Your existing data warehouse is dragging your business down. It was designed for the past when your needs were different. It is used to generate batch reports. It works on expensive servers. It struggled with large datasets, and it couldn’t process live data.
But now your business needs more. Something like real-time data. Something like scalability. Something like actionable insight.
This is why most companies today go for cloud-based systems. And their first step in this direction is to develop a data warehousing implementation strategy.
In this article, you’ll find all the steps to get there.
Data Warehousing Implementation
What Is a Legacy Data Warehouse?
Legacy data warehouses are on-premises databases that have been around since the beginning of time. They store your historical data. They provide you with your prescribed reports. However, they have limitations.
They don’t measure well. Increasing their storage capacity will cost you a lot. You will have to spend more to increase their processing power.
They don’t work well with unstructured data. Emails, logs, and social data cannot be easily stored in such databases.
Legacy databases also use older tools that may also be outdated. This adds to the danger posed by this database. Moving away from legacy systems is no longer an option; It is a necessity for the enterprise.
What Is a Cloud-Native Data Warehouse?
Cloud-native data warehouses are deployed entirely in the cloud. No server purchase required. Maintain no physical hardware. Pay for what you get.
It can scale in seconds. Need more computing resources to run a large report? It is possible. Finished work? Scale back in a few seconds.
Cloud-native data warehouses, including structured tables, unstructured logs, and streaming data, support all data types. It easily integrates with cutting-edge tools such as Power BI, Tableau, Python, and Machine Learning platforms.
In addition, it is always up to date. Cloud platforms update themselves.
Why Data Warehousing Implementation Strategy Matters
Migrating to a cloud-based model isn’t just about copy and paste
According to Bloor and Gartner, more than 50% of data migration projects are late or over budget. And what is the reason? Lack of proper planning.
A well-defined strategy for data warehousing implementation saves one from all these hassles. It charts the entire course. It provides specific goals and objectives. It determines the time and financial structure.
Without such a strategy, people will move quickly and break things. Data will be transferred incorrectly. A system crash will occur. Users will lose trust. Money will be wasted.
But with a data warehousing implementation strategy, people move deliberately. Each step follows the previous one.
Step 1: Assess Your Current System
First, know what you have. Document your data sources. What data sources do you currently have? For example: CRM, ERP, finance applications, IoT devices, third-party feeds.
Assess the quality of the data by questioning:
- Is the data error-free?
- Do you have any duplicate data?
- Are your field definitions consistent across applications?
Document your existing reports:
- Which reports are currently being used?
- Which reports are outdated?
- Which reports are not being delivered?
Meet with your stakeholders:
- What questions are currently not being answered?
- Which decisions are taking too long to make?
- What data is needed but missing?
This assessment will give you an idea. This process will guide every subsequent decision.
Step 2: Define Your Goals and KPIs
Now is the time to set clear goals. How do you define success in six months? A year from now? Choose KPIs (key performance indicators) to measure your progress.
Some possible examples are:
- Report run time before migration
- Improvement in data quality score
- Number of integrated data sources
- User adoption rate by category
Setting goals ensures that you are on the right track. It also helps you demonstrate return on investment (ROI) to your superiors.
Step 3: Choose Your Architecture
Here is your future system design. There are three main options.
The cloud-native approach is currently the most popular. Everything is moved to the cloud. This ensures scalability and cost savings.
In the hybrid approach, a portion of the data remains on-premises. The rest of the data is moved to the cloud. This is ideal for companies with high data governance requirements or sensitive data.
In the on-premises approach, the data remains on-premises. It is less popular now. However, it is still used in some industries with high regulatory compliance requirements.
At data warehousing implementation, most businesses choose between a cloud-native or a hybrid approach.
You also need to choose a data model. Traditional data warehouses are good for structured data. Data lakes can handle unstructured data. A lakehouse includes all of the above.
Step 4: Pick Your Platform
Choose the right cloud platform.
Snowflake offers excellent performance and manageability. It can scale up instantly. It works with AWS, Azure, and Google Cloud.
Microsoft’s Azure Synapse integrates data warehousing, big data, and analytics. It’s good for Microsoft environments.
Amazon Redshift delivers strong performance for large AWS workloads. It integrates well with other Amazon products.
Google’s BigQuery is completely serverless. No infrastructure management is required. Queries execute very quickly.
Databricks is a great choice for complex AI and ML pipelines. It’s built for heavy data workloads.
Microsoft Fabric is the ultimate all-in-one choice. Data engineering and analytics are combined into one product.
There is no perfect solution, and the right choice depends on your environment and preferences. A partner for data warehousing implementation will help you make an unbiased choice.
Step 5: Plan Your Data Migration
This is usually the most difficult stage. Here you are migrating years of data from existing systems to the new system. Every bit of data is important. Nothing should be left out.
Plan your migration process in stages. You should not migrate all your data at once. Migrate a dataset or a section first. Test and validate your results. Debug any issues. Then move on to the next stage.
Use ETL tools to automate this process. Informatica, Azure Data Factory, and Talend can be used effectively in this case. These tools will extract data from your existing systems, transform it, and load it into the new data warehouse.
Compare side by side. Compare the output of both your existing and new systems.
Step 6: Set Up Data Governance
Relying on technology alone is not enough to make your data warehousing implementation project a success.
Governance is just as important. In fact, it can be even more important. It sets the rules of engagement—who owns the data, who has access to it, how long it will be stored, and how its quality will be ensured.
Without proper governance, your new system quickly becomes cluttered. The same metric means different things to different departments. Data quality degrades, and so does trust.
Build your governance structure in advance. Consider the following:
- Ownership rules for each important dataset
- Quality criteria and their monitoring
- Access rights based on roles and departments
- Compliance with GDPR, CCPA, and other regulations
- Metadata management and data dictionaries
ExistBI ensures that governance is built into all data warehousing implementations from the start. This is not an afterthought but rather part of the data warehouse from day one.
Step 7: Build, Test, and Launch
Now it’s time to get down to business. Build the new warehouse facility. Launch the pipelines. Collect data. Configure the reports.
Next, ensure thorough testing. Test run the necessary reports. Compare the data from your current solution with the data from the new solution.
Finally, test its performance capabilities. Does your system work well under heavy traffic? How does it perform when multiple users use it simultaneously?
Go live step by step. First one group. Then another. Gather feedback and improve.
Step 8: Train Your Team and Monitor
The effectiveness of the new system depends on its users’ skills. Provide adequate training to your users. Explain how to create reports. Explain how to understand the data model. Train them to identify data quality issues.
Then continue monitoring after deployment. Keep an eye on slow queries. Monitor data freshness. Analyse access logs. Optimize the system according to your needs. ExistBI provides continuous monitoring and 24/7 support after deployment.
How ExistBI Can Help
Since 2008, ExistBI has helped hundreds of companies solve their data warehousing implementation projects. Our operations span over 25 different industries. Our team of over 300 experienced professionals handles all stages of the process, from assessment to deployment.
Our services are not vendor-locked. We use solutions from Snowflake, Azure Synapse, Amazon Redshift, Google BigQuery, Databricks, Microsoft Fabric, IBM, SAP, and many more, depending on our clients’ needs.
