How does the Agile methodology apply to data warehouses?
The agile methodology is a way of looking at project management, it also provides an ideal framework for looking at data warehouses.
Data warehouses, large containers where business data is stored and accessed, often seem like monolithic structures that are fastened in place. Instead of something that moves, develops, and grows with a business, they are sometimes seen as unknown and rigid entities.
On the other hand, the agile methodology seems to be continuously flowing, adapting, and changing based on client demands. Due to this, it’s not hard to see why people believe these two concepts – data warehouse and the agile methodology – are completely incompatible.
Yet, as you delve into the benefits of the agile methodology within project design, it actually becomes apparent that these fields have more in common than one initially would think.
In fact, software developers are taking to the agile framework in droves, with 85% of international software development companies preferring this framework to deliver projects more than any other style. With this vast adoption, it’s no wonder that we’re starting to see this methodology bleeding into other areas of tech.
In this article, we’ll be breaking down the agile methodology, tracing its benefits for business, and then connecting it to its similarities within data warehouses. By the end of this article, you’ll see that these concepts are much more alike than once thought.
Let’s get right into it.
What is the agile methodology?
Agile methodology is a method of product management that focuses on having many iterations with different sets of feedback instead of delivering one final product.
This form of project management anticipates problems along the way, allowing clients to continually give their feedback and ensure that the final product is as close to what they want as possible.
While a typical project management strategy would listen and converse with the client, understand what they want, then deliver that as a final product, there is a vulnerability in the fact the client could simply not like it.
Working by using agile methodology ensures that teams are better aligned with what the customer wants, ensuring that they understand the process and all adaptations at all times.
What are the benefits of using the agile methodology?
There are many benefits to using an agile methodology when moving through a project. Spanning from reducing risks to increasing speed and getting better results, there’s a lot of weight behind this project management strategy.
Typically, when using an agile methodology, project managers can expect to:
- End up with a better product
- Boost customer satisfaction
- Reduce risks
- Increase flexibility
Let’s break these down further.
Better Final Product
Using an agile methodology ensures that the project is continually refined over its duration. Instead of submitting one final project that could result in scrutiny, you’ll be able to continuously develop closer and closer to what a client wants as an end product.
This process of learning and growing will always result in a better final project, ensuring that your team hits the brief out of the park.
Due to the many different iterations of your project that you will present, the agile methodology always ensures that the customer is involved to a much greater degree.
This sense of inclusion makes sure that they can never turn around at the end of a project and disagree that you have completed the brief, as they would have been an active agent in informing the final product.
As they’re involved directly in the product, you’ll be able to quickly work through roadblocks by communicating with them and developing your vision for the project as it coincides with their main goal.
By making all of your changes dependent on feedback from these users, you will continually be pushing for a better product, boosting the chance that the customer is happy with the final design.
In fact, this is so often the case that 25% of projects that follow this methodology are significantly more successful and profitable than those that follow traditional development formats.
As a side effect from including the customer in the process to such a high degree, there is a greatly reduced risk of the customer not liking the final product.
Equally, as these are smaller sprints with small changes that are then presented, if something isn’t to the client’s taste, you’ll only be losing a small percentage of your total work.
When this is compared with a typical project development sprint, designing the whole product from zero and then presenting it, you can instantly see the distinction between the levels of risk.
If a client doesn’t like a traditionally created product, you have to start again at zero. If they don’t like your most recent iteration, you need only move back a few days of work.
Using this agile methodology, the risk associated with taking creativity into your own hands is significantly reduced.
In traditional project schemes, there is one single goal – finish the project. Due to this, teams are driven to complete this one major requisite, often spending weeks or months on developing towards a singular goal. With these constraints on finality, there is much less room for flexibility during the process.
Whole projects can rarely be planned without something springing up in the middle that needs refining, changing, or adding to the project.
When developing a project using the agile methodology, you’ll be able to suggest new features or additions to the client with ease, ensuring that they have a more extensive final product.
Additionally, with the extra iterations and less risk that come from them, there is significantly more flexibility for teams, making the whole process a breeze.
How does the agile methodology relate to data warehouses?
Much like project management moving through different iterations of a project, a data warehouse could truly be endless. A data model is never final because, at any moment, new data can be added that greatly shifts the internal significance of other datasets.
Due to this possibility, data warehouses are viable locations in which the agile methodology can thrive. Full data, modern or historical, will be loaded, collated, recollected, moved, transformed, and applied a potentially endless amount of times.
Equally, testing datasets can be pulled upon in countless situations, providing the backing for different pools of analysis. When a business wants to integrate data into its systems, it should be through an agile methodology.
Instead of committing to uploading all the company data in one go, businesses must realize that data is a continually updating and morphing entity. Instead of treating data warehouses as a finalized location for data, we must turn to see them as just the next iteration in our business’s continual progress.
Data that may seem final today will have new data points added tomorrow, which in turn will be incorporated into data warehouses.
Indeed, one of the main benefits that arise when looking at comparisons of BigQuery vs. Redshift, two leading warehouses, is their ability to continually expand and grow. As a business scales, the requirements that are expected of a data warehouse also begin to develop and grow.
When it comes to business, a data warehouse is never a finalized project but simply the next iteration during an extended agile methodology.
Although traditionally, the agile methodology is a way of looking at project management, it also provides an ideal framework for looking at data warehouses. Both projects run within these parameters as these warehouses are continually updated, seeking new iterations that serve their clients.
As businesses expand, continuously grow, and draw upon new data sets, the data warehouse that it uses must also increase with it. Due to this, new iterations are developed that directly align with how project management that uses the agile methodology is run.
While the connection between project management and data-driven analysis seems strange, it is actually incredibly similarly structured.
If we start to treat data warehouses as following the agile methodology, continuously updating and developing, we’ll be in a much better position to conduct continual business analysis.
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