Congratulations on completing
the Data and AI Maturity Assessment!
You are at
Stage
What this means
You're at the beginning of your data and AI journey. Systems are disconnected, processes are manual, and data isn't driving decisions yet.
You’ve started building. Maybe you have dashboards or a data warehouse, but things are still fragmented and not standardized.
You’re doing many things right. You’ve got BI dashboards in place and some experience with AI or governance, but gaps still exist in integration, automation, or scale.
You have a mature stack, trusted dashboards, and advanced analytics. Now it’s about scale, performance, and keeping things maintainable.
You’re among the most advanced teams in your industry. AI is deeply embedded. You’re setting benchmarks, not following them.
Where you are
Level
Data infrastructure
Data modeling & quality
BI & dashboards
Predictive & prescriptive analytics
Governance
AI applications
Level 1
Level 2
Level 3
Level 4
Level 5
How you can advance to the next tier
Data infrastructure
At a maturity score of 10, your startup is likely experiencing a lack of structure and organization in your data infrastructure. This can lead to difficulty in accessing and utilizing data, causing delays and inefficiencies in decision-making processes.
Here’s what we recommend you do next:
- Perform a data audit to identify any gaps or inconsistencies in your current data infrastructure.
- Create a data governance plan to establish clear guidelines and processes for managing and using data.
- Implement data quality assurance measures to ensure the accuracy and reliability of your data.
- Invest in training for your team to improve their understanding and utilization of data within the company.
Modeling & data quality
Your score for Modeling & Data Quality is currently at 10, indicating a high level of maturity in this area.
Here’s what we recommend you do next:
- Review and update your current data models to ensure they accurately reflect your business needs.
- Implement a regular data quality checking process to identify and address any inconsistencies or errors in your data.
- Establish and maintain a standardized data schema to ensure consistency and improve data hygiene.
- Consider investing in automated data quality tools or hiring a dedicated data quality specialist to support these efforts.
BI & dashboards
As a senior data strategist, I can see that the Seed startup is at a high level of maturity with a score of 10 for BI & Dashboards. Though their score is high, they may still be facing some challenges in their dashboards such as disorganization, unreliable metrics, or outdated reports.
Here's what we recommend you do next:
- Focus on organizing and cleaning up your dashboards to make them easier to navigate and understand.
- Clarify the metrics you are using and ensure they are accurate and reliable.
- Work on building trust with your users by providing clear and transparent reporting.
- Regularly review and update your dashboards to keep them relevant and useful for your team.
Predictive analytics
Here's where you are right now:
- Ideas for Machine Learning use cases
- Notebooks created, but no production impact yet
Here's what we recommend you do next:
- Identify a specific business problem or opportunity that can benefit from predictive analytics
- Collect and explore relevant data to understand patterns and relationships
- Develop a simple prototype model using tools like Excel, Tableau, or Google Sheets
- Test and validate the model with real data and adjust as needed
Governance
As a senior data strategist, I have to say, your governance is a bit of a mess right now. Access control seems to be all over the place, ownership is not clearly defined, and data is just floating around without any visibility.
Here’s what we recommend you do next:
- Implement strict access control measures to ensure only authorized individuals have access to sensitive data.
- Clearly define ownership of data and ensure that all employees are aware of their responsibilities.
- Improve visibility of data by implementing a tagging system and regularly reviewing data usage.
- Avoid getting caught up in abstract policy design or lengthy frameworks and focus on practical actions that can be implemented quickly.
AI applications
Write 2–3 lines describing their AI reality: lots of buzz, no deployed use cases, maybe a prototype no one uses. Keep it grounded and clear.
Here’s what we recommend you do next:
- Develop a clear understanding of your business goals and identify areas where AI can help.
- Start with a small, specific use case that can have a tangible impact on your business.
- Utilize open source tools and resources to build and test your prototype.
- Solicit feedback from your team and potential users to refine and improve your AI solution.
You’ve got clarity.
Now let’s make progress.
You’re probably duct-taping reports together, reacting to fires, and guessing your way through KPIs.
At this stage, velocity matters more than perfection. The challenge is building a usable foundation — without overengineering, overhiring, or overbuying.
How 5X helps:
We give you a plug-and-play data stack, ingestion, modeling, dashboards, and governance in one platform. Our experts build it with you, so you’re not stuck duct-taping metrics across tools that don’t talk to each other.
You’ve got pieces of the puzzle — a warehouse, maybe some dashboards — but it’s held together with best guesses and broken SQL. Every new request is a one-off build.
How 5X helps:
We help you consolidate the warehouse, model core business metrics, and introduce QA standards that don’t require new headcount. Your team gets answers they can trust, and you get out of the spreadsheet firefighting loop.
You’ve got dashboards, pipelines, models… but they’re not always trusted or actioned. Business teams want more, faster, and your data team’s burning out just trying to keep up.
How 5X helps:
We bring structure: tested governance frameworks, automated observability, and faster deployment paths for models and dashboards. You move from reactive support to proactive enablement without increasing your engineering footprint.
Your data team ships — but duplication, compute waste, and rogue dashboards are slowing things down. Your execs want smarter automation, but engineering cycles are stretched.
How 5X helps:
We help you clean it up by codifying logic, reducing compute spend, and building internal tools like smart alerts and AI-powered workflows. Your infra supports scale without spiraling.
You’re already running structured experiments, internal tools, and ML in prod. The question now is: how do you 10x your leverage without slowing down?
How 5X helps:
We help you co-build GenAI copilots, reusable ML modules, and internal accelerators tailored to your business DNA without duplicating work or slowing down compliance and governance.
Use this report as a strategic input
This report is designed for teams who want to move forward, not just score themselves. It’s built to complement your existing goals, and help anchor planning discussions with your leadership team. Use it as a clear, thoughtful guide to shape priorities across data infrastructure, analytics, and AI readiness.
Share your score on LinkedIn and we’ll include your org in our upcoming 2025 Industry Benchmark Report—a comparative view of how companies at your stage and scale are evolving their data & AI capabilities.
