The Application of Big-Data Analytics in Major Industries

What Is the Relationship Between Big-Data Analytics and Business Analytics?

Business analytics are useful in describing the collection, sorting, processing, and evaluating of any type of business data—typically by using statistical models and iterative approaches. The ultimate goal is to acquire actionable, relevant business insights that are useful in addressing internal issues and increasing efficacy, productivity, and revenues.

Similar to the use of big-data analytics, many business sectors and industries utilize business analytics to improve upon their essential processes and simplify their daily tasks. Let’s consider some common examples.

Finance

The goal of financial data analytics is to shape business strategies using trusted, reliable, fact-based insights rather than fallible, human instincts. By providing a detailed view of a company’s financial data, intelligent financial analysis provides business leaders with an easier way of gaining in-depth knowledge of key trends and acting to improve their overall business functions.

Manufacturing

Business analysts use data to assist stakeholders in understanding essential factors that could impact operations. Manufacturing sectors employ big data to identify the following:

• equipment downtime
• inventory levels
• maintenance costs

Ultimately, the use of big data can help organizations to streamline inventory management, risk management, and supply-chain management by developing measurable efficiencies and productivity improvements.

Human Resources

HR departments play a vital role in modern organizations. For a company to excel, they must choose employees who fit predefined criteria. HR professionals are responsible not only for identifying the best applicants, but also for working to bring them on board.

Business analysts contribute to this process by using data-analytics tools to view relevant data in an accessible format. They use these tools to identify high-performing applicants through factors such as their educational background and employment history, which are among the most useful insights into hireability.

Big-Data Visualization Tools

Data visualization involves the use of graphs and charts to represent data in more digestible ways. In the realm of big-data analytics, visualization tools and techniques are essential for the efficient analysis of large volumes of information and to enabling teams to make data-driven decisions.

Businesses often use big-data technologies such as distributed storage and processing systems, machine learning, and data-visualization tools to handle the scale and complexity of the data that the organization generates. Together, big data and cloud observability enable organizations to achieve greater operational efficiency, detect and resolve issues faster, and improve the overall quality of their cloud-based services.

Examples of big-data visualization tools include the following:

• Google Charts—A powerful, free, data-visualization tool, Google Charts is useful primarily for creating interactive charts for online displays. The program utilizes dynamic data and relies solely on HTML5 and SVG (Scalable Vector Graphics) for outputs, allowing in-browser functionality with no need for additional plugins. Users often use Google Charts to create maps, gauges, treemaps, pie charts, and bar charts, employing simple algorithms and intuitive controls to reduce their workload.
• Microsoft Power BI—This tool provides professional analysis of business intelligence. The main advantage of Microsoft Power BI lies in its accessibility and UX design considerations, allowing simple compatibility with tablets, desktops, and smartphones while still generating analytical findings more quickly.
• ChartExpo—Configured for particularly deep data analysis, ChartExpo is a great data-visualization tool that is compatible with both Google Sheets and Microsoft Excel. In minutes, you can turn confusing data into insightful, easily understood charts, using a plug-in framework that safeguards user data and ensures that files remain in their selected locations.

Wrapping Up

Most industries now realize the value of big-data analytics and data visualization throughout their daily operations. When processing large volumes of data, you can use visualization tools such as charts, tables, and graphs to easily summarize relevant information and to produce a more scannable and understandable analytics framework.

Business analytics, conversely, is useful for analyzing historical data using statistical methods and technologies to generate new insights and improve strategic decision-making.

Frequently Asked Questions (FAQs)

Let’s consider some questions that people frequently ask about big-data analytics.

1. What is the difference between data science and big-data analytics?

Data science is a field of study involving the collection of data for use in predicting, analyzing, and producing findings in relation to a specific subject. In contrast, big-data analytics is useful for describing voluminous information that you must first compile to provide usable insights. This process requires software analysis because the data set is too large for manual study.

2. How can big-data analytics affect business results?

Data analytics is important because the process helps businesses to optimize their performance. Implementing big-data analytics into business models means companies can reduce their costs by identifying potential improvements in efficiency and store relevant data in more manageable ways.

3. What are the biggest challenges facing big-data companies?

The biggest challenges that big-data companies face involve the current lack of skilled data professionals, an absence of reliable data-security methods, and a limited understanding of how best to manage large pools of information.

4. Can small industries use big-data analytics in their decision-making?

Yes, small industries can use big data in their decision-making. For example, if their main goal is to grow their business, early adoption of the practice of using big-data analytics would prove to be an advantage in the long run.