Data Engineering

Top 10 Data Warehouse Challenges and Solutions

December 13, 2023

In ever-expanding data engineering services, the importance of data warehouses cannot be overstated. As organizations strive to harness the power of their data for strategic decision-making, data warehouses serve as the backbone, housing and managing vast volumes of information. However, with great capabilities come great challenges. This comprehensive guide will explore strategic data warehouse problems and solutions to the top 10 data warehouse challenges businesses face today. Focusing on the critical aspect of data quality governance, we’ll navigate through the complexities of data warehousing, catering to the personas of higher management, chief people officers, managing directors, and country managers.

Role of Data Warehousing

Information Management

Minimizing redundant operational data and organizing data to fit the organization’s overall purpose in gathering it are the goals of Data Administration, often called Information Resource Management – IRM. Building and keeping a data warehouse running well requires these features. Name standards, data element mapping methods, and database building rules must be developed and published by business and systems staff members before any meaningful warehouse development activity can commence. Users won’t trust the data they retrieve from the warehouse if the data isn’t defined well, and systems administrators won’t be able to find the operational system data they need to fill the warehouse quickly enough. Having a dedicated team of personnel managing information risk management (IRM) is crucial for any firm, but particularly so if contractors are involved in building and maintaining the data warehouse.

Database Architecture

The database architect is mostly in charge of the administrative aspects of the warehouse’s physical design. He or she takes part in the modeling process by acting as a stand-in for the entities that will be physically implemented in the model. The senior database analyst is responsible for directing the development of the database tables, keeping the physical environment of the warehouse in good repair, and keeping an eye on any changes made to the environment by less experienced database analysts. The capacity to envision the warehouse’s physical layout is a strength of the database architect.

Repository Administration

An organization’s metadata should be stored in a repository so that it can be accessed and managed centrally. Information about the data’s origin, intended transformation, format, and purpose is described in metadata. By providing a centralized home for all business and system data collected throughout development, a repository can store data models and procedures. It takes two people with different sets of abilities to manage a warehouse’s repository: an administrator with knowledge of data (or IRM) and someone with knowledge of databases. In addition to managing the integration of the logical models of the operational and warehouse systems and participating on the standards development team, a Repository Administrator acts as a liaison between the technical and user communities for the operational and warehouse metadata.

Analysis of Business Area Needs

Understanding the analytical procedures and data required for business inspection is the purpose of a data warehouse business area analysis. A warehouse data model will be developed when representatives from the business area and Information Resource Management meet to discuss the needs for a data warehouse. “What data do we want analytical access to?” is an important question that needs answering during the requirements-gathering phase. From what procedures will this data be derived? In what ways may this data be used to make important decisions? Meetings should stay on track to save time and energy, and it is the facilitator’s job to make sure that everyone has asked and answered the right questions.

Data Analysis

While both operational and informational systems modeling make use of the same techniques, the two types of models that emerge are as follows: a.) a representation of the operational business requirements that is both detailed and optimized for transaction processing; and b.) a representation of the informational business requirements that is both simplified and optimized for analytical processing, although with less detail. You can’t have one without the other; in fact, you should incorporate both into your system development or improvement plan. Based on the operational data model of the targeted data engineering services area, the informational data model should fulfill the analytical requirements of that area. Users work in teams to construct the models, which are subsequently validated by transferring data between the operational and warehouse models.

Data Warehouse Challenges and Solutions

Data Quality Concerns

According to Gartner, poor data quality is a common issue for organizations, with the research firm estimating that the average financial impact of poor data quality on businesses is $15 million per year.

Data quality is the bedrock of any successful data warehouse strategy. Inaccurate or inconsistent data undermines the integrity of analyses and decision-making processes. Poor data quality can lead to misguided insights, eroding stakeholders’ trust in the data warehouse.


Organizations must institute robust data quality governance practices to address data quality concerns. Regular data profiling, cleansing, and validation processes should be implemented to maintain high data accuracy and reliability. By establishing clear data quality standards, organizations can ensure the data within the warehouse is a trustworthy foundation for decision-making.

Scalability Issues

The global cloud-based data warehousing market is expected to grow at a CAGR of over 22.3% from 2020 to 2025, indicating a significant shift towards scalable cloud data warehousing solutions.

As data volumes grow exponentially, traditional on-premise data warehouses may struggle to scale effectively. This can result in performance bottlenecks, delays in data processing, and increased costs associated with hardware upgrades.


Cloud-based data warehousing solutions offer a scalable alternative. Leveraging the elasticity of cloud infrastructure, organizations can seamlessly scale their data warehouses based on demand. This addresses immediate scalability concerns and provides a cost-effective solution, allowing businesses to pay only for the resources they consume.

Integration Complexities

According to a survey, 94% of IT decision-makers reported that they faced data integration challenges, highlighting the prevalence of this issue.

The modern data landscape is diverse, with data coming from various sources in various formats and structures. Integrating this disparate data seamlessly into a data warehouse can be complex and time-consuming.


Implementing data integration maze tools and middleware becomes crucial in overcoming integration complexities. These tools facilitate the extraction, transformation, and loading (ETL) processes, ensuring that data from different sources is harmonized and compatible within the data warehouse. This streamlining of integration processes enhances overall efficiency and accuracy.

Data Security and Privacy

IBM’s Cost of Data Breach Report estimates the average total cost of a data breach to be $3.86 million, a 15% increase over 3 years, underscoring the financial impact of inadequate data security.

Ensuring the security and privacy of sensitive data within the data warehouse is paramount. Unauthorized access, data breaches, or non-compliance with data protection regulations pose significant risks.


The solution lies in implementing robust security protocols. Encryption mechanisms should be employed to safeguard data in transit and at rest. Access controls must be rigorously enforced, limiting data access based on roles and responsibilities. Furthermore, compliance with data protection regulations, such as GDPR or HIPAA, is essential to mitigate legal and reputational risks.

Lack of Data Governance Strategy

A survey by Collibra found that 87% of surveyed organizations identified data governance as a critical initiative, emphasizing the growing recognition of its importance.

The absence of a comprehensive data governance strategy can result in unstructured data management, leading to governance gaps, inconsistent practices, and a lack of accountability.


Developing and implementing a robust data governance framework is imperative. This involves defining clear policies, roles, and responsibilities for data management. Data stewardship and ownership should be established, ensuring accountability throughout the data lifecycle. A well-structured data governance strategy forms the backbone of effective data quality governance.

Performance Tuning Challenges

According to a study by Panoply, over 80% of data professionals reported performance challenges with their data warehouses, indicating a widespread concern.

Poorly tuned data warehouses may experience slow query performance, affecting real-time analytics and decision-making. Inefficient database designs, indexing, or suboptimal configurations contribute to this challenge.


Regular data warehouse performance tuning is essential to address these challenges. This includes optimizing queries, indexing, and partitioning strategies. Organizations can fine-tune their data warehouse to deliver optimal performance by understanding the data access patterns and workload demands, ensuring timely and efficient data processing.

Meeting Business Requirements

A survey by TDWI revealed that only 40% of organizations feel their data warehousing projects consistently deliver business value, highlighting the gap in meeting data warehouse business requirements.

Aligning the data warehouse with evolving business needs is an ongoing challenge. The static nature of some data warehouses may result in a mismatch between data capabilities and the dynamic requirements of the business.


Establishing clear communication channels between data and business teams is pivotal. Regular feedback loops involving stakeholders at various levels help continuously refine and update data warehouse requirements. This ensures that the data warehouse remains agile and responsive to the organization’s ever-changing needs.

Data Warehouse Strategy Alignment

According to a report by Nucleus Research, companies that align their data strategy with business goals achieve a 23% increase in ROI.

The data warehouse strategy may not align with broader organizational goals, resulting in a lack of synergy. This misalignment can lead to missed opportunities for leveraging data as a strategic asset.


Ensuring alignment between the data warehouse strategy and overall business objectives is paramount. Strategic insights should emphasize the impact of effective data warehousing on organizational success. This alignment fosters a data-driven culture, ensuring that data is leveraged as a valuable resource across all facets of the organization.

Adoption and User Training

A study found that organizations that invest in employee training have 24% higher profit margins than those that don’t, emphasizing the positive impact of training on adoption.

Lack of user adoption and insufficient training can hinder the effective utilization of the data warehouse. Users may struggle to leverage the system’s full potential, limiting its impact on decision-making.


Investment in user training programs is a critical aspect of addressing this challenge. Providing comprehensive training ensures that stakeholders at all levels understand the value of the data warehouse and are proficient in its usage. This enhances user adoption and maximizes the value derived from the data warehouse.

Cost Management

Forbes reports that organizations spend, on average, 7.6% of IT budgets on data warehousing, showcasing the significance of cost management in this domain.

Managing the costs associated with data warehousing, including hardware, software, and maintenance, can be a significant challenge. Organizations must balance the need for performance with budgetary constraints.


Exploring cost-effective cloud-based solutions is a strategic move. Cloud platforms offer flexibility and scalability, allowing organizations to optimize resource usage based on actual needs. Periodic reassessment of infrastructure needs ensures that the costs associated with data warehousing remain aligned with the organization’s budgetary considerations.

How can Brickclay Help?

Brickclay is well-positioned to help organizations overcome the numerous data warehousing-related difficulties because it is a top provider of data engineering services. Using our knowledge and experience, we create individualized plans to meet the specific requirements of every business. To assist organizations in overcoming the top 10 data warehouse difficulties, Brickclay has developed the following:

  • Data Quality Governance: Brickclay is an expert in creating and sustaining strong data quality governance procedures, which guarantee that the data in the warehouse is accurate and reliable to the highest degree.
  • Cloud-Based Solutions: Brickclay suggests and sets up data warehousing solutions on the cloud that can be scaled up or down as needed. In this way, performance bottlenecks are avoided and the system can expand without a hitch as data needs change.
  • Integration Complexities: Our team has vast experience integrating multiple data sources. We employ sophisticated integration tools and middleware to simplify the process and guarantee a unified data flow from different formats and structures.
  • Data Security and Privacy: Brickclay Places a Premium on Security Protocols. We employ strong security protocols, encryption, and access controls to protect sensitive information within the warehouse and guarantee adherence to data protection rules.
  • Data Governance Framework: Brickclay helps businesses create and execute a data governance framework that covers all bases. With an eye on data quality governance, we check that all procedures, policies, and lines of responsibility are crystal clear.
  • Improve Efficiency: Brickclay is an authority for optimizing data warehouse performance. We apply indexing, partitioning, and caching to improve query performance and maximize the data warehouse’s efficiency.

Businesses can trust Brickclay to help them through the data warehousing maze, and we can help them transform obstacles into chances for development and innovation by employing our data engineering services.

Brickclay offers bespoke data engineering solutions that can revolutionize your data warehousing strategy and help you overcome obstacles. Contact us now and together we can embark on a journey toward success in business and better data management.

About Brickclay

Brickclay is a digital solutions provider that empowers businesses with data-driven strategies and innovative solutions. Our team of experts specializes in digital marketing, web design and development, big data and BI. We work with businesses of all sizes and industries to deliver customized, comprehensive solutions that help them achieve their goals.

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