Introduction to Big Data Analytics
Have you ever wondered, why Netflix recommends you movies or shows which are your favorite ones?
This is magic of Big Data and Big data analysis. Netflix have approximately 232.5 million paid subscribers who are daily visiting Netflix and spending some time. When any user logged into Netflix, his/her data capturing starts at the background.
- Which movie you are watching?
- What are you searching?
- Where are you spending most of your time?
- Which category of movies or shows you are watching?
Such numerous data points are captured for millions of users during each second. Such a huge amount of data which cannot be stored, processed, and analyzed at your local system or by using traditional tools is called as a Big Data.
What is Big Data?
Massive amount of data which cannot be stored, processed, and analyzed using traditional tools is known as Big Data.
This large data is then captured, processed, and analysed to gather some useful outcome or information or trend.
For Example, if you are watching Sci-fi movies on Netflix, then application will suggest or recommend you more and more Sci-fi movies. So that you can spend maximum time on Netflix, and it will be profitable to Netflix.
What is Big Data analytics?
Big data analytics is a process to generate meaningful, useful outcomes from big data market trends, customer choice, unknown corelations and hidden patterns.
- The goal of big data analytics is to find out valuable outcomes from the extensive amounts of data that are generated by various sources such as social media, mobile devices, sensors, and other technologies.
- This involves the use of advanced tools and techniques such as machine learning, data mining, natural language processing, and predictive analytics.
- Big data analytics can help organizations to gain a deeper understanding of customer behaviour, optimize business processes, improve decision-making, and drive innovation.
- It is widely used in industries such as healthcare, finance, retail, and telecommunications, among others.
The 5Vs of Big Data:
This large amount of data is having various characteristics which are used to categorized it. There are 5V’s which are associated with characteristics of big data.
- Refers to the enormous size of the data sets involved.
- Big data typically involves terabytes or petabytes of data that cannot be processed by traditional data processing methods.
- Refers to the speed at which data is generated, collected, and processed.
- Big data is typically generated and collected in real-time or near real-time, and needs to be processed quickly to gain actionable insights.
- Refers to the diversity of data types and sources, which can include structured, unstructured, and semi-structured data.
- Big data can come from various sources such as social media, emails, sensors, and other devices, and can be in different formats such as text, images, and videos.
- Structured data: Excel files
- Semi-structured: Email
- Unstructured: Photos, scans etc
- Refers to the quality and reliability of the data, which can be affected by errors, bias, and other factors.
- Big data can contain incomplete, inconsistent, or inaccurate data, which can affect the accuracy of the insights gained from it.
- Refers to the potential insights and business value that can be derived from analysing Big Data.
- The goal of big data analytics is to find out valuable outcomes from the extensive amounts of data that are generated by various sources such as social media, mobile devices, sensors, and other technologies. This provides competitive advantage over the others.
Techniques of Big Data Analytics:
Big data Analytics consist of number of techniques and tools which are used to dig out some meaningful insights and value from large and complex data.
Here are some most used tools and techniques in Big Data Analytics:
1. Machine Learning:
- Machine learning involves the use of algorithms and statistical models to analyze and learn from data.
- It can be used for a variety of tasks such as predictive modelling, natural language processing, and image recognition.
2. Predictive analytics:
- In Predictive analytics, statistical models and machine learning algorithms are used to make predictions about future events based on historical data.
- It is used for tasks such as forecasting, risk assessment, and fraud detection.
3. Data Mining:
- Data mining also involves the use of statistical algorithms to extract patterns and insights from data.
- It is used to uncover hidden relationships and identify trends in large and complex data sets.
4. Natural Language Processing (NLP):
- NLP uses algorithms and models to understand and analyze human language.
- It is used in applications such as sentiment analysis, chatbots, and speech recognition.
5. Data Visualization:
- Data visualization uses graphical representations to present data in a way that is easily understandable and interpretable.
- It is used to help users identify patterns and trends in data and make informed decisions based on the insights gained.
6. Cloud Computing:
- Cloud computing involves the use of remote servers to store, manage, and process data.
- It is used to handle the large volumes of data generated by big data analytics and to provide scalable and flexible computing resources
7. Distributed Computing:
- Distributed computing uses multiple computers or servers to process data in parallel.
- It is used to handle the large volumes of data involved in big data analytics and to speed up processing times.
Such number of techniques and tools are used in Big data analytics but the choice of technique depends on the specific goals or targets of the analysis and the characteristics (5Vs) of the data being analyzed.
Applications of Big data Analytics:
Now a days, Industry 4.0 have really started evolving through various key technologies and Big Data Analytics is one the major contributor.
Big data analytics is used in many industries and applications which has helped them make better decisions, improve operational efficiency, and gain a foot ahead in competition.
- Healthcare providers are using big data analytics to improve patient care and outcomes.
- For example, many big hospitals use big data analytics to identify patients who are at risk of re-admission and to develop personalized care plans to improve their outcomes.
- Healthify Me analyses huge data of users and suggests diet plan, exercises to keep health fitness.
- Retail companies are using big data analytics to gain insights into customer behaviour and preferences.
- For example, Amazon, Flipkart, Alibaba uses big data analytics to recommend products to customers based on their browsing and purchasing history, and to optimize its supply chain operations.
3. Financial Services:
- Financial services companies are using big data analytics to identify fraudulent transactions and to assess credit risk.
- For example, American Express uses big data analytics to detect fraud in real-time and to provide personalized recommendations to its customers.
4. Manufacturing Industry:
- Manufacturers are using big data analytics to optimize their production processes and reduce costs.
- For example, General Electric, Bosch uses big data analytics to monitor the performance of its industrial equipment and to predict when maintenance is needed, reducing downtime and improving efficiency.
- Transportation companies are using big data analytics to improve logistics and route optimization.
- For example, Swiggy, Zomato, Uber uses big data analytics to optimize its delivery routes or travel routes to reduce fuel consumption, saving millions of dollars in operating costs.
6. Social Media:
- Social media companies are using big data analytics to improve user engagement and to personalize content.
- For example, Facebook, Instagram etc. uses big data analytics to analyses user behavior and preferences, and to optimize its newsfeed algorithm to show users the most relevant content.
Big data Analytics is transforming the way the business operates competes. It is providing numerous benefits such as Better decision making, Improved efficiency, Enhanced customer experience, competitive advantage and Innovations.
Over few years from now, Big data is going to be the key technology and its early adoption will be the only way to be in the competition.