The largest names in tech lead the way in data driven development (DDD). Here, we’ll look at what data driven development is and why it’s important. Also, we’ll discuss the role data engineering plays in data driven software development.
Looking back, tech companies did something important from the early beginnings. Developers and engineers understood the growth potential of one key factor that could be a stepping stone to something larger. What was that? Data.
Nowadays, it’s common and necessary for companies to practice attentive data collection and data management for their organization. This is because data can improve decision-making across multiple business levels.
Nearly all business investments, large or small, are based on data parameters. These data parameters can include the collection of things like location, search history, customer demographics and more. Using this and similar data, companies in turn gain advantages over those whose decisions aren’t based on data, or worse, invalid data.
Therefore, it’s important to understand if you’re investing in any custom software, web application software, data organizing practices and data functions should be an important component. With software projects, you’ll also be working closely with data engineers, so we’ll look at what you should keep in mind.
What is Data Driven Development?
Let’s start with the basics. According to Wikipedia, data driven development (or programming) is defined as “a programming paradigm in which the program statements describe the data to be matched and the processing required rather than defining a sequence of steps to be taken.” Let’s break this down.
This simply means that data driven development, or DDD, is the practice of developing software systems that are programmed to execute unique tasks based on the data that’s present, rather than programming the same action for any and all data sets.
Nowadays, companies use DDD to solve different problems in different ways. Let’s look at three data driven development examples where it’s been implemented to further enhance user experience and the overall service product.
Amazon Use Case
One big con that comes with online shopping is the price of shipping. It costs a lot to ship products across countries. To tackle this, Amazon uses the data they have regarding their customers to determine the warehouse logistics in order to reduce the shipping costs for the company and users.
Also, before data driven development was implemented into Amazon stores and other e-commerce platforms, product recommendations were all over the place. Users searching for products on the website (window-shopping) were being recommended products that were not related at all. That’s a wasted opportunity! Over-time, customers wouldn’t glance in the direction in the recommended products section.
The implementation of data driven development has led to the integration of personalized shopping experiences and improved warehouse logistics. Increasing sales and reducing costs.
Software now analyzes the purchasing patterns of customers from previous transactions, shopping carts, or their famous “Wishlist” section to improve experiences. All metrics help predict and recommend new products while also automating sales data to the most cost effective choice.
This has helped Amazon and vendors increase sales from customers who enjoy “window-shopping” by motivating them into making satisfying purchases on products they never knew they would love.
The results, you ask? A report on Sellerapp says that 89% of online shoppers prefer buying products from Amazon than on any other website. And we know by now the sales numbers Amazon has accomplished through technologies and advance in data driven software.
Youtube Use Case
In the first 9 months of 2021, YouTube generated $20.21 billion according to investopedia. What does Youtube sell? Well, advertisement is the main source of revenue for YouTube. Many firms advertise their products on YouTube hoping to get traffic to their products from YouTube’s users.
YouTube on their part uses data driven development to make sure these advertisements go to the right audience so as to be able to generate maximum traffic for the companies advertising on their platform.
Youtube has continued to develop and integrate software algorithms that improve data storage and management. This helps them to know the products/services/topics that interests their users the most. It’s a large database of profiles, interests, dislikes and geo-locations.
It has far surpassed the basic idea of only tracking viewed content. Things like time-on-site, video category type, and the device types add to the information. Their evolving software uses historically collected data to suggest other videos users might find useful. For example, things like:
- Videos Liked by other people who also watch the videos you like
- Videos Liked by people who live around your location
- Videos from the same channel
- Videos from related topics
Data driven development creates data algorithms in order to make this possible. Now the right advertisements go to the right audience. Advertisers can be more targeted and generate traffic to increase sales and long term growth.
Spotify Use Case
For every song that’s streamed on Spotify or any platform, there’s data behind it. Every time songs are played on Spotify, data is being gathered on how long a user spent listening to a song, the music genre, the artists they enjoy most, among other things.
Here’s a unique gift Spotify shares with its listeners. Spotify is known for creating Daily Playlists based on the music interest of each user. Even better, every year Spotify users await their “Spotify Wrapped” report. It’s a report card on how they listened to music the past year. This report thrills users by providing how much they listened to specific artists or songs.
Spotify also gathers data on the locations that listen to a particular song the most. This data is what artists use to market their music to specific locations through tours and shows.
Before the era of streaming, musicians had marketing teams who reviewed the analysis of album sales and radio plays to know which countries are the best touring markets. They couldn’t instantly and accurately prove which songs were more popular.
In the era of streaming and with the application of DDD, musicians and record companies are able to know which songs in an album users enjoy the most and what type of music users enjoy listening to. At an instant, reports and data can be sorted and shown.
DDD provides answers in different ways, it gives companies a better idea about the service and the users they are serving. This helps companies improve and launch new services in order to satisfy user demands, simple and faster.
What Is Data Engineering and Why it’s Important
A course on Coursera defines Data Engineering as “the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with useful applications in just about every industry.” But, let’s look at these more closely.
Data engineering is practiced by software engineers whose role is to build softwares called data pipelines to efficiently collect and manage data. This type of software engineer is known as a data engineer.
Further, data pipeline development is used to funnel data from one source to a different destination. This practice can be used for scraping data from other websites, a database, an API or even hardware devices like a GPS or a phone.
A data pipeline uses a set of steps and processes to automate the movement and transformation of data between a source system and a target repository. In layman terms, it is a pipeline (a software system) that helps transfer data to where it is needed.
Importance of Data Engineering
Earlier, we discussed different use cases where data driven development has been applied but we need to discuss the role of data engineering using a couple of use cases.
There are several application areas where data engineering is key. Let’s look at three cases where this is true.
Importance of Data Engineering in Developing Machine Learning Models
According to the Oxford dictionary, Machine learning is “the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.”
More simply, machine learning is the practice of programming a software to learn and make self improvements based on a set of parameters. The part of the program that is being trained (or undergoing learning) is called a Machine learning Model. It is also known as the “brain” of the program.
Microsoft further defines machine learning models as “a file that has been trained to recognize certain types of patterns.”
Taking this idea, we can see this is where a data engineer’s skill comes into play. Engineers will build pipelines that specify and allow for the gathering of data to make machine learning models possible. The data engineer makes sure the right data is gathered in the right format and to the proper location, in order to optimize the “learning” process for the machine (software).
Importance of Data engineering in Data Analysis
Wikipedia defines Data Analysis as “a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.”
As described before, data provides opportunities. For example, an e-commerce company can integrate data gathering software to incorporate specific data about users. This can include customer location and the products they buy in order to analyze the kind of products they need to stock.
They can also use it to make smarter decisions on advertisements and product expansions. It’s through data analysis that decisions based on data provide the best opportunities for improvement and growth.
Importance of Data Engineering in Data Storytelling
Storytelling through data is said to be “the ability to effectively communicate insights from a dataset using narratives and visualizations.”
As the name implies, it’s a way of telling a story based on available data. A big example is a monthly financial review. For example, financial companies can release figures and trends to their investors every month so they know how the company has been managing their portfolio allowing them to make smart modifications.
A data engineer makes this possible through practices we mentioned before, data pipeline creation. Systematically, they construct data highways in the software that makes this possible. With proper data engineering practices, the software collects, filters and arranges data for a story to be told.
Summary and Conclusion
Data driven development is a cemented paradigm in software development and is continuously being implemented in modern softwares. The use-case of data driven development spans across several industries, from e-commerce, to finance and music. Data engineers are key in developing data pipelines used to gather data across several sources.
Being an integral part of DDD, data engineering is used to build and offer unique solutions, from storytelling through data, to data analysis and developing machine learning models. The application of DDD into the development of software is a proven way of offering better services and solving more problems with future scalability.
At Endertech we have multiple data-driven development pilots across our portfolio. Regardless of the business size, companies can take advantage of data-driven software today and gain the advantage. Start by researching or talking to your developers. Or, you can also reach Endertech; we have great minds ready to help you and your company transform!