Writing about data warehouse software reminded me of a YouTube reel with a guy sprinting around carrying a tray of full glasses. My anxiety built as he kept adding more, and before too long — disaster!
Like the runner, a data warehouse has a lot on its plate, and so do you as the procurement manager for your organization. This article provides a handy data warehouse requirements gathering checklist to help you select the right software.
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Data Warehouse Requirements Checklist
Drive standard definitions and queries and enable data manipulation.
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Set the tone for quality analysis with accurate, reliable metrics.
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Use data seamlessly with workflow design, modeling and governance support.
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Get more from raw data with joins, functions, filters and statistical analysis.
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Leave no data behind. Utilize insight from all available sources, including cloud and streaming systems.
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Automate processing and generate insights using AI-ML. Separate analytical and operational workloads without missing a beat.
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Key Requirements
Here’s the features list recommended by our analyst team to fine-tune your search.
1. SQL Support
The Standard Query Language is how you derive meaningful insight from raw information in relational databases. Most warehouses are SQL-based as it’s versatile and built to make pipelines run faster.
SQL is the backbone of the warehouse for querying and setting business rules. Source
It has powerful functions and supports drag-and-drop querying. Moreover, it helps integrate your warehouse with BI tools. SQL is behind every action you take on data, be it correcting data types, removing duplicates, or selecting datasets for querying.
Indexing and partitioning are techniques that improve warehouse performance. Indexing creates special lookup tables to load and retrieve information faster. Partitioning allows dividing tables and indexes into smaller, manageable pieces.
Compared to operational systems, warehouses have a lot going on. They must adhere to certain rules, ACID being a primary requirement. It means they should maintain information integrity, keep data exclusive, and halt the transaction if even one component fails.
ETL processes can be long-winded, and storing intermediate results in temporary and transient tables is a cost-effective alternative to requesting additional storage, which can cost you a tidy sum.
Physically storing data isn’t always undesirable — materialized views store query results for quick retrieval.
Querying hierarchical data is complicated. Calculating sales by region and, further, product category requires recursive queries or common table expressions.
SQL supports geometric shapes and geographic features, relying on indexing to improve spatial query performance. GeoJSON compatibility will allow the import/export of location data across systems.
- Standard DDL Support
- DML Operations
- Temporary and Transient Tables
- Materialized Views
- Recursive Queries
- Geospatial Data Support
2. Data Management
If you read my article on BI requirements, you’ll find data management features in that list. If your BI tool has data management, does your warehouse solution need it, too?
Yes, it does. In warehouses, the focus is on data ingestion, storage, structuring and historical data handling. A visual workflow designer will help you set up processes for ETL, data validation and governance.
Consider these use cases for warehouse workflows. Which one do you relate to?
- You can schedule an SQL query for updating tables as new data becomes available. These tables feed dashboards with up-to-date, accurate information. It’s an example of an event-driven workflow.
- Or you can set up a workflow to prepare data, run a SQL query and execute a model training code. And just like that, you have a data model ready to go!
- Batch processing workflows perform nightly aggregation and routine tasks like report refreshes and data archiving.
Empower your universal searches with a data catalog. Source
Pre-defined code, real-time source connections and parallel processing are nice-to-have features. Track how teams use the warehouse platform with execution logs and performance dashboards.
Data governance often takes a backseat in supporting technologies like warehouses.
According to Nate LaFerle, Principal at Remisphere Digital,
Companies invest a lot in securing their operational systems – but the data in the warehouse is just as sensitive, except that it lacks built-in controls and accountable data owners within the business. A user who can only see data from certain business units or plants in the operational system should only be able to access the same data in the data warehouse. Those controls are not automatic.
Ask your vendor if the platform allows configuring governance during ETL. Quite a lot depends on your analytics engineer. Once you’ve deployed the warehouse instance, defining access, retention and archiving protocols is their responsibility.
Their task begins much earlier. Establishing data structure using logical models and defining schemas is their job.
Most warehouses have a star schema for easy data access.
It consists of a central fact table surrounded by multiple dimension tables. The fact table contains numerical measures, such as sales, revenue or website traffic. Dimension tables contain descriptive attributes, such as product, customer, time and location.
Metadata has information about all the data within your warehouse in granular detail, such as its structure, format, quality and relationship with other datasets.
- Workflow Designer
- Visual Querying
- Data Modeling
- Data Blending
- Live Connection
- Metadata Management
- Data Governance
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3. Data Preprocessing
If theater performers aren’t ready before the curtain rises, there’ll be no show. That’s what preprocessing is — cleansing, organizing and exploring data to derive business insight.
Validation checks ensure that data adheres to the desired format, numerical values fall within a specified range, fields aren’t empty and entries are unique.
A data catalog is a detailed asset inventory in a warehouse. It enhances discoverability — data is easier to find — and understanding of how datasets relate. Think of it as a GPS, helping you pinpoint specific datasets quickly and efficiently.
Profiling identifies missing values and outliers, and data lineage tracks your data’s antecedents, establishing credibility and trust.
The best time to catch dirty data is during ETL.
According to LaFerle:
Organizations often overlook the business processes that contribute to bad data quality and instead focus on cleaning up bad data once it’s already in their systems. This allows data issues to flow to the BI platform before they can be fixed, and this can undermine trust in the solution. You need to develop a holistic strategy to prevent the most common issues from happening in the first place while proactively intercepting anything that slips through the cracks — before it makes it into (an) analytics solution.
Warehouse solutions don’t provide data quality warnings but they support ETL processes that trigger these alerts after encountering discrepancies.
- Data Exploration
- Data Cleansing
- Data Catalog
- Data Profiling
- Data Lineage
- Data Validation
- Data Quality Warnings
4. Data Transformation
Loading data to the warehouse is just the start. ETL/ELT includes transformation, which is when aggregations, joins and lookups help you associate and enrich datasets.
Aggregate functions summarize large datasets to reveal hidden patterns and trends. Joins merge data from different sources to help you understand complex associations. Lookup functions enrich query results by adding information from other tables.
String functions manipulate text data within a dataset, and filter functions improve performance by reducing the amount of analyzed data.
Statistical functions support advanced analytics by introducing probability calculations, helping businesses make far-reaching decisions.
- Aggregate Functions
- Join Functions
- Lookup Functions
- Filter Functions
- String Functions
- Statistical Functions
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5. Data Connectivity and Sharing
Your warehouse should be open to querying by leading SQL systems.
Access the power of your data warehouse with SQL Server. Source
support data sharing across systems, networks and geographical boundaries, especially if your business spans counties or continents.
Bulk loading is an efficient data transfer method that allows organizations to handle substantial datasets effectively. It’s faster than traditional row-by-row insertion methods.
Incremental uploads are lighter on the system than full updates, but they need careful management to ensure data accuracy and integrity.
Bulk data unloading — transferring heavy volumes of data back to files and analytics systems — is equally useful, enabling data migration, archiving and analysis.
Ask vendors if their warehouse solution supports CSV, JSON and Parquet formats.
While CSV is a popular format for bulk loading due to its simplicity and ease of use, JSON is useful for dealing with unstructured data and hierarchical fields. Parquet is a columnar storage format optimized for analytics.
While bulk loading has its uses, streaming data ingestion is great for customer personalization, IoT analytics and fraud detection in banking, financial services and insurance. Modern warehouses are versatile and can ingest bulk loads as well as streaming data.
Transferring data in the cloud requires proper guardrails — security should be watertight, systems should be compatible and protocols must adhere to security regulations. They should be part of your data warehouse business requirements.
Seamless data sharing across regions will maintain your team’s productivity and make them feel as if they’re sitting in the same room. High data availability and disaster recovery are must-haves on your data warehouse requirements template.
- Bulk Loading and Unloading
- Data Streaming
- Multi-Cloud Storage Compatibility
- Secure Data Sharing
- Cross-Region Replication and Failover
6. Platform Capabilities
AI and ML are the wonder twins that automate ETL, data preprocessing and data quality management. Additionally, they allow setting up workflows for exploratory data analysis, model training and data engineering.
You might not need advanced data science capabilities if you have simple analytical needs and small datasets. However, you might get basic AI features with your warehouse solution; ask vendors what’s available and at what cost.
Data warehouses work behind the scenes to support BI dashboards.
Warehouse tools may not include analytical dashboards, but they serve as critical backend technologies for BI and data visualization tools. If you’re into logistics or warehouse management, you can display real-time data from your warehouse platform.
You should ask the vendor if consumption dashboards are available; many vendors provide usage metrics within the platform. As for alerts, warehouses don’t have them built in, but they support BI tools that have dashboards with alert options.
- AI-ML Integration
- Dashboards
- Alerts and Notifications
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Why Requirements Gathering Is Business-Critical
Software procurement and implementation are complex endeavors requiring meticulous planning and execution. Data warehouse business requirements gathering is a crucial first step in this process.
According to Davide Romano, CEO of PrimeLicense, a software reseller:
Requirements gathering is the backbone of efficient software procurement and strategic IT investment.
Matching your software to internal goals and market demand becomes straightforward with a data warehouse requirements template. It helps define your business needs and prioritize features that matter to your stakeholders.
Romano shared how a polished requirements checklist helped them prepare for vendor discussions:
In my experience at PrimeLicense, having the ability to clearly define needs on which each software purchase is based has been transformative in aligning our technology with both internal goals and the needs of our clients. This stage allows us to identify functional gaps, prioritize features, and avoid many pitfalls that could result in project delays, over-budgeting, or inefficiency.
This approach also allows for negotiation with suppliers on more favorable terms, as we buy only what we really need, and because it provides an excellent opportunity for value-for-money buying.
A data warehouse requirements template will give you the clarity to reach out to stakeholders and record their input accurately. You can also better manage expectations across teams.
However, it’s easier said than done.
Eliot Vancil, CEO of Fuel Logic LLC, told me:
One of the hardest things for us is meeting the needs of all the different stakeholders while staying within the budget. To keep the project on track, we need to be clear about what the priorities are and who is responsible for what.
You might consider adding aspirational features to your list — an excellent way to balance immediate needs with long-term vision. Cost calculation will be easier, and you can plan for deployment and testing accordingly.
How to Create a Data Warehouse Requirements Gathering Template
Romano shared his process.
During requirements gathering, I start by holding in-depth consultations with stakeholders whose insight is important to the comprehension of specific needs and goals.
I delve into what software functionalities are most critical for them and how these will support both immediate and long-term objectives.
Next, I go into the full analysis, where I compare all the available software solutions against our requirements of features, ease of use, and cost-effectiveness.
At the end, we test some selected options with key users to confirm the real workflow fit. This way, we make sure we meet the technical requirements to boost productivity and satisfaction in the team.
Vancil shared:
(Post-deployment)…we use an iterative feedback method to test early versions of the software and change the requirements based on user feedback.
People often think that gathering requirements is a “one-and-done” job, but it’s a process that changes over time.
It’s also important to remember that finalizing the requirements can extend to testing the pilot or watching the proof-of-concept.
Having worked with buyers and vendors over the years, we’ve developed a Lean Software Selection process that takes you from defining requirements to signing up with a suitable vendor using best practices and a systematic approach to minimize risk. Read about it here.
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FAQs
According to LaFerle:
The top considerations should generally be around performance/scalability, ease of integration, and security. Each organization will have different priorities (and compliance requirements), but a failure in any of those dimensions is a failed project.
Additionally, there are some technical requirements. Besides integrations, your warehouse platform should provide a development environment for coding custom features.
Role-based access control, data encryption and column and row-level restrictions will keep data secure. SSO enables hassle-free sign-ins, while multi-factor authentication adds an extra security layer.
Warehouses are the gatekeepers of data and should adhere to security regulations, such as ISO, SOC, GDPR and HIPAA. If your throughput varies, you’ll need a warehouse that can scale dynamically. Additionally, backup servers are a safety net, helping you recover from data loss.
According to LaFerle, if you don’t have a clear picture of how much long-term maintenance will cost you, it can cause sticker shock later. He also said that while data cleansing is extremely important, it’s often overlooked.
New projects can introduce new data objects, structures and attributes, or result in mass changes to data. (New) data needs to be integrated and harmonized with historical data in the warehouse. None of this happens automatically.
As organizations seek to gain insights from their data warehouse, they sometimes find they’re limited by the completeness and accuracy of the data itself. While these issues are usually inherited from the source systems, they can also be introduced in the ETL/ELT process as well. Data cleansing and harmonization efforts are not small undertakings, and are often overlooked in planning.
SQL warehouses work better with structured data and complex queries where ACID properties (Atomicity, Consistency, Isolation and Durability) are critical. This focus on data integrity makes them ideal for financial services, healthcare and defense.
They can handle unstructured data also but may struggle with large volumes and rapid changes.
NoSQL warehouses are great at real-time processing and handling semi and unstructured data. It makes them the right fit for social media analytics and personalized marketing. They’re horizontally scalable, meaning you can add servers for heavier workloads.
Hybrid warehouses offer the best of the two — they can handle SQL and NoSQL assets. Cloudera, IBM Db2, Amazon Redshift and Oracle Autonomous Data Warehouse are examples.
Your choice will depend on your unique business needs, project requirements, the nature of your data and the required analysis speed.
Here are some questions to ask yourself while creating the data warehouse business requirements checklist.
- Is your data structured or unstructured?
- What kind of queries will you be running? Are they simple analytical queries or complex, real-time analytics?
- How much information do you expect to store and process?
- What are your requirements for query response times?
- What’s your budget?
- Do you have the necessary skills to maintain a NoSQL warehouse?
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Next Steps
Remember the YouTube runner from our intro? Unlike him, backend technologies such as data warehouses have a lot riding on them. You can’t take any chances when your investment is on the line.
Need help getting started with software selection? Get our free requirements template to list your unique needs and match them to a suitable solution.
Do you have any specific questions about data warehouse requirements? Leave a comment below, and we’ll be happy to help!
SME Contributors
Eliot Vancil, CEO of Fuel Logic LLC, leads a Dallas, Texas-based company specializing in comprehensive fuel management solutions, including delivery of various diesel types and gasoline nationwide. His diverse professional path includes roles such as Proprietor of Beaumont Hotel & Spa and former CEO of Network Logic, where he oversaw IT support and services for Texas businesses.
Davide Romano, a software solutions expert and founder of PrimeLicense.com, leverages his Java and full-stack development expertise to empower businesses. He guides them in selecting optimal software solutions and optimizing IT budgets, with proficiency in Microsoft, Adobe, architecture & multimedia tools, and antivirus & VPN solutions.
Nate LaFerle has been a trusted advisor to some of the world’s largest organizations tackling complex data migration & governance challenges, leading high-impact global project teams at clients including 3M, American Airlines, and Johnson & Johnson. As a consulting talent leader, he oversaw the career & learning programs for over 600 global data consultants and partners at Syniti, a leading data management software and services provider. He continues to advise clients and deliver solutions through his independent consulting practice, Remisphere Digital (www.remisphere.com).