Unleash the power of your data with an Enterprise Data Warehouse strategy

Many businesses work with disparate, unstructured data sets, making it harder to take analytical-driven decisions.

This is why a sound Enterprise Data Warehouse (EDW) strategy is essential in appropriately storing and managing the huge volumes of data at hand and leveraging it to gain insights. Such a strategy can also help businesses become future-ready and more sustainable, reduce uncertainties, foster new business opportunities, and strive towards operational excellence.

However, creating and implementing an EDW strategy comes with its own unique set of challenges, which must be addressed at the outset.

Challenges in creating an EDW strategy

Though businesses are aware of the advantages of a sound EDW strategy they are not always sure how and where to start. The EDW transformation process is complex and requires a clear plan to execute as well as a trusted, experienced partner to guide this process while understanding business use cases.

Some of these commonly encountered challenges are:

Poor data auditing

An inability to fully audit existing data sets and workloads and gauge the level of complexity within them puts businesses at a disadvantage and doesn’t allow for a clear strategy to be developed.

No clear ‘end-game’

Businesses must have an idea of what the final EDW transformation plan entails. This can determine how and where to implement a strategy and what they need to do to reach their designated goals and objectives.

Unclear allocation of time and resources

The strategy needs to factor in the time it will take to execute, what resources are required, and whether current talent is equipped to support this process. Many businesses start the transformation process but fail to realize the scope of the task. This can lead to several roadblocks and hiccups along the way and even derail the entire process. This can impact costs and cause budget overruns if not considered at the outset.

Technical debt

Businesses may prioritize a shorter work time over a more substantial approach. However, by following the former route they could face hurdles during the process leading to an increase in costs, resources, and time.

Data disruption and loss

Any downtime has a detrimental effect on business functioning. This, combined with apprehensions over data loss and maintaining data quality and integrity during the transformation process, raises fears within businesses before undertaking the process.

Security and risk

Security and risk are key concerns facing businesses and a lack of clarity over data privacy and ethical concerns over data usage can hinder the transformation process.

Regulatory issues

Businesses are not always up-to-date with the latest regulatory compliance and legal issues surrounding data transformation and need to constantly keep abreast of changing rules.

Data architecture

Businesses must determine how best to design an elastic and resilient future-state data architecture, which is both accessible and flexible and facilitates better collaboration between cross-functional teams.

How to start?

There are several options available to businesses looking to migrate their data but the two most common are:

Migrate on-premises systems to the cloud

Optimizing existing infrastructure for optimal cloud performance


Creating platforms and tools from scratch (more time and resource intensive but easier to customize and scale)

Where to start?

Depending on the specifics of each business, the volume of existing data, and what the intent is, players need to assess several factors:

Analysis of workload spread

Depending upon the workload spectrum, the target cloud-native services must seamlessly and efficiently operate in an integrated fashion.

ETL or BI heavy

Based on the workload spectrum, businesses need to determine the right approach to take. For large volumes of data that require in-depth analysis with statistics, dashboards, and visualizations (commonly used by CXOs and sales teams), business intelligence (BI) is crucial in this process. The Extract-Transform-Load (ETL) process within an EDW relies on pulling the existing data, transforming it into a specific standard, and converting it to suit the destination EDW. These two are usually disparate processes and can be dealt with in logical phases.

Phased approach vs. big bang

Deciding timelines and priorities for data transformation is crucial before executing. This must be done by taking into consideration the volume of data, types, and level of complexity.

Technical debt (performance)

Technical debt restricts workload performance and can obstruct meeting the desired performance SLAs as well as consuming higher capacity and increasing cost exponentially. This can be controlled through workload optimization and performance tuning by following best practices of the cloud and workload optimization where the scripts/queries etc., are not written with the perspective of a cloud-native mindset.

Cost management

Clear budgeting of the task, considering time, resources, and management. This also includes another dimension of cost on the target cloud side where the legacy workloads should be optimized while on-the-move so that the price-performance ratio is controlled on the cloud side.

Accurate gauging of data volume and quality

Auditing existing data both by volume and type can speed up the transformation process.

Checking compatibility issues

Choosing the right destination for the data when migrating requires assessing compatibility and related risk issues.

Extensive assessment of architecture, infrastructure, complexity

Examining current IT infrastructure and architecture can free up time overall, especially when gauging the complexity of the data to be transformed.

Developing a roadmap

A solid EDW strategy should start with a roadmap to understand the magnitude of the task, create milestones, and ensure everyone is onboard during the process and with the end results.

Assessing level of automation

Shifting to an EDW is not easy and can take time when undertaking manual processes. Assessing which tasks can be automated can save time and effort.


Though modernizing legacy data warehouse workloads to a modern stack is no easy task, there are definite benefits in doing so, including the following:



The importance of a sound EDW strategy cannot be understated. The benefits to businesses in having clear, consistent, and streamlined data allow for better data quality and security and ensure its usage can be leveraged and optimized.

EDWs enable businesses to know what customers want and provide them with better services. By using accurate and ready-to-use real-time data, which can be analyzed with BI/analytics tools, businesses can gain deeper insights into their preferences and offer them customized products and services.

Modern data warehouse platforms can leverage automation to streamline and accelerate workflows, spot and eradicate errors, and save time and costs for businesses. Businesses can also automate repetitive tasks, reduce manual errors, and increase efficiency when managing in-house processes.

They can encourage greater collaboration due to centralized, easy-to-access shared data, which can encourage greater teamwork and raise productivity.


As businesses look to move towards a new future, they need to leverage their existing data to help them make better informed decisions. They need to be able to quickly and effectively scale, respond to customer demands, and apply data-driven insights while harnessing the power of next-generation technologies and automation.


Gurvinder Arora
Senior Lead Technical Writer