Congratulations on completing
the Data and AI Maturity Assessment!
You are at
Operational
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
Data Infrastructure
The company is likely experiencing some growing pains with their current data infrastructure score of 8. They may be facing bottlenecks and inefficiencies in their data processes, causing delays and errors in their data analysis. This could lead to missed opportunities and hinder their ability to make data-driven decisions.
Here’s what we recommend you do next:
- Implement a data governance framework to ensure data quality and consistency across the company.
- Invest in automation tools to streamline data processes and reduce manual errors.
- Conduct a thorough data audit to identify any gaps or areas for improvement in your current infrastructure.
- Train and upskill your data team to ensure they have the necessary skills to effectively manage and utilize your data infrastructure.
Modeling & data quality
Modeling & Data Quality
The current state of your data modeling and quality is quite advanced with a maturity score of 8. However, there are still some areas that could use improvement to ensure the reliability and accuracy of your data.
Here’s what we recommend you do next:
- Continue to focus on table-level modeling to ensure consistency and accuracy in your data.
- Make sure to regularly check and maintain metric consistency to ensure the reliability of your data insights.
- Pay attention to schema hygiene to avoid any data integrity issues and maintain the quality of your data.
- Utilize your limited time and team size to prioritize and address any immediate data modeling and quality concerns.
BI & dashboards
BI & Dashboards
There may be some areas of improvement in your current dashboards, such as clutter, unreliable metrics, or outdated reports.
Here’s what we recommend you do next:
- Streamline your dashboards by removing unnecessary elements and organizing them in a user-friendly manner.
- Ensure that the metrics used are accurate and easily understandable for users.
- Earn trust from users by regularly updating and maintaining your reports.
Predictive analytics
Predictive Analytics
Their maturity score for Predictive Analytics is 8, which indicates that they have some ideas for ML use cases and may have even created some notebooks, but they have not yet been able to implement any predictive models into production.
Here’s what we recommend you do next:
- 1. Identify potential use cases that can provide the most immediate business value.
- 2. Collect and clean relevant data for the selected use case.
- 3. Use simple tools like Excel or Google Sheets to perform basic analysis and visualization on the data.
- 4. Test and validate the use case by building a simple predictive model using open source tools like scikit-learn or TensorFlow.
Governance
Governance
The current state of governance for your company is not ideal. Access to data is messy and there is no clear ownership or control over it. Shadow data is also likely present, making it difficult to track and manage.
Here's what we recommend you do next:
- Implement strict access control measures to ensure only authorized personnel have access to sensitive data.
- Establish clear ownership tagging for all data to avoid confusion and ensure accountability.
- Improve visibility of data by implementing proper data management tools and processes.
- Avoid creating abstract policies or long frameworks, instead focus on practical actions that can be implemented quickly.
AI applications
AI Applications
Lots of buzz, but no deployed use cases. There may be a prototype, but it is not being used by anyone. It's time to turn the hype into reality.
Here’s what we recommend you do next:
- Identify a specific business problem that can be solved using AI and gather data related to it.
- Build a simple AI model using the available data and test it with a small group of users.
- Collect feedback from users and refine the model accordingly.
- Deploy the model in a small-scale internal use case and measure its impact.
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.
