Big Data in Software Development

Mariel Lettier

Table of Contents

It's All About The Data
Where Does Big Data Come From?
Big Data Applications: How Is Big Data Used?
Artificial Intelligence & Big Data
Data-Driven Approaches
Developing A Data-Driven Mindset
Big Data & Data-Driven Software Development

Data Analysis seems to be everywhere you look in the software development business. It comes as a quite useful tool for every step of the development process, even after delivering the final product. That’s why, in this article, we’ll look into two related topics. These will be Big Data and Data-Driven approaches in Software Development. We’ll first give you an overview of both concepts, delving into their benefits and their relevance. Also, we'll take a look at big data applications, how big data and AI relate, and how to develop a data-driven mindset. Finally, we’ll look into the significance of Big Data and Data-Driven approaches.

It's all about the data

What is Big Data?

The term "Big Data" is pretty straightforward. It refers to extracting meaningful insights by analyzing massive, complex collections of data. This amount of data usually exceeds the traditional database software's analysis. Let’s take a look at its main characteristics!

The 5 Vs of Big Data

Volume: This would be the size of the data, usually measured in terabytes, petabytes, and exabytes. In 1999, one gigabyte of data was enough to get the name Big Data but, as you can imagine, that's no longer the case. Today, it should be larger than terabytes and petabytes.

Value: The value of Big Data stems from insight discovery and pattern recognition. To achieve this, large amounts of data go through processing protocols. This leads to higher efficiency, better customer relations, and other business benefits. That’s to say, Big Data adds a lot of value.

Variety: The data in Big Data is quite diverse. This included different types of data. To name a few, there is "unstructured", semi-structured, raw, and dense data.

Velocity: Big Data's velocity applies to the speed at which data gets accumulated. This, understandably, can be very high. So, if you can't process big amounts of data fast enough, it won't be as useful as it could be.

Veracity: This relates to how reliable or accurate the data is. Given the large amounts of data at play, this can be the hardest part to control.

Why is Big Data important?

Why is Big Data important?

Big Data tools reduce storage costs and save time when it comes to data analysis. For instance, the analyzed data translates to a better understanding of market conditions. As a result, you'll be able to make good decisions faster. Also, it helps to develop and market innovative products and services. Other benefits include boosts in customer acquisition and retention. All in all, we’d say there are many reasons why big data is so important. If you still have some doubts, we’ll mention some other big data benefits below.

Big Data Benefits

Big data offers benefits at every level; from IT to business and including at an enterprise level. Let’s take a look at other of the varied benefits big data brings to the table.

• Higher Speed
• Fraud Detection
• Risk Management
• Improved Efficiency
• Improved Operations
• Increased Productivity
• Better Customer Experience

Where Does Big Data Come From?

Where Does Big Data Come From?

Data is everywhere around us, but sometimes it is difficult to realize the scope of it. For example, each of us generates about 1.7MB of new data per second. Also, there are over 50 billion smart devices around the world. These are able to generate and analyze data. If that sounds too vague for you, below are some of the main sources of Big Data.

Social Media: With a large percentage of the world’s population using social media, this is a major source of data. Every photo or video upload, every like, every comment, and every message you send creates data. Multiply that for people using Facebook, TikTok, Twitter, Instagram, and other platforms. As you may guess, the result is massive.

Sensors: Many cities have equipment with sensors to collect data on various weather factors. Some of them include temperature and humidity. Also, there may be traffic and security cameras collecting other types of data. Adding locations and cities, numbers get quite high.

Customer Feedback: How many times did you get asked for feedback after acquiring a service or product? Even though not everyone provides it, it’s safe to say, those who do amount to quite a large number. For example, Airbnb may ask you about the features of the flat you stayed at. In this scenario, retailers ask for different details of the purchase. These can include the time of delivery and the state of the packages. Other instances include telecommunications companies asking for feedback on their services.

IoT Appliances: Appliances connected to the internet are increasing. These products also collect and store data. From your Smart TV to your Smart Printer, even including your Smart Coffee Maker.

Transaction Records: Financial transactions have records. Whether it’s an e-commerce transaction, a banking or business one, there will be data behind it. There is also the data generated by credit cards, debit cards, or any other type of electronic payment.

These are just a handful of examples. There are also medical records, GPS, emails, documents, mobile apps, and system logs. The list is endless.

Big Data Applications: How Is Big Data Used?

Now that we know where big data comes from, let’s see what it’s used for. From what we’ve seen so far, one might get the idea that big data is only a way for businesses to make money. Yet, this information gets used for a wide variety of applications. These can result in long-term benefits for everyone. Let’s take a look at some of them!


Hospitals can use data collected from patients for evidence-based medicine. As a result, this saves time and money in tests and reaches diagnosis in less time. Further, patient data can predict the progress of a patient’s condition.


Telecommunications and entertainment companies use data to solve problems. Some of these include creating data for specific audiences and recommending content.

Traffic Control

Big Data gets an amazing usage to manage traffic in cities where congestion is a big issue. In consequence, traffic management means better efficiency and livability within high congestion levels.


Here, Big Data reduces product defects, improves quality, and increases efficiency. Not to mention that it saves time and money. Moreover, it's helpful for shipment data, demand forecasting, and advanced robotics in manufacturing.

Search Quality

This is one of the most well-known Big Data applications. When using Google or other Search Engines, data gets used to improve results. This means that the next time we use it, the search quality will have improved.

There are also countless other Big Data applications. For instance, in fields like education, insurance, retail, transportation, and natural resources.

AI and Big Data

A lot of people have questions about the relationship between big data and AI. Well, AI uses data to improve, and the larger the amount of data, the better. So, Big Data plays a key role in Artificial Intelligence's results accuracy. Yet, the relationship between Big Data and AI also goes the other way. This means that Artificial Intelligence is necessary to process these amounts of data. As a result, Big Data and AI have a symbiotic relationship. What's more, it's expected this bond will lead to AI feeding Big Data to itself. At the same time, it will make data analytics less labor-intensive. Not too far from now, we’ll be seeing Big Data and AI combinations making our lives easier.

Data-Driven Approaches

Data-Driven Approaches

What Is A Data-Driven Approach

Data-Driven approaches are those when decision processes rely on data analysis and interpretation. When it comes to making decisions, you rank data over experience or intuition. Further, for a data-driven approach to succeed, data must be accurate and relevant.

Why Is Data-Driven Decision-Making Important

Data-Driven decisions reduce risks, save costs, and increase proactivity. What's more, they decrease decision bias and are more objective than other approaches. Above all, there's a simple fact: data does not lie. It helps to predict future trends and raise success chances. Moreover, it also generates higher levels of revenue. That's what makes Data-Driven analytics key for any business.

How To Develop A Data-Driven Mindset

You now know what is to be data-driven and why it matters, but how can you work on developing a data-driven mindset? We’ll give you some tips on this below.

Work on Data Literacy

Your team should be able to understand the data so they can use it the right way. You can make use of data visualization, develop new processes and provide training. If team members don’t understand the data and why it matters, there's no use for a data-driven approach.

Learn To Look For Patterns Everywhere

A data-driven approach means being more analytical and looking for patterns. This mindset not only applies at work but also in your everyday life. As a result, you'll be able to train your brain to become more Data-Driven.

Be Aware of Biases

Sometimes, our brain sees what it wants to see instead of what the data shows. We all have our biases, and that’s ok! Being aware of them will help reduce their impact on the decision-making process.

Get Skeptical

Being Data-Driven entails asking questions and using data to answer those questions. This means you can’t take everything you’re told at face value. Question everything and reach your own conclusions based on data. Also, as we’ve mentioned before, this data has to be accurate and relevant.

Embrace Failure

You’re not going to win every time. When you fail, make the most of it and learn from your mistakes. They will provide valuable data that you can use for the next round.

Big Data And Data-Driven Software Development

Big Data And Data-Driven Software Development

When it comes to Big Data, like with AI, there is a two-way relationship with Software Development. Developers are creating apps that use big data. Meanwhile, they're using Big Data to streamline their development processes. If you use Big Data in a dev process, it inherently means using Data-Driven Analytics. This is only natural as a Data-Driven Software Development model has come to be the standard in the field. Moreover, this means you get the benefits of both a Data-Driven approach and Big Data. To name a few, you can increase your revenue and efficiency, and decrease decision bias.

An important highlight is that Big Data Development is not for everybody. Actually, it takes fast processors and sophisticated software analytics. That's the only way to provide meaningful feedback from massive amounts of data. Yet, if you could leverage a Data-Driven process, you'd also get near real-time analysis. And, as you may know, this comes as an invaluable tool in Software Development and Testing. Another relevant thing is that, as we've mentioned, it's not all about business and profits. Over the last few years, many studies about Big Data & Development have seen the light. Within these, a constant is the amazing possibilities of complete integrations. Some of these encompass fields like education, agriculture, and even humanitarian emergencies. If you want to know more about this, you can read one of the studies here.


As we’ve seen, Big Data applications and Data-Driven analytics have incredible potential. Of course, they seem to be the future of businesses and Software Development. But, they will also provide us with incredible opportunities to improve our quality of life. We hope to have given you a broader perspective of big data and data-driven development. What do you think they will bring us next?

Today, Big Data is a critical part of Software Development. Unleash your audience's potential! We can work together to create data-driven products and services, focused on each audience to create a never-seen user experience. Let's do it!

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