9 Tips to Ensure a Successful Data Warehouse

Data and data bases

Data Warehouse is a single repository to store all the data from an organization’s databases and related sources in one place. All the forms of structured and unstructured data in different formats and visualizations are extracted from the organization and transformed. Depending upon an organization’s approach, data could be transformed before or after loading into the Warehouse.

Besides acting as a single source of truth for your organization, Data Warehouse services offer storing large amounts of data, finding correlation patterns, and recognition for business decision-making. It effectively helps in data analytics, visualizations, and reporting. 

How to Ensure an Effective Data Warehouse?

The Data Warehouse market is expected to reach  US $30 billion by 2025. But how will you ensure a successful Data Warehouse for your business?

Here are 9 tips to help you prepare an impeccable roadmap:

1. Pre-implementation planning

Strategy for planning a Data Warehouse implementation enables an organization to proceed faster without hindering current operations and facilitates the process for further analysis. Each role is well defined in the pre-plan, and the extent of information sharing is set that meets the business requirements of Data Warehouse development. 

Organizations with multiple departments might need help to finalize an implementation strategy. For example, each department has repositories, like finance, operations, marketing, inventories, etc. Such an organization is huge and requires multiple stakeholders to participate in the meetings. 

The extent of sharing data and prioritization is set during these meetings after discussions and negotiations. The panels ensure that all the stakeholders agree on transferring data from relevant repositories and set a well-defined timeline for the scalable project. Each stakeholder needs to understand the purpose of sharing data from their department to understand it better. 

Data from each department will collaborate with the others to convey meaningful information necessary for the success of the business. Data Warehouse will transform the extracted information from these departments and transform into better and more understandable sets that align with business goals. Processes such as ETL/ELT, OLAP cube and reporting should be pre-decided before Data Warehouse Implementation.

The team finalizing the strategy comprises of 

• Stakeholders 
• Managers from respective departments 
• IT managers
• Business analysts
• Data Scientists
• Engineers

A skilled business analyst is an ideal negotiator for the project because Data Warehouse’s ultimate goal is to analyze information for desirable business outcomes. Upon deciding the timeline of the project, a budget is set depending upon the repositories involved, timeline, goals, and room for scalability.

After finalizing the implementation strategy, it should be properly documented for practical procedures and repeated demonstrations.  

2. Data Warehouse Deployment

After developing a plan for implementation, you need to choose an appropriate deployment for Data Warehouse. It could either be an on-premises model or a cloud-based warehouse. 

The on-premises deployment model benefits an organization in terms of control, access, speed, and security. Organizations with strict data protection policies choose on-premises models for warehouses. Since Data Warehouse is deployed within the physical infrastructure, information transfer and access are not latent. There is no lag in Data Warehouse access, and other such operations as cloud authorization are not there. Data is governed more safely within the organization’s premises and offers faster results.

A Cloud-based Data Warehouse deployment Model is among the most common, adaptable, and scalable systems for analysis. In 2022, 60% of existing corporate data will be stored in Clouds. This figure has doubled since 2015 and is expected to reach new heights by 2025. Most of the legacy on-premises Data Warehouses are shifting to cloud-based deployment models. The reason is scalability, quick access, management, and maintenance. Cloud-based Data Warehouse deployment reduces the workload of the in-house team because most of the functionalities are maintained by the service provider. Providers of Data Warehouse services manage all the APIs involved in the process. The budget for the entire project can be scaled up and down depending on the requirements.

An optimal solution is to choose a hybrid Data warehouse service deployment model that uses an on-premises setup with a multi-cloud environment. The Hybrid Data Warehouse as a Service (DwaaS) models offer you security like on-premises setup and scalability like cloud solutions.

3. Introduce Data Modeling

You must introduce Data Modelling before the procedure warehouse starts in the organization. It is done to visualize relationships between different types and structures. The step of Data Modelling must happen before agile data warehouse design as it is a blueprint that helps the organization plan the structure of the data mart. There should be proper documentation of stored data types, attributes, formats, nomenclature, and relationships with other types. 

You must set all the standards for data to be stored in the Warehouse before the process of extraction starts. The decision to choose the model depends upon stakeholders, the architecture of the Warehouse, processes used, deployment type, etc.

4. Finalize Data Warehouse Process: ETL/ELT

An organization with multiple departments and branches has loads of repositories, including databases from third-party integrations, external sources, and real-time data. There must be practice in taking out all the data, making it understandable, and loading it into the Warehouse for analysis procedures. 

Choosing ETL or ELT practices ensures the success of your Data Warehouse and its purpose of analytics and reporting. 

• ETL: Extract, Transform, and Load
• ELK: Extract, Load, and Transform

Both practices have the same processes but apply at different times with respect to the Warehouse, depending upon the requirements. 

ETL is a standard practice used in Data Warehouses for effective analysis procedures. 

The first step in any practice is to extract all forms of data from the organization, its integrated databases, and other sources. Such data may be structured or exist in unstructured forms. The data might exist in different types, formats, and attributes within an organization. In ETL, all the data is transformed irrespective of the requirement to ensure quick reporting and analysis. Post transformation, data is loaded into the Warehouse for further procedures. 

ELT is a new practice that is slowly replacing ETL as a standard procedure. In ELT, data is first loaded into the Warehouse before any transformation. Depending upon the requirements, organizations filter out data they don’t need and transform the relevant set. Such a process ensures high analyzation speed, no wastage of time, and streamlined processes in transforming the data. The irrelevant data that might not be in use is left undisturbed for future reference. ELT is mostly used in cloud-based deployment models with high-processing Data Warehouses.

5. Optimizing OLAP

Post setting up a successful Data Warehouse using ETL/ELT in accordance with models, OLAP is among the most important technologies to implement. Abbreviated as OLAP, Online Analytical Processing is a technology to enable managers and analysts to access data from the Warehouse.  

OLAP is generally referred to as OLAP Cube or hypercube because it can access warehouse data from multidimensional databases. A hypercube is OLAP at a given point in time that can extract and store data from multiple sources. It arranges the data into multiple formats to analyze and visualize before handing it over to analysts or managers. Users put multiple queries in OLAP to obtain data of their choice. OLAP fetches the data from the Warehouse and transfers it to the recipients.  

You need to optimize the queries to OLAP for effective data analysis. MYSQL queries and data science capabilities must be addressed in the Warehouse. The OLAP query returns data from the Warehouse to the recipient for further analysis and visualization procedures. Ensure that minimal data is processed upon retrieval because the database needs to expand. This will cost more to an organization in the case of a pay-as-you-scale price model.

6. Effective visualization

Your organization should create user-friendly reports, visualizations, and dashboards for all the members to understand and work upon. Even employees with non-tech backgrounds should be able to understand the dashboard, visualizations, and reports. 

Furthermore, you should provide access to dashboards, visualizations, and reports from any device. This methodology is most effective for organizations in remote operations and cloud-based warehouse deployment options. 

There should be a front-end system where the analysts and managers apply the insightful information obtained from data queries of OLAP. The organization should employ self-analytics that allow employees to create custom visualizations, reports, and dashboards. Such user-friendly visualizations help an organization curate and list valid information together for better understanding. This will create clarity and put lesser queries on OLAP for retrieving data repeatedly.

7. Data Warehouse Training

Post-deployment of a Data Warehouse in an organization, there should be meetings and training sessions that demonstrate usage and access. The relevant employees dealing with data warehouses may be from something other than tech backgrounds. To ensure that everyone on the team has access to the Warehouse and related technologies, proper training sessions should be conducted regularly. This will result in a better understanding of data visualizations, dashboards, and reports. 

8. Data Warehouse Maintenance

An organization should indulge in proper maintenance programs that deploy quality assurance teams to check bugs and vulnerabilities. Data Warehouse should operate in a Continuous Integration / Continuous Deployment model that keeps the system up to date and regularly operates source code repositories. Errors are reported to the data warehouse development team, and effective measures are taken to resolve the issues.

9. Data Warehouse Management

Management of the Data Warehouse post-implementation is a crucial way to maintain an effective operation throughout the life cycle. An organization should regularly manage Data Warehouse to maintain the efficiency and performance of the system through ETL/ELT procedures and OLAP queries. 

The Bottomline

The success of a Data Warehouse depends upon many factors, the foundation of which is effective pre-implementation planning. Stakeholders of various departments in an organization meet and set up goals for Data Warehouse development to streamline reporting and analysis. 

Choosing between on-premises, cloud-based, or hybrid model deployment is another crucial step in deciding the success of Data Warehouse. Processes like Data Modelling, ETL/ELT, optimized OLAP cube, and self-analytics visualizations are the most important steps for an enhanced performance Data Warehouse and analytics. 

Post the successful implementation, the Quality assurance team should continuously test Data Warehouse for any vulnerabilities, security threats, and vulnerabilities. Data Warehouse developers should update new features as they should be kept up-to-date to meet the compliance and performance of the current systems.

Thank you to our guest author Vikas Agarwal, the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence Consulting, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

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