You’ve successfully launched NetSuite Analytics Warehouse (NSAW). Your dashboards are live, subject areas are customized, and users are actively logging in. Even better, you’ve eliminated manual spreadsheets and hours of exporting saved searches from finance and operations. That’s a major achievement.
But now you’re wondering: What’s next?
How do you move beyond basic dashboards and unlock the real power of NSAW?
You’re not alone. This is exactly where many organizations plateau.
To truly maximize NSAW (or any analytics platform), you need to recognize that analytics evolves in stages. Each layer builds upon the previous one. Once you understand this progression, you can align analytics maturity with business goals and create a clear roadmap forward.
The Four Stages of Analytics
Think of analytics as a staircase. You can’t skip steps; each one strengthens the next.
Descriptive Analytics – What happened?
This is the starting point for every organization. You’re reviewing historical performance:
Sales by month
Inventory on hand
Margins by product line
Revenue trends over five years
Examples in NSAW:
Analysing five years of revenue data.
Identifying that certain months consistently outperform others.
Descriptive analytics gives visibility. It tells you what occurred, but it doesn’t explain why.
Diagnostic Analytics – Why did it happen?
Now you begin connecting the dots. Instead of just viewing numbers, you uncover causes.
Examples in NSAW:
June sales spike? Overlay marketing campaigns and discover summer promotions drive the increase.
Snowblowers sell in winter, patio furniture peaks in spring.
Supplier lead times extend during holiday months.
Diagnostic analytics transforms raw data into insights. It adds context to the story your numbers are telling.
Predictive Analytics – What is likely to happen next?
This is where analytics becomes forward-looking.
Once you understand historical patterns and their drivers, forecasting becomes possible.
Examples in NSAW:
Predicting a 40% increase in product sales next June based on seasonality.
Identifying that vendor lead times stretch from 28 to 45 days during holidays.
Spotting repeatable demand cycles across seasons.
Predictive analytics enables smarter planning, but it only works when your descriptive and diagnostic foundations are strong.
Important note:
While machine learning and AI can automate parts of predictive modelling, they still rely heavily on clean data and validated drivers. Diagnostics never truly disappear they just evolve.
Prescriptive Analytics – What should we do about it?
This is the ultimate goal.
Now you’re not just reporting or forecasting, you’re driving action.
Examples in NSAW:
Ordering inventory in April to prepare for June demand spikes.
Building automated reorder models based on lead time and forecasted sales.
Setting stock-out alerts to notify buyers before shortages occur.
Prescriptive analytics converts insight into decision-making power.
So, What Should You Do Next?
A practical way forward is to run a structured workshop with your team.
Step 1: Identify Your “Black Boxes”
Ask:
“Where do we lack visibility?”
Example:
“We don’t truly know inventory levels until someone checks manually.”
This signals a need to strengthen descriptive analytics and provide better real-time visibility into stock, allocations, and work orders.
Step 2: Uncover the Unpredictable Areas
Ask:
“What feels inconsistent or misunderstood?”
Example:
“We never know why we run out of certain items.”
This belongs in the diagnostic layer, understanding seasonality, supplier delays, or purchasing patterns.
Step 3: Define the Big Opportunity
Ask:
“Wouldn’t it be great if we could predict stock-outs before they happen?”
Before jumping into forecasting, confirm:
Do we have accurate real-time inventory data?
Do we understand vendor lead time trends?
Do we know whether seasonality consistently affects demand?
If not, strengthen your foundation first.
The Key Takeaway
Organizations often chase advanced forecasting too quickly. But predictive and prescriptive analytics only deliver value when built on reliable descriptive and diagnostic layers.
By progressing step by step:
Gain visibility.
Understand the drivers.
Forecast confidently.
Automate smarter decisions.
That structured approach is what turns NSAW from a reporting tool into a strategic decision engine.
