If your business stakeholders hunger for insights but find enterprise-wide reporting and analytics don‘t fully satisfy their appetite, data marts may be the right ingredient. These purpose-built data stores can whip up understanding 3x faster in focused domains.
In this comprehensive guide, I‘ll showcase everything you need to successfully build, utilize and manage data marts. With the right approach, these systems cultivate decision-making superpowers within individual teams.
What is a Data Mart and How is it Different from a Data Warehouse?
Think of a data warehouse as a big bowl containing all ingredients from across your organization. It houses financials, sales, inventory, logistics – data coming together into one place. Great for whipping up company-wide reporting. But specialized needs get tougher to address.
For example, what if the marketing team wants more complex analysis on campaigns, pipelines and social engagement? Blending this wider context still, but focused specifically on their priorities?
That‘s where data marts shine! Purpose-built from the ground up for a line of business, small yet powerful. No wasted ingredients diluting the flavor. A custom recipe crafted to the preferences of its users – your marketing master chefs!
Here‘s how data marts differ from the mothership enterprise data warehouse:
Specialized data – Only relevant to the department, not the whole organization. Makes querying faster and analysis more targeted.
Focused scope – Meant for a single team, not company-wide sharing. Security and performance tune to their needs.
Tailored design – Customized schemas, tools and visualizations matching user preferences rather than one-size-fits-all.
Cost effective – No need to pay for full enterprise scale hardware and infrastructure upfront if starting small.
Quicker insights – Value delivered in weeks or months instead of multi-year programs.
In summary, data marts unlock analytics capabilities beyond the broad corporate data dictionary. Options exist for blending some shared dimensions too. But the accent stays on the needs of its departmental consumers.
Now let‘s explore common architectures and structural styles adopted by these data marts.
Three Primary Data Mart Styles
While sharing similar base ingredients, how data fills a mart can impact its nutritional value. Three primary designs stand out:
1. Dependent Data Mart
The dependent model draws nourishment directly from the centralized data warehouse itself. Finance may opt for this route to enrich their general ledger and budget planning data with sales, inventory or procurement context already collected enterprise-wide.
Pros 👍
- Consistent data definitions and business logic across systems
- Re-uses existing ETL and data models reducing duplication
- Central governance minimizes access control risks
Cons 👎
- Enterprise data warehouse performance issues slow data mart users
- Changes to warehouse schema require data mart re-work
2. Independent Data Mart
If preferring full control over ingredients and prep, an independent data mart is the choice. The e-commerce department may take this path to customize web sales, customer attribute and behavioral data analysis combining data from their transactional databases, BigQuery event logs and 3rd party signals.
Pros 👍
- Flexible to meet our specific analysis and data needs
- Not limited by centralized schedules and governance
Cons 👎
- No visibility into data relationships across other groups
- Higher costs recreating ETL and storage from scratch
3. Hybrid Data Mart
Hybrid data marts allow some ingredients pre-prepped in the central warehouse while directly sourcing other raw materials. Product analytics may take this approach – enriching enterprise dimensions like customer, sales districts and fiscal calendars with integrated device telemetry, quality test data and even social review sentiment signals unique to their needs.
Pros 👍
- Get value from existing cleansed enterprise data
- Still customizable to our niche needs
Cons 👎
- Some dependencies on central data teams for source system changes
- Data blending carries some incremental complexity
Based on culture, priorities and risk tolerance – organizations often standardize on one style for consistency. But the door remains open for teams like e-commerce or product engineers who benefit more from isolated customization.
These data mart styles set foundation and boundaries. Next let‘s explore the full process for constructing your kitchens.
Steps to Implement a Data Mart Solution
Building out a fully-functional, user-approved data mart entails several distinct phases:
The ingredients and prep work vary. But most real-world projects follow this general recipe getting from raw data to actionable insights.
🥣 1. Design & Plan the Kitchen
First, clearly define the guest list, menu priorities and meal timelines upfront:
Gather requirements – Number of food critics served? Specific KPI nutrition goals? Expected degustation volumes? Double confirm by surveying your users early.
Source ingredients – What originating systems hold the proteins, spices and produce needed for your planned signature dish analysis?
Model recipes – Map out complete seasonings, preparation steps, cooking equipment and servings needed.
Construct venue and layout – What foundation and furnishings supports the catering planned? Appliances, countertops, ventilation for success factors like security and performance.
Thorough planning and designing up front means our resulting gourmet analysis and insights better delight patrons once doors open.
🍳 2. Build Test Kitchen Infrastructure
With intended menus and venue selected, now construct the molecular gastronomy lab:
Acquire kitchen tools – Procure necessary hardware equipment per budgets – pots, pans, ventilation hoods.
Install ovens and ranges – Construct database instances on SQL Server, Oracle or PostgreSQL aligned to scale and performance recipes.
Construct plumbing – Implement ETL and data pipeline tools like Informatica, SSIS or Talend based on complexity factors.
Create pantry storage – Tables, indexes, partitions. Store commonly accessed ingredients ready for rapid preparation.
Once built, test sample customers seasonings, recipes and service cadence before full restaurant launch!
🥗 3. Gather & Prepare Ingredients
With a fully operational test kitchen, we‘re ready to source select ingredients:
Procure ingredients – Extract and gather relevant data from identified transaction systems, files, event streams per the collection plan.
Wash produce – Resolve inconsistencies, deduplicate records, normalize formats.
Cut, slice, dice – Apply business logic rules and data transformations needed to ready materials.
Store finished items – Load the consolidated, prepared datasets into tables and specialty appliances accordingly.
Start with high value ingredients and dishes to showcase possibilities before expanding the menu breadth.
🍽️ 4. Open Doors to Customers
With tasting and plating finalized, open up to hungry data users across the business:
Print menus – Documentation and usage guidelines tailored to various customer sophistication levels.
Setup place settings – Table views, security profiles, filtered perspectives.
Train waiters – Demo analysis examples, visualizations and self-service features to patrons.
The goal is seamless dining where data is delicious, accessible and consumers become fans rather than needing white glove guidance.
🧺 5. Maintain & Enhance Over Time
Post-launch, meet customer special requests for new flavors while optimizing kitchen operations:
Adapt seating – New guest data needs may require expanding bar stool or lounge chair capacity.
Tune equipment – Address noisy fridge compressors; optimize oven rack spacing; replace dulled knife blades degrading prep speeds.
Refine sauces – Customer tastes evolve, adapt seasoning and emulsions accordingly.
Introduce seasonal flavors – Welcome new ingredients like IoT sensor feeds or emerging online favorability signals.
Continuous tuning and refinement helps our data mart solution become an enduring destination versus short-lived food fad.
Key Design Principles for Satisfied Customers
Beyond the core workflow, adopting these fundamental principles sets up data marts for repeat customer success:
Start small, deliver fast – Focus first on the minimum viable lifestyle restaurant. Get patrons familiar with top stating dishes quickly.
Plan for growth – Ensure kitchens allow expanded menus, more covers, seasonal demand surges.
Fail fast with test data – Experiment with cloned datasets and test runs extensively first before public guests.
Automate tasks – Script database build and deploy processes. Monitoring checks versus manual reviews.
Adhering to these operating mantras smooths adoption while minimizing lifetime TCO and security vulnerabilities even as new customers continually walk through the doors.
Next let‘s examine a real-world data mart rollout following these patterns.
Implementing the ACME Marketing Data Mart
ACME Corporation has a strong appetite for analytics, but their enterprise Oracle data warehouse caters more to financial planning use cases. Standardized sales, budget and product data feeds systems company-wide.
Marketing however needs guidance on campaigns, web traffic, lead management and social engagement too. Blending this wider context focused specifically on their specific brand building priorities is the goal.
After discussions, IT determines that a hybrid data mart approach allows rapid value delivery while keeping future expansion options open:
Marketing provides their wishlist of key insights needed. IT sketches out blueprints melding these essential ingredients:
- Campaign ROI – Blending marketing qualified lead (MQL) pipelines with cost and revenue data
- Web analytics – Clickstreams, conversions, multi-touch attribution
- Social data – Post reach, engagement, follower sentiment factors
- Customer dimensions – Product line, lead source and persona attributes
With priorities set, IT architects the kitchen:
- Appliances: Leverage existing SQL Server capacity for faster time-to-value given internal deployment skills
- Extractors: SQL Server SSIS ETL tools consolidate data from multi-cloud sources
- Data Prep: Standardize schemas, associate identities, transform metrics
- Service Style: Enable self-service analysis in Power BI with pre-built visualizations and examples
In just 10 weeks, the hybrid data mart enters active duty – blending customized social and web data feeds with shared customer and sales dimensions from the EDW. Marketing analysts gain accelerated insights to optimize spending and tactics.
Post-launch, the data mart expands to additional teams needing these perspectives. Event and brand teams also now benefit from tailored ingredient melds unfeasible within the centralized Oracle warehouse.
Key Takeaways
For organizations seeking to marinate departments in richer insights beyond one-size-fits all reporting, data marts unlock potential. Blending existing enterprise data with other more targeted or emerging signals creates specialty analysis possibilities.
Here are the top lessons for getting started:
- Data marts fill analytical gaps beyond broad enterprise reporting
- Modern architectures allow custom or hybrid data integration
- Follow structured blueprint from planning to maintenance
- Design for security, extensibility and growth from day one
- Monitor adoption and continually refine to needs
The journey to embedding analytics starts small but with business priority focused vision. Data marts built iteratively at the department level pave the way through demonstrated quick wins.
Once groups gain comfort relying on these insights daily to accelerate decisions, optimism grows on scaling to enterprise-level over time. Positive momentum germinates through evidence versus edict.
Now that you‘ve got the full menu and recipes, start carving out your own delicious data mart driven future!
