10 10 10 10 15 20

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

Nascent
Siloed systems, spreadsheets, no automation
No standards, inconsistent use
Static reports, limited usage
Only historical data, no predictions
No roles or policies in place
No AI adoption at all

Level 2

Developing
Basic warehouse, manual integration
Some modeling, mostly undocumented
A few dashboards, low adoption
Some diagnostic insights, no ML
Informal policies, not enforced
Experimental pilots, no value yet

Level 3

Operational
Centralized storage, partial automation
Key domains modeled, basic QA
KPIs tracked regularly across teams
Early ML pilots, some usage
Governance roles defined, limited scope
AI in limited production use

Level 4

Advanced
Scalable cloud platform, most sources integrated
Standardized models, documentation in place
Interactive, real-time dashboards used broadly
ML supports many decisions, some automation
Active governance body, policies enforced
Multiple AI apps delivering business impact

Level 5

Leading
Unified, real-time platform with enterprise coverage
Fully governed catalog, automated validation
Predictive dashboards with alerts and drill-downs
Prescriptive AI embedded in workflows
Governance embedded in org culture, with tracking
AI powers products, ops, and innovation at scale

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.
Download the Guide to Data & AI Maturity

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.

Get real dashboards


Skip the 6-month rebuild


Build once, scale with it

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.

Unified metrics

QA workflows without heavy process

Dashboards your team will actually use

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.

Faster model deployment

Clean governance that doesn’t slow teams down

Clear ROI from your data ops

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.

MLOps, done right

Cost optimization across infra

Internal tools that drive ops efficiency

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.

GenAI copilots, custom to your workflows


AI/ML ops at scale


Experimental frameworks that drive speed and safety

Book a strategy session

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.

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