Big Data Analytics: Unveiling Real-Life Applications, Benefits, and Challenges

Introduction


Did you know that the average person generates a staggering 1.7 MB of data per second?

In today’s digital age, data is everywhere and its volume is growing at an unprecedented rate. This surge has given rise to big data analytics, empowering businesses to extract valuable insights from expansive information repositories and gain a competitive edge.

But what exactly is Big Data, and how can its real-life applications reshape industries? In this article, we’ll delve into the various facets of big data analytics, exploring its applications, benefits, and the challenges it poses.

What is Big Data?

Big data refers to the large and complex datasets that defy conventional management. They cannot be processed, or analyzed using traditional methods and tools. Originating from diverse sources like social media, internet transactions, and audiovisual content, big data is characterized by its volume, velocity, and variety — commonly known as the three Vs.

  1. Volume: Enormous amounts of data generated daily — an estimated 328.77 million terabytes per day as of April 3, 2023 according to Exploding Topics.

  2. Velocity: The speed at which data is being generated has also increased significantly. Real-time systems and IoT devices (non-standard computing hardware such as sensors or appliances) contribute to a constant stream of data that demands immediate analysis.

  3. Variety: Big data comes in various forms such as structured (e.g., databases), unstructured (e.g., social media posts), and semi-structured (e.g., emails). This diversity in the types of data poses a challenge for traditional processing systems.

    Big Data Analytics_Volume, Velocity, Variety article by SCD Company

Now that we have a basic understanding of big data, let’s explore its real-life applications.

Big Data for Education and EdTech

The education sector has witnessed a remarkable increase in the use of big data analytics in recent years. Educational institutions now collect vast amounts of student information, including attendance records, grades, test scores, and, in some cases, even social media activity. This information is then used to identify patterns and trends, enhancing teaching methods and student performance.

But how else can Big Data be used in Real-Life Setting to help advance the Education Industry?

  1. Forecast students’ academic progress: Predictive analytics can forecast students’ academic success by analyzing past achievements and behavioral patterns, allowing educators to intervene early if a student is falling behind or needs extra support.
  2. Personalized course recommendations: Online personalized course recommendations are facilitated, breaking free from educational institution attendance constraints in the post-COVID era. Big Data can analyze students’ preferences, offering custom recommendations to make learning more personalized and enjoyable.

    Have you ever Googled a topic you’re interested in, visited a skill-building free course to improve your knowledge for work? As you can see, this solution can be beneficial for online educational providers (e.g. edX, Coursera, and others) to help adults advance in their career. And don’t be surprised if courses tailored to your needs start to get recommended one after another. That’s the power of data analysis and recommendation systems.
  3. Career Prediction: Big Data analytics leverages students’ records, grades, interests, and courses to suggest suitable career paths based on acquired data.

Big Data for Financial/Banking Industries

1. Duplicate Removal

Big data plays a crucial role in data management. Data duplication is a significant issue for leading financial or banking organizations dealing with vast datasets. Duplicate records not only take up unnecessary storage space but can also lead to inaccurate analyses, impacting major decisions.

Big data analytics tools use complex algorithms to identify and remove duplicate records, ensuring that businesses have clean and accurate data at all times. This not only improves the quality of decision-making but also saves time and resources spent on manual de-duplication processes which is mostly impossible if you’re dealing with tens of thousands or millions of records at once.

For example, at SCD we were presented with a case when one of the largest European Banks had accumulated millions of customer records for years, some of which with different address formats, typos, and errors. Since the information was private and could not be entrusted to a third-party vendor, our engineers were challenged to create a unique algorithm to provide the bank structured data with clear, structured information without errors and duplicates.

2. Demand forecasting

The financial market does not fall behind in demand forecasting. By carefully analyzing product demand through previous sales data, traditional trends, buyer personas, big data can help accurately forecast which new products will be in demand by which customer groups. Lead generation processes become easier, enabling personalized offers to the right audience based on available statistical data.

3. Fraud Detection

Big data can be used to analyze the financial transactions of customers, determining the basic parameters of their individual behavior and detecting anomalies or atypical behavior. For real-time fraud detection software, you can check out the list by Seon.

Big Data Application in Healthcare Industry

1. Electronic Health Records (EHRs) have replaced traditional paper medical records. EHRs are one of the most widely used digital data formats in the medical industry, recording digital details of every patient: demographics, medical history, allergies, laboratory test results. Thanks to accurate use of this information, big data is used in EHRs to help avoid unnecessary paperwork or manual work. EHR systems are often used to trigger reminders to patients when it’s time for them to get a new lab test or track prescriptions, etc.

2. Disease Diagnosis- Pattern Recognition, the data analysis process using machine learning and deep learning techniques to recognize patterns in data, is commonly applied to diagnose diseases and predict risks in the healthcare industry. By recognizing patterns in various medical datasets, professionals can make more informed decisions, ultimately improving patient care.

3. Medical Insurance Analysis - Collected data from patients, such as healthcare or previous test records, can be tracked based on medical insurance data to predict the possibility of a minor or major illness for a patient. This predictive analysis not only informs patients about potential health risks but also enables medical professionals to diagnose and intervene well in advance. For instance, machine learning algorithms can analyze historical patient data to identify subtle patterns indicating early signs of certain conditions, facilitating proactive medical interventions.

Big Data in Media/Entertainment

1. Social Media Monitoring using Sentiment Analysis

With the rise of social media, businesses or individuals can now tap into a vast pool of data that reflects customers’ opinions and sentiments. Sentiment analysis, also known as opinion mining, uses natural language processing (NLP) to analyze social media posts, updates, comments, reviews, and even emojis to determine people’s attitudes towards a particular product, service, or individuals (data from products and advertisements or bloggers and influencers).

This data is incredibly valuable for businesses and individuals alike as it provides insights into how customers perceive their brand. It can be used to improve marketing strategies, address negative feedback, and enhance overall customer experience. If you have no time to manually go over every reaction or comment to understand your audience’s reaction to your brand, consider tapping into sentiment analysis.

For example, Instasentiments (tool developed by SCD) has helped Instagram influencers by providing users with information on the average tonality of the comments on their Instagram posts. It reveals the most positive comments to encourage user interaction and the most negative comments to help the account owner deal with them.

2. Personalized Recommendations

Music industry leaders such as Spotify, have been using Data analysis to track listening patterns, preferences, demographics of their users according to 7wdata to provide personalized song recommendations for their users making the experience truly unique for each individual.

Big Data for Travel/Hospitality Industry

Similar to the Media and Entertainment digital transformation, the use of Big Data is being widely incorporated in the Travel Industry to help make better business decisions based on data.

1. Reputation Management: Every minute customers leave feedback on hotels, travel locations, malls, cafes, and more on travel sites, booking pages, search engines, or dedicated review platforms, sharing their opinions with the world. Analyzing all this data one-by-one would be close to impossible. Big Data can be used to analyze the overall feedback from travelers, whether their experiences have been negative or positive, and use this data to make appropriate recommendations to new travelers. This is where recommendation systems come in handy.

2. Strategic Marketing: Since the tourism industry marketing possesses challenges due to its vast variety of users and target audience, big data can be used to correctly analyze current market trends, travel, and location trends to understand which areas people are particularly interested in. This helps target them more effectively, preparing personalized offers based on a user’s behavior.

The list doesn’t end here. Big Data is becoming more widespread, and industries are catching up with this revolution:

3. Manufacturing- Predictive maintenance and supply chain optimization*

4. Technology and IT- Cybersecurity, system optimization or customer experience analysis*

5. Agriculture- precision farming and crop monitoring*

6. Real Estate- predicting property trends*

7. Transportation- real-time traffic analysis or route planning*

Challenges related to Big Data

Despite its advantages, big data presents challenges like data availability, security concerns, and high infrastructure costs. However, its diverse applications across multiple industries make it an indispensable tool in today’s digital landscape and overpower the risks.

Summary

Big data analytics has become an essential tool for businesses in today’s digital age. Its applications are diverse, from education improvement to sentiment analysis and data management. By leveraging big data analytics, all businesses can gain valuable insights that enhance decision-making processes, improve customer satisfaction, and ultimately drive long-term business growth. As technology continues to advance, we can expect more innovative use-cases for big data in various industries. So whether you’re a business owner at a startup, fast-growing medium business, an enterprise or tech engineering manager just looking to stay ahead of the competition and reap the benefits of technology — it’s time to embrace big data analytics!

Nov 28, 2023