Big Data is an all-encompassing term that refers to the accumulation of data in large pools employed in today’s global corporate world. It is a collection of organised, semi-structured, and unstructured data gathered by businesses.

Big data necessitates data storage and processing solutions. As a result, these systems are an essential component of many data management architectures. In addition, they’re frequently used in conjunction with tools that help with big data analytics and application platforms.

Back in 2001, the renowned analyst Doug Laney identified the three fundamental aspects of big data, famously known as the “3 Vs”: 

• Volume: This represents the sheer quantity of data, typically measured in petabytes, terabytes, or exabytes, collected and stored over time. 
• Velocity: This refers to the speed at which data is generated, processed, and made available for analysis. 
• Variety: Big data encompasses a diverse range of data types, from structured to unstructured, adding complexity to its management. 

Currently, the scope of big data has expanded to include two additional dimensions: “value” and “integrity,” further enriching its significance in the business world. 

The Importance of Big Data

Companies depend on big data to improve customer service, marketing, sales, team management, and many other routine operations during their analysis. They rely on big data to innovate pioneering products and solutions. Big data is the key to making informed and data-driven decisions that can deliver tangible results. The brands aim to boost profits and ROI with big data while establishing themselves as a market leader in their respective segments.

Thus, big data gives companies a competitive advantage over competitors who don’t use big data yet.

Some examples of how big data helps companies are:  

• Assisting companies to refine their advertising and marketing strategies/campaigns.
• Improve their consumer engagement and lead conversion rates. 
• It helps to study the changing behaviour of corporate buyers, customers and the market.
• Become more responsive to the market and customers needs.

Even medical researchers use big data in identifying risk factors and symptoms of diseases. Doctors also majorly depend on big data to improve disease diagnostics and treatment frameworks. They also rely on data from social media sites, surveys, digital health records and other sources from government agencies. 

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The Primary Sources of Big Data:

A significant part of big data is generated from three primary resources: 

• Machine data
• Social data, and
• Transactional data. 

In addition to this, companies also generate data internally through direct customer engagement. This data is usually stored in the company’s firewall. It is then imported externally into the management and analytics system.

Another critical factor to consider about Big data sources is whether it is structured or unstructured. Unstructured data doesn’t have any predefined model of storage and management. Therefore, it requires far more resources to extract meaning out of unstructured data and make it business-ready.

Now, we’ll take a look at the three primary sources of big data:

1. Machine Data 

Machine data is automatically generated, either as a response to a specific event or a fixed schedule. It means all the information is developed from multiple sources such as smart sensors, SIEM logs, medical devices and wearables, road cameras, IoT devices, satellites, desktops, mobile phones, industrial machinery, etc. These sources enable companies to track consumer behaviour. Data extracted from machine sources grow exponentially along with the changing external environment of the market. The sensors which record this type of data include:

In a more broad context, machine data also encompasses information churned by servers, user applications, websites, cloud programs, and so on.

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2. Social Data 

It is derived from social media platforms through tweets, retweets, likes, video uploads, and comments shared on Facebook, Instagram, Twitter, YouTube, Linked In etc. The extensive data generated through social media platforms and online channels offer qualitative and quantitative insights on each crucial facet of brand-customer interaction.

Social media data spreads like wildfire and reaches an extensive audience base. It gauges important insights regarding customer behaviour, their sentiment regarding products and services. This is why brands capitalising on social media channels can build a strong connection with their online demographic. Businesses can harness this data to understand their target market and customer base. This inevitably enhances their decision-making process. 

3. Transactional Data 

As the name suggests, transactional data is information gathered via online and offline transactions during different points of sale. The data includes vital details like transaction time, location, products purchased, product prices, payment methods, discounts/coupons used, and other relevant quantifiable information related to transactions. 

The sources of transactional data include:

• Payment orders
• Invoices
• Storage records and
• E-receipts

Transactional data is a key source of business intelligence. The unique characteristic of transactional data is its time print. Since all transactional data include a time print, it is time-sensitive and highly volatile. In plain words, transactional data will lose its credibility and importance if not used in due time. Thus, companies using transactional data promptly can gain the upper hand in the market. 

However, transactional data demand a separate set of experts to process, analyse, and interpret, manage data. Moreover, such type of data is the most challenging to interpret for most businesses.

Categories of Sources of Big Data  

In my experience, the sources of big data are incredibly diverse, contributing to its sheer volume, rapid velocity, and wide variety. Firstly, the immense trove of data generated by social media platforms through user interactions, posts, and comments is a significant contributor. Secondly, the Internet of Things (IoT) gathers data from a multitude of connected devices, including sensors and wearables. 

Business transactions and e-commerce platforms play a crucial role in providing valuable insights into customer behavior and sales trends. Data also originates from our ever-present mobile devices, offering information such as location data and app usage statistics. Furthermore, the healthcare sector generates substantial data from electronic health records and medical devices. 

Beyond these, data sources extend to scientific research, weather monitoring, and even satellite imagery. Understanding these diverse categories of data sources is paramount in unlocking the full potential of big data for gaining valuable insights and making informed decisions. 

How Does Big Data Analytics Work?

Companies need to work around analytics applications, partner with data scientists and engage with other data analysts to extract relevant and valid insights from big data. In addition, they must have an enhanced understanding of all available data. Finally, the analytics team also needs to clarify what they want to extract from the data. 

The team needs to take care of :

• Cleansing,
• Profiling,
• Transformation, 
• Validation of data sets.

These are some of the most important initial steps taken in data analysis.

Once all the big data has been prepared and gathered for interpretation, a combination of advanced data science and analytics disciplines is applied through different machine learning tools. This will help to generate results that lead to businesses growth and development.

Some additional steps ideal to the analysis of big data are:

• Deep learning offshoot of data
• Data mining
• Streaming analytics
• Predictive modelling
• Statistical analysis
• Text mining

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moreover, there are different branches of analytics used in extracting insights from big data. These models of analytics are as follows:

1. Marketing Analytics

 It gives valuable information for improving a brand’s marketing campaigns, promotional offers and other consumer outreach. 

2. Comparative Analysis

 It looks into customer behaviour metrics and enables real-time engagement with customers so that enterprises can compare brands, products, services and business performance with their competitors. This analysis requires the following type of data:

• Demographic data
• Transactional data
• Web behaviour data
• Consumer text data from surveys, feedback forms etc.

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3. Sentiment Analysis

 It focuses on customer feedback on a specific product or service, customer satisfaction, and pointers to improve in these areas.

4. Social Media Analysis

. This analysis is about people’s responses over social media platforms regarding their choices and preferences over a particular service or product. This analysis helps businesses identify possible problems and target the correct audiences for all their marketing campaigns.

What Should Businesses Do to Extract Valuable Insights from Big Data?

Real business value is extracted from the capacity of big data to generate actionable insights. Companies should aim to develop a cohesive, comprehensive, and sustainable strategy for analysis. They should also focus on differentiating themselves in the industry through decisions that support employees and business development. 

Big data analysis is a resource and time-intensive task. Despite having the most advanced technologies, companies often struggle with big data analysis due to skilled and qualified big data experts. And hence need to hire specialists who can provide them with growth-oriented insights. This is where you can make a difference. By gaining competent big data skills and knowledge, you can become a valuable asset for any organisation.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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Conclusion

In my experience, big data serves as the foundation of modern industry operations. The analysis of big data enables companies to develop growth strategies that are relevant for both the present and the future. It plays a crucial role in examining market trends and understanding customer requirements. 

However, the core dynamics of big data have evolved beyond merely engaging with data. The broader perspective now involves identifying credible methods to boost data production in the upcoming years, ensuring the acquisition of more extensive and dependable insights.