic pipelines
Upon becoming more successful, however, these tools:
- Become fragile
- Make the company unable to trust their data
- Increase the cost of engineering resources
Modern data infrastructures should always evolve as the product becomes more complicated - not the other way around.
Key Requirements
- GDPR (EU)
- CCPA (CA)
- SOC 2 (sec and reliably)
The way this will impact your company includes:
- Storage of data
- Processing of data
- Access to data
1. Data Residency
User's data should remain within a certain geographic area.
This has a major effect on the:
- Architecture of your storage
- Redundancy of your data
2. Data Retention
Businesses need to:
- Remove data after defined periods
- Keep track of lifecycle policies
3. Auditability
You must keep a complete record of:
- where data originated
- how it was manipulated
- which users accessed it
Because of this need, data lineage has become very important.
Build Versus Retrofit
Building compliance in the early stages of your product will:
- Mitigate risk
- Decrease long-term costs
Retrofitting compliance after-the-fact will be:
- Very expensive
- Slow growth down significantly
The compliance will not stop you; it will merely give you a framework for your data platform's design
Core Levels
Ingest Events
When an event occurs, a user operates and makes the choice to complete the event; this is the first process.
Receive the Users
The ingestion stage collects and stores this data.
Store the Layer
This is where the incoming raw data will be kept.
Process Layer
The processing layer converts the previously stored raw data into an analytical form.
Controlling the Layer
In order to manage all of these processes, there will be some system or method of ensuring the correct order of all of these layers of events are completed according to the rule of six layers.
The System to Observe
This system would be in place to monitor the health of the entire data system.
Processing Event-Cycle vs. Event Processing in Real-Time
Processes that can use real-time data:
- In order to create product dashboards.
- Tracking user activity.
Processes that can use batch data:
This is for reporting and analyzing historical data.
Working with Multiple Systems
Integrate:
- Product Databases
- CRM's (Customer Relationship Management)
- Marketing (Marketing Automation and Tracking System)
Ensuring:
- The consistency of all data at the time of its ingestion.
- Timing of data.
Key Insight
A modern data infrastructure is designed to be extensible and supports the separation of concerns while integrating.
Common Use Cases for SaaS Companies Using Modern Data Infrastructure.
The use cases that apply to an organization may provide clarity to the design decisions made.
1. Creating Real-Time Product Analytics
2. Assuring that Ingesting Data is Low Latency and Reliable.
3. Creating a Customer 360 View.
4. Creating Product Usage/Billing Data/Support Interaction as My Data Would Create a 360 View.
5. Creating Precision in Data-Driven Decision-Making via Timeliness & Accuracy.
6. Using A/B Testing to Drive Business Results.
7. Is My Data Infrastructure Capable of Aligning AI/ML Integration?
Key Insight
Your specific use case creates a unique requirement for your data pipeline architecture.
Key Principles to Follow
Through successful organizations, a pattern emerges that is easily recognized within high-performing teams.
What do other organizations miss?
1. Treat Your Data as a Product
Curated vs Collected.
2. Develop Observability Early in the Data Pipeline.
Defining the issue before the event affects the users.
Create Cross-Functional Ownership
Collaboration between data engineers, product managers, and business analysts.
What others get wrong:
1. Reactively fixing things after they break.
2. Tool sprawl (too many scattered tools).
3. No data governance (no defined roles and responsibilities for data ownership).
Before vs After
Before:
- Unreliable metrics;
- Historically unreliable pipeline.
After:
- More reliable data;
- Faster decision-making.
Insights
The key to success is viewing your infrastructure as a strategic asset rather than an afterthought.
How to Implement/How to Get Started
Developing a modern data infrastructure requires a structured approach.
1. Begin with key data flows;
Focus areas:
- Revenue metrics.
- Product Usage data.
2. Align your data infrastructure with the company’s overall business strategy;
Make sure the data infrastructure supports the product strategy and metrics correspond to the company’s KPIs.
3. Incrementally migrate systems;
- Run parallel pipelines;
- Verify results;
- Stepwise transition.
4. Build design for scaleability;
- Use modular pipelines;
- Reuse components.
5. How Logiciel Can Help
Logiciel uses an AI-first engineering model that integrates observability and lineage; automates pipeline reliability; reduces engineering overhead;
Thereby allowing customers to:
- Scale faster;
- Gain trust in their data;
- Lower operating costs.
Conclusion
It is no longer an option to build a modern data infrastructure for SaaS product analytic; it is a neccessity.
Three points to remember are:
- SaaS data complexity increases very rapidly;
- Infrastructure should scale proactively;
- Without observability and governance, data systems become unreliable;
Incremental implementation wins and finding the right balance between minimal engineering and scaling incrementally.
When implemented correctly, will create faster product driven decisions, gain higher data trust, as well as create a better user experience.
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Call To Action
If you have a high growth SaaS product analytic infrastructure;
I suggest you start by reviewing these two documents;
- Why isn’t my data infrastructure working? Root cause analysis & corrective measures;
- What does the modern data stack look like? Definitions in simple terms for Engineering Managers.
Or take your next step;
👉 Schedule a demo with Logiciel to discuss how Logiciel can help SaaS companies scale their data systems.
Logiciel Solutions has developed an AI-first Data Infrastructure to help Product Managers and Engineering teams build scalable & dependable data systems to support their growth.
Logiciel combines data engineering expertise; intelligent automation; and scalable architectures to easily migrate from disparate systems to high performing data platforms.
Frequently Asked Questions
What is a SaaS modern data infrastructure?
A scalable system that provides real-time access to product data for better & more accurate decision making.
Why do SaaS companies have difficulty with product analytic?
The volume of data, complexity and the requirement for real-time data increases rapidly as the product scales making many simplistic systems inadequate.
How does data observability provide improved product analytics?
By allowing organizations to detect and fix problems while maintaining a culture of data reliability in dashboards and analytics systems.
What are the components of a SaaS modern data infrastructure?
Ingestion, storage, processing, orchestration, and observability are the components of a SaaS Modern Data Infrastructure.
How Should SaaS Companies Get Started?
Start with your most important data flows; build in monitoring; and incrementally scale while ensuring alignment with business objectives.
