A data warehouse functions as a specialized type of database, serving as a single source of truth to store large volumes of data from multiple sources for analysis and reporting purposes.

Some potential benefits of analyzing the data in your data warehouse include improvements related to:

Decision Making: enables data-driven decisions with timely, accurate, and comprehensive insights.
Analysis: enables complex queries and historical analysis to uncover trends and insights.
Quality: standardizes and cleanses data within the database to improve accuracy and reliability.
Consistency: ensures uniform data definitions and formats across the organization.
Performance: optimized for fast query processing even with large volumes of data.
Accessibility: centralizes data from multiple sources, making it easier for users to access and query information.
Security: provides robust access controls and protects sensitive data in one place.
Integration: ingest data easily with BI Tools (e.g. Microsoft Power BI, Tableau, etc…).

Analyze the data to gain insights

By analyzing historical data and trends, you can gain insights such as:

identify patterns for appointment cancellations
identify patterns for item return / exchange,
plan a change in a supply chain,
plan a change to your inventory management process,
make predictions about sales revenue,
make predictions about customer needs,
make predictions about required appointments per day,
make decisions about new product launches,
make decisions about pricing strategies,
plan events at a specific location for marketing,
inform go-to-market strategies

Common KPIs to track

 Each business has its own set of preferred KPIs to track. KPIs can be viewed from different perspectives. Such as, by store, by region, by sales reps, by time period, etc.

Some common KPIs:

Inventory
Revenue to Budget
Revenue over time
Return Rate
Average deal value
Completed / Cancelled / No Show Appointment
Deal/Quote to Sale conversion rate
Non-delivered Orders
Products running low
Products on high demand
User inactivation per day / week / month / year
Qualified leads
Campaign ROI
Order fulfillment cycle time
Inventory Turnover

Common types of analysis leveraging a data warehouse

A data warehouse isn’t just a database for storing data; it’s a platform for unlocking insights. By consolidating information from across the business, it becomes the foundation for powerful, reliable analysis.

Whether you’re tracking sales performance, understanding customer behavior, or forecasting demand, the data warehouse ensures you’re working with clean, consistent, and up-to-date information.

Some common types of data warehousing data analysis:

Type of analysis	Descriptive Analysis
What it does	Summarizes historical data to understand what happened in the business over time.
Typical difficulty level	Easy
What’s typically involved	Typically done through SQL or built-in BI dashboards.
Example KPIs / insights gained	Revenue (daily / weekly / monthly), Average sale price

Type of analysis	Diagnostic Analysis
What it does	Determines the reasons behind past outcomes. Drills deeper than descriptive analysis to uncover the cause of trends.
Typical difficulty level	Easy
What’s typically involved	Requires dimensional modeling, strong joins, and data consistency across sources.
Example KPIs / insights gained	Actual vs Budget by cost center, Revenue variance by price, volume, mix

Type of analysis	Predictive Analysis
What it does	Used to forecast future outcomes based on historical patterns and trends.
Typical difficulty level	Hard
What’s typically involved	Requires data science tools and pipelines beyond SQL (integration with Python, Spark, or AutoML platforms).
Example KPIs / insights gained	Revenue forecast using appointment scheduled and appointment completed

Type of analysis	Prescriptive Analysis
What it does	Recommends actions by analyzing potential outcomes of various scenarios.
Typical difficulty level	Hard
What’s typically involved	Requires simulation tools, optimization engines, and possibly real-time feedback loops.
Example KPIs / insights gained	Budget reallocation plan, Profit-maximizing price

Type of analysis	Exploratory Data Analysis
What it does	Discovers patterns, relationships, or anomalies in the data without having a specific question in mind.
Typical difficulty level	Moderate
What’s typically involved	Flexible schema and accessible tools make this easier if the data model is intuitive.
Example KPIs / insights gained	Isolation-forest scores, Missing-value heatmap

Type of analysis	Operational (Real-Time) Analysis
What it does	Used to monitor and analyze ongoing processes and operations using real-time or near-real-time data.
Typical difficulty level	Hard
What’s typically involved	Requires low-latency ingestion, real-time query capabilities, and alerting mechanisms.
Example KPIs / insights gained	Orders-per-minute, Order-book imbalance ratio

Steps to add new analysis functionality to your data warehouse

As your organization matures in its use of data, the need for deeper, more flexible analysis grows, whether that includes:

building predictive models,
enabling real-time dashboards,
or adding new business metrics.

These steps will help ensure your data warehousing platform remains scalable, relevant, and aligned with evolving business goals.

1. Understand the business need (i.e. meet with marketing, sales, etc. teams to understand what they want).
2. Identify KPIs.
3. Define the KPI logic based on the source data and business need.
4. Identify the fact and dimension tables based on the requirements.
5. Write logic to transform the source data to fact and dimension tables.
6. Once data is transformed and ready, use a BI (Business intelligence) tool to put it in front of the business users.
7. Continue to monitor the data and data warehouse performance for improvements.

What makes analyses easier to accomplish?

Structured and clean data – If you already have cleaned data, calculating a new KPI like is often just a new query.
Use of a BI tool (e.g. Microsoft PowerBI, Tableau) – This allows rapid prototyping and sharing of new dashboards or reports.
 Leveraging existing historical data and time-series trends to create new KPIs
Scalable Infrastructure – No waiting on IT for servers or capacity. Just add and analyze.
Reusable Data Models and Views – You can layer new metrics on top of existing transformations without rebuilding pipelines.

What aspects of adding new analyses can be particularly challenging?

Data Availability and Integration – Adding new metrics from a third-party tool may require new ETL pipelines and cleaning logic.
Complex Data Modeling – joining data with existing data models can be tricky without careful modeling.
Governance and Validation – Without strong governance, you risk conflicting metrics and loss of trust in reporting.
Performance Optimization – This might require materialized views, aggregate tables, or query tuning to maintain dashboard responsiveness.
Stakeholder Alignment – A technically correct report isn’t useful if it doesn’t answer the right business question.

Data warehousing analysis gotchas

Even with a powerful data warehouse and a strong analytics team, there are common pitfalls that can derail accuracy, performance, and trust in your insights. These gotchas often emerge as organizations scale their analytics or start layering in more advanced functionality.

Misalignment between business requirements and data design

Example: the sales team needs net revenue by product after returns, but the warehouse only stores sales in a fact table with no return adjustments. Dashboards show healthy growth until the team finds the issue.

Assuming data quality without verification

Example: a developer points a report at the customer_email column, assuming it’s unique. A quick uniqueness test later reveals duplicates created by a client. Counts were overstated for a long time.

Not understanding the complexity of historical data

Example: pricing analysts back-fill a new price column by looking at catalog price, unaware that prices fluctuate daily. Historical queries now show current prices for last year’s orders, inflating revenue by a significant amount in comparison.

Ignoring the data lineage and transformation logic

Example: an ETL process is already doing a calculation for counting a quantity based on a service condition. A manager who is unaware of that change also manually updated the quantity and uploaded it to the system. Because of this, the quantity is now calculated twice.

Mixing the granularity in metrics

Example: a dashboard showing daily trading P&L by desk, then joins monthly operating-expense data from the general ledger to produce Profit Margin. Early in the month the expense denominator is near zero, so margins appear off-the-chart positive; by month-end they crater. The manager misreads the volatility and freezes headcount just before a revenue surge.

Underestimating performance and query costs

Example: analysts love an interactive SQL notebook that scans the 4-year clickstream fact table (3 TB each run). Cloud-warehouse credits burn ~$15 K in a week; queries slow as slots max out, and other teams’ nightly jobs miss their SLA.

Neglecting data security, data governance

Example: developers grant SELECT * on the financial schema to the whole analytics group. A few months later, a download of payroll data shows information that the user shouldn’t have had access to.

Performance optimization

Example: a sales widget refreshes every 15 seconds and runs a select * query on a big fact table with no filters (No select list trimming, No date condition). Cloud warehouse autoscaling spins up more nodes. This approach will reduce performance and cost money.

Access control

Example: there’s an export button to download row data, with no proper access control. Everyone will be able to download all data with no filter on it.

Overcomplicating the solution

Example: a developer builds a complex pipeline to ingest a 10 MB CSV file that’s created once a day. The maintenance overhead dwarfs the benefit; a simple storage account drops and COPY INTO would have easily solved this.

Unclear Metric Definitions

Example: the no-show rate for a management report shows 20% (With correct logic) and the Clinic dashboard shows 12% because it’s not considering rescheduled appointments.

How long does it take to set up new analyses and how much does it cost (in 2025)?

Assuming you’ve already got a data warehouse set up, it generally takes about a few weeks to several months to add a new analysis.

Primary drivers include:

1. the type of analysis to be performed,
2. the business data size,
3. and sources needed to extract the data from.

A few scenarios:

Scenario 1 –

Business objective	Integrate a new data source for existing metrics
The work it entails	A new data source needs to be added with different schema and a different source type (with csv files, MS SQL DB or MySQL DB already exist). We need to add this to existing appointment and revenue-related metrics.
Duration estimate	~1 month
Cost estimate	~ $15,000 – $25,000

Scenario 2 –

Business objective	You need a simple metric using existing data. Let’s assume you want to add a Repeat Purchase Rate metric to an existing sales dashboard.
The work it entails	You only need to make a calculation and add a new metric to your dashboard because the data already exists, and no changes are needed in your data warehouse process.
Duration estimate	~3 to 5 days
Cost estimate	~ $4,000 – $7,000

Scenario 3 –

Business objective	Add new metric: Customer No-Show Rate by Store and Event Type.
The work it entails	This will be a new analysis using existing tables with moderate logic change. You already have customer, event and store data. You need to decide the logic for no-show and make the change to your existing data warehouse.
Duration estimate	~1 to 2 weeks
Cost estimate	~ $10,000 – $15,000

Scenario 4 –

Business objective	You need a new metric for which you only have partial data available in your data warehouse. In this case, you are trying to identify resource usage vs availability to spot under-utilized resources (staff, offices, seats etc.)
The work it entails	You already have resource data, but you need to make a change in your data warehouse process to add resource schedule data. And you need to define the required logic based on availability and schedule.
Duration estimate	~1.5 to 2 months
Cost estimate	~ $25,000 – $45,000

Next Steps / How can I get started?

If you need help adding new analyses (tracking new KPIs, creating custom reports) to your data warehouse, feel free to reach out and we can discuss the following topics:

Analysis requirements
Reporting requirements
How much time will it take
How much it will cost