Understanding Big Data Capabilities + Types and Use Cases

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Big data capabilities are the features of big data that enable it to be used for different applications and business needs. With big data, you can collect more information from larger volumes of discrete data sources and analyze them using advanced analytics techniques. 

However, using big data methods does require a certain level of sophistication.

As a startup, Aloa leverages the power of big data to make better decisions and drive more value from its data. With that said, we're here to share our insights on big data so you can apply them to your own systems. Since we provide software outsourcing consultation services, we also work closely with innovators, businesses, and startups to enhance their ability to leverage big data for their benefit.

Here, we'll go through what big data is, the types of big data, and the many use cases for it. Afterward, you'll clearly understand how big data can be leveraged for efficient and accurate operations.

Let's get started!

What is Big Data?

Big data is a term used to describe the vast amount of information generated by businesses, governments, and individuals on an increasingly frequent basis. These datasets are of considerable size and complexity that cannot be processed using conventional data processing applications. 

It is also a collection of technologies and techniques used to extract valuable insights from these large sets of data, enabling organizations to make better decisions. The main goal of big data analytics is to extract useful information and insight from gathered data. 

The gathered information helps businesses and startups make data-driven decisions that improve their operations and performance. Leveraging big data allows companies and startups to gain a competitive edge, streamline their day-to-day processes, and better understand their customer's needs. 

Characteristics of Big Data

Big data is able to process massive datasets in a shorter time than traditional data analytics methods. With it, it's easier to uncover behaviors across web-based clickstreams, social media data, customer behavior trends, sensor data, location-based data, and more.

With the right tools and techniques, organizations can gain valuable insights from their big data to better understand customer needs and wants or predict emerging market trends. Knowing that it's essential to understand the characteristics of Big Data.

The four main characteristics of big data are:

4 characteristics of big data
  • Volume: Big data typically consists of a large volume of data. It can range from terabytes to petabytes and even exabytes.
  • Velocity: The speed at which new datasets are generated is another characteristic of big data. This is referred to as velocity.
  • Variety: Data comes in many forms, such as text, audio, video, etc.; each type must be analyzed differently.
  • Veracity: Veracity refers to the quality of data and its reliability. It's important to ensure that your big data is accurate and up-to-date to make proper decisions.

As the scope and capabilities of big data increase, two additional characteristics have emerged as well:

  • Variability: Refers to how fast and how drastically the data structure changes.
  • Value: Refers to the potential value or insights from big data before making decisions.

Types of Big Data

Big Data is used to help businesses make informed decisions and create personalized customer experiences. Organizations use big data for various purposes, such as analyzing consumer behavior, predicting emerging market trends, and optimizing marketing campaigns. There are three main types of big data used by businesses today:

types of big data
  • Structured Data: Structured data types are organized and stored in a database. It includes fields, records, and files that are easy to store and analyze. Common examples of structured data include customer information, purchase histories, and web analytics.
  • Unstructured Data: This type of data does not have a predefined structure and is not organized in a database. Examples of unstructured data include emails, photos, videos, and web pages.
  • Semi-Structured Data: Semi-structured data also contains elements that are not organized. Social media posts are a common example of semi-structured data as they contain text, images, and videos in an unstructured format.

How Does Big Data Work?

Big data capabilities enable organizations to identify patterns or insights from large volumes of structured and unstructured data. Advanced analytics, such as machine learning and artificial intelligence, can be used to gain even more insight into big data. Machine learning algorithms can uncover patterns in the data to help identify customer behavior trends, predict emerging market trends, and optimize marketing campaigns.

Before jumping into big data analytics, it's ideal to outline a few prerequisites:

  • Clearly understand their data objectives and how to best utilize big data capabilities.
  • Find the right tools that can help extract insights from their datasets.
  • Have an experienced team who understands the use cases for big data to maximize its potential.

Once these prerequisites are in place, businesses can benefit from big data analytics' data-driven insights.

To get started with applying big data analytics to your business or startup operations, follow these steps for the best results:

5 steps of big data analytics

Step 1: Collect Data

In collecting data, there are several ways to go about the process. Ideally, you'll have outlined the goals and objectives of your data collection to ensure that the data is relevant and useful for your business needs. 

Part of that outline includes the type of data you are after and how you will collect that data. For the types of data, you'll collect, consider both structured and unstructured data to get the most insights out of your data. There are various options for collecting data, such as web scraping, buying datasets from third-party sources, and using APIs to collect data.

After collecting data, you won't want to use it in its raw form. Taking the time to clean and preprocess the information before using it for analytics will help ensure that your results are accurate and reliable. 

This includes removing any irrelevant or duplicate information and standardizing formatting across datasets. It's also essential to ensure the data is valid, meaning it's correct and meets certain expectations. Once your data is preprocessed and ready for use, you can analyze it with big data tools.

Step 2: Store Collected Data

Storing preprocessed data after collecting it is essential. Before doing so, you'll want to organize the data into a format your team can access and work with. It's also important to consider how secure your data storage solution is to protect against malicious attacks or theft of sensitive information.

When it comes to data storage, there are a handful of ways to go about it, the most common options being:

  • Cloud storage: This solution allows for scalability and ease of access since the data is hosted remotely.
  • On-premise storage: This solution is stored in an onsite server and requires manual maintenance.

The choice between cloud or on-premise storage will depend on your organization's specific needs and requirements. Small businesses and startups do well to opt for a cloud storage option which is generally cheaper and requires less maintenance, upkeep, and space. For larger businesses, using an on-premise storage solution may make more sense as data can easily be accessed anytime, with more control.

Step 3: Analyze the Data

Once you've collected and stored your data, the next step is to analyze it for insights. This is where data analysis tools come in handy, allowing you to explore your data quickly and easily. Big data analytics tools aid you in uncovering patterns or correlations between different variables that the human eye may otherwise miss. Here are a few tools to consider exploring:

  • Hadoop: Hadoop is an open-source software framework that enables the analysis of big data sets in distributed computing systems. It uses a MapReduce algorithm to break up large datasets into smaller pieces for easier processing and storage.
  • Apache Spark: Apache Spark is an open-source framework that helps with real-time analytics on streaming data. It's also ideal for both batch and interactive analytics.
  • NoSQL Databases: NoSQL databases are becoming increasingly popular for their ability to store data in a non-relational format. This makes them ideal for storing large amounts of unstructured or semi-structured data.

Data analysis uses various techniques, including predictive analytics, anomaly detection, natural language processing (NLP), and machine learning algorithms. Depending on the goals and objectives of your data analysis project, you can choose the best techniques to use.

Step 4: Process Your Data

Data processing is the step where you take the information gathered from data analysis and use it to gain insights or make decisions. This could include developing a visualization of your data, creating a report, making predictions, or taking action based on the results of your analysis.

Once you have completed all four steps in the big data process, you can reap the rewards of leveraging your data for insights. Big data analytics is a powerful tool that can help organizations make better decisions, improve customer experience, and increase revenue. 

When used correctly and with the right team to execute it, big data can revolutionize any organization's operations. Collecting and using big data requires technical and market knowledge to ensure it is done correctly and efficiently. 

Organizations should ensure they have the right tools, teams, and processes to maximize their use of big data analytics. Part of your team includes working with seasoned developers who can help you collect, store, analyze, and process data. 

Developers can also help you with the technical challenges of big data analytics, such as scalability, latency, security, and more. To start sourcing the right team to assist in your big data initiatives, reach out to Aloa for valuable insights into software outsourcing consultancy, linking you with the right team of developers to work on your big data project and providing the necessary resources to help you succeed.

Step 5: Implement and Monitor

After processing and analyzing your data, you must take the insights and implement solutions. This could mean changing existing processes, creating new strategies and initiatives based on your data, or launching products and services based on what your data revealed.

Once you've implemented these new initiatives, it's important to constantly monitor them. Keeping a watchful eye will allow you to identify any areas of improvement and make changes whenever necessary. 

This could mean tracking customer feedback, sales trends, or employee performance metrics. Big data analytics tools can monitor whatever is relevant to your business objectives.

Big Data Implementation Across Industries

No matter the industry, big data continues to propel how businesses operate. Many companies use big data to gain a competitive advantage and drive innovation. This could include using customer data to improve products and services, utilizing machine learning algorithms to automate processes, or tracking employee performance metrics for efficiency.

Whether you're a retailer or healthcare organization, big data can help identify trends, develop insights, and recommend solutions to complex problems. Let's take a look into the specific industries and how big data is used to progress their respective businesses:


big data use cases by industry

Software Development

Big data in software development can help developers gain insights into user behavior and trends to create better applications. Companies use big data analytics for market segmentation, customer experience optimization, error detection and analysis, metadata tracking, and more. These insights can help improve the development process and make apps run more smoothly and efficiently.


In healthcare, big data is used to gain insights into patient health, improve outcomes, and reduce healthcare costs. Healthcare providers use predictive analytics to identify high-risk patients and anticipate medical needs before they become problematic. Additionally, organizations use big data to track the effectiveness of treatments, monitor drug safety, and access real-time patient vitals data.


The fintech industry uses big data to gain insights into consumer purchasing behavior and help identify fraud. Fintech companies use big data to analyze customer spending patterns, detect fraudulent activity, build personalized experiences, and offer customized products that meet the customers' needs.


E-commerce companies use big data to gain customer insights and create a better user experience. Big data helps e-commerce businesses understand customer buying habits, track website performance, detect fraud, optimize product recommendations, and drive sales.


Within the manufacturing industry, big data is used to predict potential machine failures, reduce waste in the production process, and optimize supply chains. Manufacturing companies use big data to track machinery performance, identify potential problems before they arise, and develop solutions to increase efficiency and productivity.

Big Data Best Practices

When using big data to drive insights, it's important to be mindful of best practices for data governance. This involves setting up procedures and guidelines for collecting, storing, analyzing, and protecting data. Following these best practices helps ensure that the data is accurate and secure.

6 best practices when using big data

Define Clear Business Objectives

As mentioned earlier in the prerequisites section, it is important to first understand your company's goals and objectives when beginning the data analysis process. Defining these goals ahead of time provides clarity for team members and helps keep everyone on the same page.

Collect and store relevant data only.

Getting caught up in collecting too much data can lead to analysis paralysis. Companies should focus on collecting the most relevant and actionable data that will help inform their goals. Additionally, ensure that the collected data is stored securely and only accessed by authorized personnel.

Ensure Data Quality

Data accuracy is essential for drawing meaningful conclusions and making informed decisions. To ensure data quality, companies should regularly audit their data collections to identify inconsistencies or outliers. This will help maintain your data's integrity and ensure you're getting accurate insights from it.

Use Appropriate Tools and Technologies

Data analysis involves a lot of processes and calculations that can quickly become overwhelming. Companies should use appropriate data analytics tools and technologies suited to their needs to simplify the process. This will help streamline the process and promptly ensure you get accurate results.

Establish Data Security and Privacy Policies

Data privacy regulations vary from country to country, so staying informed of applicable laws and establishing your organization's data security and privacy policies is important. This will help protect customer and business data and ensure that you remain compliant with all applicable laws.

Leverage Machine Learning and Artificial Intelligence

Big data can drive machine learning and artificial intelligence algorithms that can automate processes, identify patterns, and develop real-time insights. Utilizing these technologies can help companies unlock the potential of their data and gain a competitive advantage.

Focus on Data Visualization

Data visualization is an important part of data analysis as it helps make data easier to understand and digest. Companies should use visuals such as charts, graphs, and maps to help present their findings clearly and concisely.

Big data has the potential to revolutionize businesses of all sizes and industries. Still, it is important to clearly understand the process before diving in. By following the best practices outlined above, companies. 

Challenges in Big Data

As with any modern technology, big data has certain challenges that must be addressed to get the most out of it. Here are some challenges to look out for when using big data:

3 challenges in leveraging big data

Data Growth

Managing large datasets can be challenging and expensive for companies. Data continues to grow exponentially, and businesses must have the capacity and resources to handle the influx of data.

In line with that, having access to vast amounts of data is not always good if it's not managed correctly. Companies must establish data governance in the processes and procedures for collecting, storing, analyzing, and protecting data to maintain its accuracy and integrity. Moreover, for more efficient and cost-effective data management, companies can explore Airtable alternatives to ensure the optimal organization of large datasets.

Data Security

Ensuring the security of sensitive information and protecting it from breaches. Companies must establish data security protocols to secure customer and business data. Additionally, organizations must know applicable laws and regulations when collecting and storing data.

Data Integration

Combining and reconciling data from different sources can be an operational challenge. Organizations must have the appropriate systems and tools to ensure that data from various sources are correctly integrated.

The Future of Big Data

As the volume of data being produced grows, more businesses are shifting from traditional data storage and analysis methods to cloud solutions and big data techniques. The future of big data looks promising, with the potential to revolutionize how businesses operate and make decisions. Alongside that, developing advanced analytics and data mining algorithms will continue to expand the possibilities.

Key Takeaway

Big data capabilities of predictive analytics have a wide range of applications that have transformed many industries over the past decade. As technology develops, we can expect even more advancements in this field and new possibilities for companies to leverage big data.

To make the most out of big data, companies must clearly understand their objectives to define relevant datasets for analysis. Additionally, it is essential to have safeguards in place for protecting customer and business data and staying compliant with applicable laws.

For more insight into how big data can impact your business or startup, reach out to [email protected], where our team of experts can guide you through the process of establishing, using, and making the most out of your initiative to incorporate big data into your operations.

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