Exploring Different Types of Big Data Analytics

Big Data Analytics has revolutionized the processes that companies and organizations apply to evaluate their millions of bytes of data in the last few decades. Using structured, semi-structured, and unstructured data, the development of efficient operations, access to relevant strategic information, and higher levels of customer satisfaction can be realized. Big Data analytics is widespread across multiple sectors including manufacturing, retail, insurance, healthcare, education, AI and ML.

In this article, we will learn what is Big Data Analytics, its different types, benefits, challenges, and applications in real life. We will also see the framework for big data to help understand how big data analytics processes work and which metrics use cases they support when it comes to enterprise engagement in making business decisions.

What is Big Data Analytics?

Big data analytics refers to the process of collecting, reviewing, and analyzing a large amount of information and data to uncover beneficial information and trends that can be used by companies or businesses to better their markets. Accessible and reliable such data makes it possible for organizations to be opportunistic in formulating strategies that fit the dynamic competitive environment. This process involves using complex data analytics strategies and enables organizations to turn the tide of huge volumes of data created through social media, e-commerce, IoT devices, mobile and PC applications, and so forth.

Big data is not restricted in terms of its form but the varying types of data can however be split into two basic types, structured and unstructured. Some technologies that help organizations align structured and unstructured data from diverse sections are the Business Intelligence (BI) tools and systems. To fulfill their work objective of understanding how the business or organization is performing, the end users – employees most of the time, input questions in the BI tools provided.

Important Features of Big Data

The 3 Vs are mainly used to distinguish big data with some variations expanding it to the 5 Vs:

• Volume: The data in this case ranges from terabytes to zettabytes.
• Velocity: The rate of creating, acquiring, and analyzing the information.
• Variety: The range of data formats, including unstructured (text, photos, and videos), semi-structured (XML, JSON), and structured (databases).
• Veracity: Data correctness and quality.
• Value: How beneficial the conclusions drawn from the data are.

Big data has been around for a long time, and people often need clarification about why big data analytics is important today. Big Data Analytics is now essential for governments, corporations, and organizations worldwide. Here’s a thorough examination of the reasons big data analytics is so important:

1. Improved Decision-Making

Big Data Analytics is the procedural logic that enables organizations to offer their clientele potential services by processing a lot of data in real-time. With these insights, decision-makers can formulate strategies based on real data rather than gut or speculation. For instance, retailers can decode the purchase patterns of their clients to know how best to stock their warehouses.

2. Improves Operational Efficiency

Big Data Analytics is a technique that uncovers process enhancement possibilities such as identifying eliminable steps and activities and ineffective practices found within the operational model of the organization. In manufacturing, for instance, predictive maintenance employs analytics to determine when equipment is likely to fail, thereby minimizing both downtime and maintenance expenses.

3. Enhances Customer Satisfaction

Organizations can improve analytics to better understand the customer journey and leave out some of their preferences and pain points. For instance, with such data crises management must connect with its audience in a more meaningful way by combining data from various points like social media, online activities, and past purchases.

4. Stronger risk control

To overcome this constraint, companies can leverage the potential of big data and analytics to analyze a greater quantity of information, increasing the number of potential risks and their effectiveness evaluation, allowing for easier identification and monitoring of all potential threats, and reporting insights that enhance control and mitigation measures.

5. Improving customer experiences

The combination and analysis of structured data sources with unstructured data give more insight about a customer and help in creating recommendations, personalized dashboards, etc.

Also Read: Top Big Data Analytics Tools

How Does the Big Data Analytics Work?

Big Data Analytics comprises several stages and involves a variety of tools, technologies, and methodologies to enable the processing and analysis of large datasets by the organization. Here’s how this process works:

1. Data Collection

The first stage involves collecting data through numerous channels such as sensors, social media, transaction databases, websites, and IoT devices. The data may be structured, semi-structured, or unstructured. The focus here is on obtaining all pertinent data as it ensures that the analysis presents the most possible comprehensiveness of any one problem or situation.

2. Data Storage and Management

When the data has been collected, it is stored in infinitely scalable systems capable of handling the immense volumes created. Typically, this includes distributed storage solutions such as Hadoop Distributed File System (HDFS), various cloud storage options available, and data warehousing solutions. All these systems make very sure that the data is highly organized and ready for analysis at all times while managing access and ensuring security.

3. Data Cleaning and Preparation

Earlier than analysis, uncleaned data typically has error duplicates and other inconsistencies that might influence the outcome of the analysis. Therefore, the cleaning process would get rid of these anomalies by standardizing formats, filling in missing values, etc. to ensure data consistency.

• Data Integration

Different sources of data come together and are unified to bring a single dataset online. In the process, redundancy is removed, and thus, the information becomes a complete whole.

• Data Processing

Before analysis, the data is preprocessed with computational frameworks including Apache Spark, Hadoop MapReduce, or SQL-based systems that further place that data into subsets or aggregates as required by the analytics task at hand.

• Data Analysis

This includes applying advanced analytical techniques such as machine learning, statistical modeling, and predictive analytics to the data using tools such as Python, R, Tableau, and SAS to enable data scientists to explore patterns, trends, and correlations. This is where insights are generated, predictions are made, and data-driven decisions take shape.

• Visualization

Visualizations such as charts, graphs, dashboards, and reports are used to display the analyzed data. Thanks to tools like Excel, Tableau, and Power BI, stakeholders can comprehend complicated analytical conclusions with ease. By using visualization, you may convey complicated ideas in a clear and approachable manner.

• Actionable Insights and Decision-Making

The last phase involves converting ideas into workable plans. Businesses use Big Data Analytics results to identify hazards, enhance consumer experiences, optimize operations, and plan for future advancements.

Different Types of Big Data Analytics

There are mainly four main types of data analysis techniques that work in big data and support businesses make informed decisions:

Descriptive Analytics

The most common form of data analytics technique is descriptive analytics. It deals with analyzing and interpreting the past and the present in order to look for patterns, trends, or information. It does not attempt to discern the likely outcomes in the future or recommend a course of action. Instead, it describes what transpired before or what is being done at that moment. It effectively encapsulates a lot of information into an easily comprehensible format such as charts, dashboards, and reports.

Example for Retail or E-commerce:

Descriptive Analytics is often used in the retail sector for performance assessment, particularly sales performance analysis. A chain of supermarkets could apply descriptive analytics to analyze the sales pattern and their variation over seasons of the last year.

Scenario: A retail chain analyzes its sales data from multiple locations measuring total monthly sales, the most selling categories, and best sales periods.

Insights: The findings might show that frozen foods sales increased by 25% in winter, while summer turned out to be the best in terms of sales of fresh produce. Most of the sales took place on Saturdays and Sundays, which together contributed 75% of the total weekly sales, the retailer sold a certain product for seasonal purposes which exceeded its sales predictions.

This enables the retailer to appreciate the buying patterns, better manage the volume of stock needed, and make appropriate arrangements for advertising.

Diagnostics Analytics

Diagnostic analytics goes one step further than descriptive analytics in that it says what the root causes of observed trends or patterns in data are. While descriptive analytics answers what happened, diagnostic analytics answers why it happened. Statistical methods, correlations, and drill-down techniques help throw such relationships up, note anomalies, and understand driving factors much better.

Example of Marketing:

In the realm of digital marketing, diagnostic analytics is used to comprehend the most abrupt shifts in website performance.

Scenario: Suppose a corporate website has seen a decline in traffic by 30% over the last month.

Analysis:

• As usual, the company resorts to its diagnostic analytics tool, such as Google Analytics, to take a look at the data.
• The team identifies that this is mostly organic search traffic (Drill-down Analysis).
• Further analysis reveals that the decrease aligns with a recent change, for the worse, in Google’s search algorithm (Correlation Study).

Insights: With this knowledge, the business may alter its content and rethink its SEO approach to conform to the new algorithm, which eventually helps it regain traffic.

Predictive Analytics

Predictive analytics can focus on forecasting future outcomes by analyzing historical patterns and relationships in the data, as well as deriving probabilities within the scope of available data from data mining practices and machine learning algorithms. It answers the question: What is likely to happen? Predictive analytics identify trends, relationships, and the likelihood of different outcomes to enable organizations to act upon future changes with calculated and appropriate actions.

Example of E-commerce:

In e-commerce most notably applied in predicting customer behavior as well as marketing optimization strategies.

Scenario: An online retailer wants to predict which customers are likely to purchase during a seasonal sale.

Analysis:

• Customer purchase history, patterns of browsing, and demographics are utilized in building a model that will predict the likelihood of these people making purchases.
• It finds that customers who looked at winter apparel within the last month and were also sent promotions via email have a 70% higher likelihood of purchasing during the sale.
• Predictive analytics also recommends the best time to send promos to increase interaction.

Insights: The retailer greatly increases sales and customer happiness by using these forecasts to target high-value consumers with tailored advertisements and promotions.

Prescriptive Analytics

Prescriptive analytics goes beyond predicting future outcomes to prescribe optimal decisions. It answers the question, What should we do? by considering insights from data, applying advanced analytics techniques, using optimization algorithms, and in some cases, AI models. Prescriptive analytics not only brings out potential future scenarios but also prescribes the actions to take to realize the positive outcomes or mitigate the risks.

Example of Logistics:

This is broadly applied to supply chain management in practice for efficiency improvements and cost reductions.

Scenario: A logistics company faces rising transportation costs as well as delays in the delivery of goods.

Analysis:

• Utilizing prescriptive analytics, this company will take the evaluation of routes, volumes of shipments, and delivery time slots. The system will identify the most cost-effective modes of transportation that guarantee on-time deliveries.
• For example, some of the routes can be recommended, per this analysis, to be delivered through a combination of rail and road transport; it reduces costs by 15%.
• To prevent stockouts or overstocking, it also suggests modifying inventory levels at regional warehouses.

Insights: By putting the suggestions into practice, the business saves a lot of money and improves customer satisfaction by making deliveries on time.

Uses and Examples of Big Data Analytics

1. Improving Health Care Services


Big Data Analytics has become very fundamental in enhancing healthcare services as well as the management of operations. It analyzes huge amounts of data to reveal patterns and trends in diseases, track their evolution, make treatments more effective for individuals, and even aid in disease containment through global monitoring.

2. Improving Retail and E-Commerce


Retail distribution and e-commerce utilize big data analytics to gain insights into customer buying patterns, optimize stock levels, and tailor advertisements. This technology enables timely delivery of advertising messages, instant price changes, and improved coordination of the distribution process to meet clients’ expectations in the e-business landscape.

3. Enhancing Financial Services


Big Data Analytics is an imperative ingredient in the financial industry for use in detecting fraud, managing risks, and making investments. Suspicious transactions and accounts will be detected, and a customer will be notified by the bank through real-time analysis of the user’s transactions.

4. Improving Marketing and Advertising Campaigns


Marketers take advantage of big data analytics and target certain groups, making campaigns efficient. Social media analytics tools help to track audience feelings and activities regarding specific products ginger in the particular audience to understand their tastes.

5. Enhancing Logistics and Transportation

Big data analytics optimizes supply chain and logistics operations by enhancing route planning, cutting delivery times, and lowering expenses. To ensure on-time delivery, logistics companies like FedEx and DHL track shipments and anticipate delays using real-time analytics.

Challenges of Big Data Analytics

• Data Quality and Integrity

The first and foremost challenge which requires focus and attention is maintaining the quality and trustworthiness of the data that has been analyzed especially in the span of Big Data Analytics. Cleaning and standardizing the format of large data sets is both tedious and resource-heavy.

Additionally, complexities related to how data is combined from different sources may deepen these problems because different systems may vary in the types of, formats of, definitions of, and levels of accuracy of the information recorded.

• Data Security and Privacy

There is always the question of security and privacy that arises when enormous amounts of sensitive data such as customer data, financial data, or other data related to healthcare are maintained. In any case, organizations have to meet a high-level threshold since such bodies have policies such as GDPR or HIPAA that require the organizations to handle data safely and acquire the permission of users.

• High Costs

Energy requirements and costs; resources needed to manage Big Data Analytics are high which means the system expenditures for infrastructure, software, and expertise are also considerable. Setting up large-scale data storage systems or cloud solutions and even maintaining these systems also cost large amounts of money as they heavily rely on advanced hardware systems and distributed computing frameworks.

• Scalability Issues

Along with the growth of the organization comes the increase in the data that is accumulated. The challenge lies therefore in guaranteeing that the analytics systems can scale to the extent needed in light of this growth while still upholding acceptable levels of performance.

• Rate of Data Expansion

Big data is constantly expanding and changing quickly. It can become very challenging to manage if you don’t have a strong infrastructure to support your network, processing, storage, and security requirements.

Conclusion

In the new era of digital development, big data can no longer be ignored. It is not only the future but also the present. The capability of big data analytics enables organizations to utilize data to generate more returns and make better choices. Organizations can also develop upon expanding data sets and large quantities of data. Learning about Big Data and Analysis of Data is important when working in Data Science and for that purpose, you must try the Accelerator Program in Business Analytics and Data Science with Nasscom by Hero Vired.