Agile Data Warehousing Explained

Editorial Team

Agile Data Warehousing

The secure electronic storing of information by a business or other organization is known as the data warehouse. The main purpose of data warehousing is to build a repository of historical data which are accessible and could be retrieved. The data are important to be examined in order to provide helpful information and insights for the sake of the company’s activities. Usually, a data warehouse is used to integrate and analyse corporate data from many sources. The data warehouse is considered the heart of the business intelligence (BI) system, which main roles are to analyse and report data.

In the past, data warehousing projects used to be a long and multi-million dollar undertaking with a significant risk of failure. Waterfall techniques prioritise vision achievement and needs over speed. They needed the company to describe a clear future state that was frequently out of date by the time it was delivered. However, most businesses were unwilling to accept such high-risk enterprises. As a result, they opted for agile methodologies. As time passes, agile methodologies have been widely practiced in companies and this methodology has gradually seeped into data warehousing, which is known as Agile Data Warehousing. Now, agile methodologies are adopted in data warehouse and business intelligence initiatives more than before.

In this article, we are going to look into agile data warehousing. Some of the topics that are going to be covered include the definition, processes, benefits, and so forth.

What Is Agile Data Warehousing?

The key focus of agile data warehousing is that it uses an iterative approach to keep business users involved. Each cycle develops on the preceding one, introducing new product features until the entire project is done. In order for the business to continuously evaluate the results, each iteration or sprint will include analysis, design, coding, testing, and deployment.

The main core of an agile methodology is delivery. The ability to reliably provide whatever you are accountable for is the fundamental purpose for processes, regardless of what you call it. Hence, in this article, we are talking about creating a data warehouse.

How Does Agile Data Warehousing Differ From The Traditional Method?

Agile data warehousing techniques emphasise producing an end-to-end Minimum Viable Product (MVP) through iterative cycles. The processes are chunked into smaller parts which will be released one by one after one iteration has been completed. Implementing this method improves delivery speed while also allowing the business to witness progressive outcomes. Besides, this will also allow the development of a more comprehensive data-driven approach over time.

This is in contrast to the more traditional method which is known as the Waterfall methodology. When applying the Waterfall technique, all the specifications and requirements are to be planned in detail before it is handed for the building process to the development team. Once all of the work or major chunks of the work have been completed, then only they could proceed with commentary and improvement.

The same concept is applied to data warehousing.

Catching Up With The Complexity Of Technology

As efforts were made to take advantage of developing technology, complexity rose as well, resulting in a huge network of multiple data storage, Extract, Transform, Load (ETL) procedures, as well as independent apps. The inefficiencies inherent in the old waterfall process became increasingly evident as the warehouse grew in complexity.

Despite the business intelligence team’s effort to produce project updates every six months, the business sponsors may still consider the delivery to be delayed and costly. The low amount of business interaction, as well as the constant stream of exceptions and changing needs, posed a challenge to the team. Sometimes, missing deadlines and estimate misses are inevitable and this may affect the morale of the project management team.

Hence, from all of these challenges that often occur while using waterfall methodologies, agile processes in data warehousing may aid a lot in overcoming these. Agile methodology is helpful since it could allow coding to begin as quickly as possible with minimal expenses. It is proven in many cases that stakeholders and business sponsors are pleased with the swift results as well as the minimal limits when agile is applied to the data warehouse.

Benefits of Agile Data Warehousing

Agile data warehousing brings benefits since the main goal of agile principles is to reduce delivery time and increase flexibility during the development processes. Flexibility will ensure a quick response to both technological and business changes. Here are some of the benefits of implementing agile methodologies in your data warehouse.

1.      Enhance The Speed Of The Delivery

Agile data warehousing aims to improve time to value. Starting with a certain subject area or data source and working up from there, form data input to data visualisation, helps the company to see quantifiable results fast and reinforce their goals. This method allows for a more collaborative approach to delivering benefits sooner. When the data is retrieved faster and results could be seen quicker, then better judgements and decisions could be made sooner too. Most significantly, business intelligence tools could provide the team and stakeholders with measurable features on a regular basis to ensure the project is on track with what was initially planned.

2.      Allow Enhancement And Improvement Framework

Agile data warehousing is recommended since it allows and provides clearer improvements that could be made for the consequent sprints and iterations. This is proven since agile methodologies will follow the typical plan, act, demo, and retrospective cycle where the work indicated in the project plans is chunked into a smaller time frame. A backlog of autonomous data warehouse approaches could be applied where the main objective is o gradually incorporate them into the delivery stage. Implementing agile will enhance the involvement of the team and stakeholders actively and instantaneously.

3.      Improving The Project Transparency

Implementing agile data warehousing will maximise the involvement and engagement of stakeholders in the project. This will improve the transparency of the project since everyone involved will be able to know and understand the work scope associated with the projects that are being worked on. Due to this reason, idea sharing and difficulties identification would be so much easier to be done.

4.      Enhance The Predictability Of The Delivery

Implementing agile may help to improve the forecasting of the delivery. With better predictability and forecasting, this will sustain the credibility of the business team and simultaneously improve the stakeholders’ and customers’ tolerance towards the product delivery since they knew what to expect from the current or future sprints. Multiple tools could also be utilized in order to keep track of daily progress.

5.      Improving Risk Management

The main concept of agile methodologies is to segregate the tasks into smaller chunks. This will enhance the efficiency in managing risk as well as reducing the complexity of the task. Implementing agile may give you a series of minor setbacks and even victories along the road. This is much better compared to a single catastrophic failure that could bring the entire project down to a halt. Hence, agile data warehousing aids in the reduction of risks and the remediation of specific locations that may have been impacted. The identification of failures could be tracked more easily in the smaller chunks and could be helpful for the team to come up with swift answers and solutions.

This may also be helpful since the previous iteration may provide insights on the risks that may occur. Hence, by referencing the previous sprint, the decision-making could be performed quicker.

Agile Data Warehouse Processes

Before we specifically study the processes for agile methodologies on data warehouse, let’s have a look at the general steps for data warehousing.

Below are the steps of data warehousing.

  1. Establishing the project goals
  2. Gather and analyse information
  3. Highlighting the main processes of the project
  4. Build the conceptual data model
  5. Identify the sources of data and arrange the data transformation plan
  6. Establish the tracking duration
  7. Execute the plan

When looking at the processes of data warehousing, we could integrate the processes with the main components of agile methodologies.

When building the team member of agile, it is important to remember that the team would be independent and cross-functional, with no regard to the current hierarchy of the organization. The team should be made up of a suitable number of members that will be fully responsible for the tasks that supply the functionality required by each sprint or iteration.

One of the agile principles is flexibility and allowing changes to be made throughout the processes. When establishing the project goals, be reminded that the project would be evolutionary. Don’t be too rigid on the initial plan established. Be prepared to allow changes to be done in the processes done in the project.  Remember to regularly conduct meetings to track the progress and obstacles that were faced during the processes. This may be noted and could be helpful references for future iterations.

Limit the duration of each sprint. You do not want to delay the delivery time when putting quite a long duration for each iteration established.

Agile Data Warehousing Best Practices

1.      Process Cycle Efficiency

One of the most common practices used in agile data warehousing is known as the Kanban framework which is effective for a disciplined team. Kanban could assist your team in evaluating and progressively improving their delivery strategy. One of the key benefits of applying Kanban is due to its strong use of measurements to drive important decisions. Aside from that, Kanban’s main emphasis is on minimizing waste and enhancing predictability.

Kanban will allow clear visualisation of work as it progresses through the processes of delivery and will track the time spent in each stage. Lean value-stream mapping approaches are applied in the Kanban framework which will assess the data warehouse or the business intelligence of the team’s delivery cycle’s process cycle efficiency (PCE). Process cycle efficiency or PCE is defined as the percentage of time spent on value-added work as a percentage of the total time it will take for a request to become valuable or delivered to the company.

The formula of PCE is as follow:

Process Cycle Efficiency (PCE) = Value-Added Time / Cycle Time

2.      Reducing Waste

Analysing each sprint or iteration using the PCE formula above is helpful since it will give the team insights revolving on the metric they need to cut waste and releasing get value to the stakeholder faster. It will help calculate and identify the obstructions and inefficiencies in the processes. The key objective here is to find out how much waste could be reduced over time. Doing this efficiently will lead to process efficiency cycle boost and generating values more quickly.

There are two suggested and effective ways to reduce waste which are by minimizing work in progress (WIP) or by making changes for the process for improvements. In every sprint, there will be a waiting time between the tasks or processes done. Limiting the number of work in progress will reduce waste since it will also lower down the waiting time between the processes for each sprint.

As for the other way, which is by improving the processes, team members could use some of the extra time to optimise their work to create value faster in the consequent sprints. Some of the things they could do are by developing and putting a new test automation framework in place. Another task that they could work on to make improvements in the processes includes researching about hyper-modeling techniques that will allow rapid and efficient adjustments whenever changes are necessary to be done. One of the things the team member could work on is to standardize the documentation on the project needs and requirements.


These are the explanations for agile data warehousing. This methodology is highly recommended to be implemented in your data warehouse since it would further ease the process of data storage as well as data retrieval for your company or organization. For further information or ways on how to efficiently implement agile data warehousing, you are suggested to look and research for more information either online or by attending courses and coaches which may provide more practical insights for this methodology. We wish you the best of luck in doing this, either starting data warehousing from scratch or transforming the existing data warehouse into agile methodologies instead.