Modernizing the Data Warehouse: Challenges and Benefits

benefits of data warehouse

In today’s time, big data and analytics are managing all business decisions, and traditional strategies for data management are no longer suitable. To enable data-driven decision-making and gain valuable insights, companies now require a reliable data warehouse solution that serves as a singular, trustworthy source with up-to-date and accurate data.

While there are several benefits of data warehouses, embarking on such a massive initiative comes with several obstacles. From addressing data quality concerns to optimizing performance, several steps are to be taken when forming a data warehouse for your expanding business.

Moreover, modernizing your data warehouse involves various stages, such as formulating approaches to ensure that your data warehouse fulfills scalability and availability needs. In this guide, we will help you understand all the benefits and challenges present while modernizing your data warehouse, to support your plan for the next deal.

What is Data Warehouse Modernization?

Today, most businesses aspire to move their data warehouse to cloud-based platforms such that they can take benefit of the reliability, availability, and scalability provided by them. Moreover, since companies today are dealing with an ever-increasing data source, they must ensure that the data warehouse will be effective enough to sustain the reforming needs of your expanding business.

Presently, businesses seek to modernize their data warehouses by driving agile approaches that emphasize automation and reduce manual intervention. This methodology not only encourages the use of the data warehouse for a broad range of functions and processes but also enhances efficiency by minimizing the requirement to develop and deploy modern data warehouse architecture from scratch.

Data warehouse modernization approaches also involve increasing focus on flexible architectures and support for several data sources, enabling companies to combine their data from multiple touchpoints. Nonetheless, like any initiative, it is crucial to assess the benefits and potential challenges to make sure you are ready for your next big data warehousing project. The fact that every business is unique, a detailed assessment of these advantages and problems will help you to develop a robust data warehouse architecture to ensure you can fully reap the advantages of a modern data warehouse.

Benefits of Data Warehouse Modernization

A modern data warehouse provides companies with the agility needed to grow and make smarter data-based decisions. Some of the benefits of how data warehouse modernization can help you build your business are:

Benefits of Data Warehouse

1. Supports advanced analytics requirements

Data analytics is the focal point of any organization. They need to extract valuable insights from data coming from several touchpoints, which are present in multiple formats. A modernized data warehouse not only helps you to integrate data easily but also to draw the right data and valuable insights. It also helps you to bring data from other sources and look at how the growth of specific teams is dependent on that of others. This, in turn, makes it easy to find and weed out mistakes and helps in making decisions that will support the growth of any business.

2. Provides high speed and effective performance

In this competitive world, a slight delay can lead to heavy losses for companies. Businesses today are required to act quickly to make sure that they don’t lose users to their competitors. Modernized data warehouses ensure that you are always accessible and available without impacting the efficiency and productivity of your expanding business. Data warehouse modernization is developed to support big data volumes, offering you a snapshot of your business and where it stands.

3. Reduces costs

Having a robust data warehouse as the core of your business intelligence infrastructure can reduce costs related to purchasing several data integration tools to segment data silos. As these warehouses are created for user-friendly accessibility, you don’t need to hire extra personnel to extract data, make reports and conduct analyses. Integrating a modern data warehouse helps you save on maintenance costs linked with finding data loss throughout the ETL procedure or low-quality data which is unsuitable because of inadequate validation during source-to-data warehouse mapping.

With features like data quality and high security, a modern data warehouse helps to reduce costs linked with lost data or data that is left unused because of inferior quality.

4. Agility and elasticity

To adapt to changing demands, growing companies are experimenting with diverse data modeling techniques. Modernizing the data warehouse and incorporating a flexible infrastructure enables these companies to achieve greater agility and access various data sources while avoiding compatibility and integration issues.

Modernizing the data warehouse simplifies the process of extracting valuable insights and increases business flexibility. Moreover, when using modernized data warehouse that depends upon the agile methodology, there is no need to manually reconstruct data models and ETL flows every time new data needs to be integrated. Agile data modeling enables you to update and redeploy models quickly and regularly to improve your data architecture.

Data Warehouse Modernization Challenges

No doubt, there are several big benefits of a data warehouse, but it is equally significant to look at the other side of the facility. There are a few common data warehouse challenges that you should consider while building your modern data management plan. Some of the data warehouse modernization challenges are:

1. Lack of governance

Ideally, a data warehouse must include data from all endpoints and functions to avoid any gap in the system. However, companies may have to encounter issues if they mistakenly overlook compliance and governance guidelines.

Thankfully, data warehouse modernization overcomes these issues by introducing an abstraction layer that serves as a barrier between the end users and source systems. This enables businesses to devise several data marts that supply data based on their needs while ensuring that regulatory requirements are fulfilled throughout the reporting mechanism.

2. Access control

Creating a data strategy framework for access control is extremely critical when dealing with data warehouses. Mostly, companies struggle to discern and define which personnel or department require access to the data warehouse. Failure to manage resources and provide permissions effectively leads to an unavoidable load on the system, causing a blockage that could have been avoided.

3. Ineffective data strategy

Developing a well-focused data strategy is significant when designing or modernizing the data warehouse. The lack of an effective data strategy causes the team to not understand and embrace the benefits offered by their modern data warehouse. Collaboration among stakeholders is significant to make an effective data strategy, hence, creation, design, and planning are required to be part of a continuous process.

Common Data Warehouse Problems and Solutions

1. Inaccurate Inventory

Incomplete or inaccurate records become evident when warehouse workers cannot find products in the expected location or are instructed to place items in already full spaces. This leads to wasted time, effort, and errors, causing delays in shipments and incorrect picks.
These problems often arise due to suboptimal and cumbersome manual processes that generate inaccurate data in the system. One potential remedy is to implement warehouse execution system (WES) applications and other automated systems to streamline data recording activities.

2. Poor Layout

E-commerce’s growth has emphasized the importance of efficient space utilization. In fulfillment centers, increased movement and picking have amplified the costs of suboptimal layouts. Warehouses near population centers occupy costly land, further driving up expenses.

To address this, employing a WES can help identify frequently picked items, enabling the rearrangement of storage locations to minimize picking distances. Furthermore, exploring new racking and storage technology, such as a high-density automated storage and retrieval system (AS/RS), could significantly enhance space utilization.

3. Redundant Activities

Many warehouses suffer from unnecessary and excessive product handling, leading to cases or pallets being moved through multiple intermediate locations. Redundant activities, like duplication of information and data entry, also waste valuable time.

To address this issue, seeking solutions from the warehouse team’s input and consulting with warehouse technology specialists can provide fresh perspectives. Implementing a new layout or investing in a modern Warehouse Execution System (WES) could be effective ways to tackle these challenges.

4. High Labor Costs

Distribution and fulfillment center managers face mounting pressure to cut costs while enhancing service levels.

Although picking remains largely manual at present, various technological solutions are emerging. Exploring how technology can boost pick rates is crucial. Implementing pallet flow systems and Automated Storage and Retrieval Systems (AS/RS) technology should be integral to the solution, as they can significantly improve efficiency and productivity in the picking process.

5. Managing Returned Items

E-commerce growth has led to increased return volumes. Informal or poorly planned systems result in double handling, and inadequate record-keeping leads to time wasted on resolving queries.

To tackle this, begin by understanding product flow and documenting warehouse activities. Standardization and technology assessment should follow. Experienced automation providers can offer efficient solutions that may not have been considered before.

6. Product Diversification

Aligned with the growth of fulfillment centers, warehouses are holding a wider variety of items than ever before. While WES capacity isn’t usually one of the top warehouse problems, the increase in picking work most definitely is. As with space utilization, the answer lies in using WES to determine optimal storage algorithms to increase picking efficiency. Also, look at ways of reducing the physical work involved in fulfilling these picking tasks. It’s possible that an AS/RS system is the ideal answer.


To conclude, it is crucial to import data into your data warehouse from multiple sources to get a detailed view of your business processes and operations. Due to this, all major modern data warehousing and management solutions should help integrate famous cloud-based databases, applications, and platforms like Oracle, Redshift, MS Azure, and Snowflake. By driving the specific capabilities and features of these data sources and including them in the data management process, you can enhance your business operations efficiency and increase utility.

Frequently Asked Questions

What is data warehouse?

A data warehouse is a specialized data management system designed to facilitate and empower business intelligence (BI) tasks, particularly analytics. It is dedicated to executing queries and analyses and typically holds vast quantities of historical data. The information within a data warehouse is usually sourced from diverse origins, including application log files and transaction applications.

Use of data warehouses in organization

A data warehouse serves as a centralized repository that consolidates extensive data from various sources. Through its analytical capabilities, organizations can extract valuable business insights, enhancing decision-making processes. Over time, it accumulates a valuable historical record that proves invaluable to data scientists and business analysts. Due to these capabilities, a data warehouse is often regarded as the organization’s “single source of truth.”

What are data warehouse characteristics?

Subject oriented, integrated, time variant, and non-volatile are four main characteristics of a data warehouse.

What are the advantages and disadvantages of data warehouse?

Common advantages of data warehouse are faster and more efficient data analysis, improved data quality, better decision making, increased data accessibility, and cost savings. On the other hand, common disadvantages of data warehouse are data silos, limited flexibility, costly setup and maintenance, data latency, and data security.

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