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Impact of Big Data on Business

Big Data Important to Business

Why is big data important to business today? Big data consists of typically extensive data sets made up of structured and unstructured information that requires complex processing methods for their management and analysis. This processing can yield valuable insights affecting operational and business intelligence. As an indication of the importance of big data to business, it’s estimated that the global data sphere will grow to 175 zettabytes by 2025 and that the big data industry will be worth $77 billion by 2023.

Other big data statistics support the view that big data is important to business. With the widespread use of mobile devices, web applications, wearables, and the Internet of Things (IoT), unstructured and semi-structured information now makes up an estimated 80% of the data collected by enterprises. And with its increasing volumes, there’s a growing need for big data analytics tools, storage, and data management techniques.

What Constitutes Big Data?

Examples of big data include customer databases, transaction processing systems, medical records, internet logs, mobile applications, social networks, scientific research data banks, machine-generated data, and real-time data from sensors used in Internet of Things (IoT) environments.

Problems Big Data Helps in Business

Broadly speaking, the mining and analysis of big data can yield market intelligence and provide information that fuels operational efficiencies, cost reductions, greater customer engagement, and strategies for future development. Businesses that use big data effectively gain a competitive advantage over those that don’t, as they’re able to make quicker and more informed business decisions. The benefits of big data extend across a wide range of industries and applications. Let’s look at a few specific reasons why big data is important to business.

Simplifying Logistics and Improving Patient Outcomes in Healthcare

As electronic health records (EHR) supplant old paper-based methods of management, analysis of big data enables healthcare managers to uncover critical variables that affect staffing and logistics. For instance, during the current pandemic, factors such as weather, demographics, supply chain data, and contact tracing information help caregivers manage everything from personnel deployment to the allocation of personal protective equipment (PPE) and treatment protocols.

With big data analytics, the integration of cutting-edge technologies like automation, artificial intelligence, and machine learning into healthcare applications and devices enables remote diagnostic and treatment methods and provides patient-level resources such as wearable monitors and virtual nurse applications.

Reducing Fraud in the Finance Industry

Big data analysis provides financial institutions with deep insight into transactions, demographic information, and metrics charting their customers’ and clients’ behavior. With machine learning applied to these processes, anomalies become much easier to identify, enabling banks to catch fraudulent behavior as soon as it occurs.

Improving Process Efficiency and Quality Control in Manufacturing

Using big data techniques to track digital metrics associated with these processes enables manufacturers to identify potential bottlenecks to increase the efficiency of the many moving parts of a typical manufacturing industry workflow. 

Predictive analytics of machine and process data can give makers the information they need to implement preventative maintenance measures and identify possible sources of concern affecting their production output quality. 

Big data analysis can also help procurement teams identify ways to maximize their vendor discounts and to identify supply chain partners whose performance has a positive or negative impact on production.

Identifying Customer and User Behavior in Retail and Software Environments

big data important to business

For retailers and software developers alike, big data is important to business. Big data metrics can collect information that identifies which products or features are widely used and how long consumers remained engaged with these items. Monitoring of usage logs and social media commentary can reveal any pain points or notable positives that consumers experience, providing information for marketers and developers to improve the nature of their offerings.

Improving Infrastructure Management for Government and Utilities

As the paperwork typically associated with administration goes digital, it opens up opportunities for big data analysis to reveal patterns and trends that can lead to enhanced efficiency, the chance to automate repetitive processes and strategies for resource optimization. Big data insight into issues like traffic patterns and utility usage can enable governments and providers to identify existing or future problems and create a path to better infrastructure provision.

Problems Big Data Can Create in Business

While big data is important to business, it isn’t all good news. Besides its technological applications, organizations must consider how to use big data in ways that align with public and industry expectations concerning governance, privacy, security, and other issues.

One characteristic of big data that refers to the degree of certainty in data sets is data veracity. When raw data is collected from multiple sources, the degree of uncertainty associated with some data types (such as information from social media platforms and media streams) can cause serious data quality issues that may be difficult for organizations to pinpoint.

Bad data, which leads to inaccurate analysis, may undermine the value of business analytics and cause business executives to mistrust big data as a whole. In addition, not all data has real business value, and the use of inaccurate or low-value data can weaken the insights provided by data analytics applications. 

Big data handling can also impose considerable costs on the organization. The computing power required to process huge volumes and varieties of data quickly can quickly overwhelm a single server or server cluster, increasing the demand to hundreds or even thousands of servers to distribute the processing work and operate as a collaborative and clustered architecture. Smaller organizations may find this beyond the level of their budgets — which explains the popularity of cloud-based options for big data management.

Besides the processing capacity and cost issues, the design of a big data architecture typically requires skill-sets that may be lacking within a single enterprise. New skills above and beyond standard database administration are required, and a degree of customization is typically needed to tailor a particular big data system to an organization’s specific needs.

Security is another concern. The more data that an organization stores, the more opportunity that hackers have for stealing it by exploiting various vulnerabilities.

At the data governance level, issues around the collection, storage, usage, and transmission of data must be carefully considered to avoid infringements of existing privacy legislation and regulatory compliance conditions or the erosion of public and consumer trust.

of Big Data’s Importance to Business

it is little wonder why big data important to business today. Big data puts a measurable aspect on once-holistic concepts, such as “what consumers want,” and initiates data first inductive reasoning methods that enable even non-technically inclined people to manipulate information and derive insights from it.

Big data can identify problems and present opportunities in almost any circumstance. As device technology and data communications evolve, the volume of data is continuously growing — and with it, the importance of big data for organizations in every industry. Yet in this atmosphere, 73.4% of companies still report business adoption of Big Data and Artificial Intelligence initiatives as a challenge. 

Without a doubt, big data is important to business. And as we look to the future, we can expect the role of skilled big data professionals to increase in importance and the onus to increase on big data vendors to generate awareness of what’s on offer and expand their outreach.

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