Data warehouse implementation is one of the trickier jobs in analytics. But with the right tools and good planning, you can easily wrangle all of your data into one place. Here's how.
- Why Implement a Data Warehouse?
- 8-Step Data Warehouse Implementation Plan
- Possible Pitfalls in a Data Warehouse Implementation
- Taking Your Data Warehouse to the Next Level
Why Implement a Data Warehouse?
A data warehouse is a centralized repository that stores integrated data from multiple systems.
A typical business has several mission-critical systems. There might be a CRM, ERP, e-commerce system, or marketing automation platform. All of these systems run on a relational database that holds crucial data.
You can consolidate all of this information by setting up a data pipeline, powered by ETL. This pipeline extracts data from your essential systems, integrates and cleanses it, and then stores it all in one big relational database: a data warehouse.
There's a misconception that data warehouse implementation is something that you only need to think about when your data reaches a specific volume. Small and mid-size companies delay this step until later in their development because they don't see the value of a centralized repository.
Most businesses need a data repository right from the get-go, and for one specific reason: analytics. A data warehouse is one of the fastest and most reliable ways to consolidate data from multiple systems, giving your analytics team a 360° view of your customers and your operations.
There are other reasons to consider a centralized repository, like system integration or having secure backups for disaster recovery. And getting started is easy if you have the right data warehouse implementation plan.
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8-Step Data Warehouse Implementation Plan
Once you've identified the need for a data warehouse, it's time to start planning. Follow these steps for implementing a data warehouse:
1) Gather Requirements
A company-wide data project like this will involve multiple stakeholders. You'll need to talk to:
- Decision-makers: Talk to leaders and strategists, right up to the C-suite. They will help you to stay aligned with the company's strategic objectives.
- IT: The IT team will play a practical role in the data project, like helping you connect sources to the data pipeline. They'll also deal with any errors, and they may need to support the warehouse if you go with an on-premise solution.
- Analytics: The data team will help you scope out the project. They'll define the project outcomes, which will help identify the required data sources.
- Security and compliance: Data warehousing can involve handling sensitive information. You'll need to ensure that you're not breaking any rules or creating any additional risk.
Once everyone is on board, you're ready to start your data warehouse implementation.
2) Create Warehouse Environments
At this stage, you have several options for your warehouse environment, such as:
- On-premise: Host on local hardware
- Public cloud: Use a hosted cloud solution, like AWS or Azure
- Private cloud: Host a cloud on your own hardware, or hire a trusted third party
- Hybrid cloud: Either mix on-premise and cloud storage or store data on-premise and use cloud capability for processing and analytics
A public cloud is often the cheapest and easiest option, as your host does most of the hard work for you. However, there are latency and security issues that might make you consider an on-premise or hybrid option.
Whichever you choose, you'll need to create three separate environments:
- Development: Your dev team works in this environment to test new features and new integrations. This environment holds test data, with sensitive information obfuscated.
- Testing: As the development environment, except this warehouse is for testing and QA.
- Production: This is the live data warehouse, accessed by users and your analytics team. You can't make any changes to this unless you've tested those changes in the other environments.
You can create more than three environments if required. For example, you might need separate warehouses for testing and QA. But you will need at least three, as your development team can't try out new features on the production data.
3) Choose a Data Model
Data modeling is perhaps the most difficult part of data warehouse implementation. Every source database has its own schema. Your warehouse will have a single schema, and all incoming data must fit this schema. So you need a model that suits all existing data and can scale up for the future.
Some of the main types of schemas are:
- Star schema: Fact tables with linked dimensional tables.
- Snowflake schema: Enhances the star schema by adding an additional level of dimensional tables
- Galaxy Schema: Multiple fact tables connected by common dimensional tables
- Constellation schema: Galaxy schema with an additional hierarchy of dimensional tables
Designing a schema from scratch is generally the work of a data scientist. Many cloud and commercial on-premise systems will help you to adopt a schema model to your needs.
4) Connect to Sources
Connecting is a two-step process. First, you extract data from the target source, and then you upload it to the data warehouse.
Extraction can happen in several ways, such as:
- API call: The most common method, this is a transaction processed by a secure interface.
- File transfer: Legacy systems may export data as a file, typically CSV
- Direct query: in some instances, you may be able to obtain database results using an SQL query
Once you've obtained the data, you need to load it into the data warehouse.
Because of this task's complexity, most people rely on an automated ETL (Extract, Transform, Load) to handle the entire process. Xplenty comes with a library of integrations to automatically extract from the target and load to the destination.
When you have an automated process moving data in this way, it's known as a data pipeline.
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5) Transform Incoming Data
ETL has a vital step between extract and load. Transformation is an intermediate stage, where the ETL process converts data from its original schema to the destination schema. Without transformation, your data can't slot into the destination tables.
Transformation can also include other steps, such as:
- Validation: Ensure that all data fits within logical constraints, such as dates being valid or ZIP codes matching the address.
- Cleansing: Remove any corrupt or duplicate data. Alternatively, leave the data as-is but flag up potential problems.
- Harmonization: Unify all data in a single format, such as converting temperatures to Fahrenheit or changing all date formats to MM/DD.
- Enrichment: Combine records with data from other sources to improve data quality.
If you use a tool like Xplenty, you can create schema mappings without coding. You can also set rules for validation, cleansing, and other data hygiene actions.
6) Create Data Marts
Data warehouses store everything, but most people don't need access to everything. Sales teams want sales figures; operations teams need ops data, and so on.
The solution? A data mart. Marts are a logical division within the warehouse – a limited view that only shows relevant results.
You can often manage this with the right metadata. For example, you may tag some records with "Sales" and others with "Finance". Marts can show records with each matching tag. A record that's relevant to both teams, such as a sales invoice, can have both tags. It will then appear in both data marts.
Marts are a great way of delivering targeted results. They're also an excellent way of improving data security, as they restrict people from viewing relevant data.
7) Configure BI and Analytics
Most commercial Business Intelligence (BI) and analytics tools offer simple integration with a data warehouse. You can also connect these tools directly to your ETL platform, offering even faster insights and visualizations.
BI and analytics tools rely on :
- Volume: The more data you have, the more detailed your analytics insights.
- Velocity: For real-time dashboards, you need a fast-moving stream of data.
- Veracity: Your data has to be of high quality, offering an accurate picture of your current state.
If you've followed your data warehouse implementation plan, you should be able to deliver the data your analytics team needs.
8) Audit and Review
Once your data warehouse is operational and your analytics team has what they need, you can start putting measures in place to ensure data quality.
This may involve using automated data quality testing tools to measure the quality of your warehouse contents. You can also perform sense checks to see that there are no obvious discrepancies between raw data and stored data.
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Possible Pitfalls in a Data Warehouse Implementation
A data warehouse implementation is a big project, and it can go awry. If you've followed the plan, you shouldn't have any issues. But here are a few things to watch out for:
Problem: Security risk identified.
Solution: Security is an ongoing process. Transforming fields to remove, encrypt, or mask sensitive data is the first step to preventing data loss. You have to keep a close on your compliance and security habits as well as in your choice of cloud partner who will deal with emerging threats on their end.
Problem: Personal information May Cause a Compliance Breach
Solution: Your ETL can help to obfuscate sensitive data before it goes to the warehouse, which should resolve compliance issues.
Problem: Data Quality Issues when Performing Analytics
Solution: First, check the source and make sure that the data is clean at the point of origin. If so, then it's likely to be one of the stages in your ETL process:
- Your integration has a configuration
- Your transformations aren't mapping the correct fields
- There's a problem uploading to the warehouse schema
The latter problem might require you to rethink the structure of your warehouse.
Problem: Analytics Can't Produce Actionable Insights
Solution: This usually means that you've selected the wrong data sources. Sit with your analytics team and review the data that's going in. Sometimes, you may need to go further downstream and look at ways of capturing new data.
Taking Your Data Warehouse to the Next Level
In many ways, a data warehouse is similar to a regular warehouse in that they are both all about processes – how things arrive in, how the items are stored and ordered, and how to process fulfillment requests as quickly as possible.
Your data warehouse is ultimately just a big relational database. What makes it exciting are the processes that keep it going. How you ingest data, how you integrate data, and how you feed that data out to your BI and analytics tools.
Xplenty is the perfect platform for an orderly warehouse. Book a demo today and discover what a no-code ETL can do for you.