How Companies Are Using Big Data To Sustain and Evolve?

In the era of multiple data sources, traditional data handling methods could not cope with rising data storms. Businesses had to shift to effective operations using large volumes of daily data.

About 328.77 million terabytes of data is generated daily.

Big data refers to the complex and large volume of data available from varied sources. This big data movement has brought a positive shift through the alchemy of data and analytics in business, which enables improved decision-making, optimal data handling, and increased revenues for data-driven businesses. By the end of this article, you will develop an understanding of big data fundamentals and how companies use big data to evolve.

What is Big Data?

 Big data is referred to as an enormous quantity of data that is available and expanding rapidly.  The big term simply refers to very large and complex data sets consisting of continually growing structured, semi-structured, and unstructured data, which is hard to manage using traditional data processing tools. 

Big data is a massive collection of data with high volume, greater variety, and greater velocity. It is difficult to handle using traditional processing techniques, so experts need to deploy added infrastructure to administer, observe, and build insights using this data.

The idea of big data germinated in the 1960s and 1970s with the development of data centers and relational databases. Data volumes surged in the early 2000s with the popularity of web traffic, e-commerce, and social media platforms. This created a need to handle and analyze large data sets to support the business’s decision-making process. 

Additionally, with the increased use of mobile devices, sensors, wearables, and IoT devices, zettabytes of data are generated daily. This extraction of meaningful insights and information is documented as big data phase 3. 

Why Is Big Data Important For Companies?

Companies are overwhelmed with large-scale data, but it can be an asset if useful information is regularly extracted. Big data, with its complex information, can answer probable business questions. Scrutinizing this data can provide valuable insights for efficiently solving business problems.

Big data models data flexibly, allowing cohesive analytics across varied information sources and offering a uniform view of critical actions and solutions. Here are some important reasons for leveraging big data in companies:

• Quick and Invaluable Decision Making

The availability of large volumes of data from multiple resources provides businesses with more information that can be accessed and utilized to yield insights. Businesses can drill the data to find information and patterns to solve typical business problems. Big data analytics has gained popularity due to its quick and rigorous decision-making. 

• Enhance Operational Efficiency and Reduce Costs.

Although big data stores complex data, it offers flexibility in storage and accessibility. The data processing and storage costs are low to achieve high operational efficiency and minimize storage costs. These optimal solutions make it suitable for more effective embedding in business operations.   

• Better Resource Management 

Smarter decision-making helps create organized business plans. Structuring aids in arranging and using the required resources to ensure swift business operations. 

• Greater Customer Attraction

Small and large businesses need to find a suitable customer base and understand their customers. Big data and machine learning make it easier for businesses to attract potential customers. Creating insights and recommending clients according to their needs helps in customer segmentation. 

• Generates Revenue and Development Prospects

Big data brings predictive analysis,  operational efficiency, better resource management, smart decision-making, and effective customer attraction. This helps companies optimize their costs and generates newer growth opportunities.

Data Safety and Big Data

Big data is accompanied by big security issues. The incoming complex data is filled with sensitive information, which can cause critical problems if leaked. Crucial business data must be masked from probable cyberattacks, security breaches, and data loss.

Companies should follow austere data privacy ethics to facilitate access and management of cloud data. Rigid privacy agreements should be established for stronger data protection, but flexible data accessibility should be ensured to ensure seamless data usage.

Data safety can be ensured by understanding the data, data stores, and backup solutions. Additionally, protecting the data from unauthorized access, creating awareness, and giving training about data security and regular risk assessments can ensure data safety in business. Businesses deploy proper data management systems and access control for added data safety. 

Big Data and Risk Analysis

Risk analysis covers finding possible wrongdoings and quantifying the harm they will cause. It ensures the safety of the business by identifying security measures. Big data shields the business by discovering possible patterns for loss or downfall.

These large data sets can find upcoming and present customer patterns. With the use of statistical analysis, companies can derive comprehensive business plans to detect shifts in behavior. Forecasting patterns using statistical analytics can help identify attributes that affect the growth of a business.

Risk mitigation measures can be applied early by getting answers to the questions using artificial intelligence and machine learning. Drilling over chunks of big data helps identify susceptible digital and financial frauds, customer defection, and unusual behavior.

Risk analysis helps businesses cover security loopholes and block the gateway to breaches. Additionally, hindsighting past incidents and critically evaluating them to find better solutions makes the system stronger and safer.    

Learn Different Types of Big Data

Big data grows exponentially from social media platforms, government portals, and streaming data from wearables, mobile devices, and IoT devices. Businesses that use this proliferated data strategically gain success. Modern computing gives businesses speed, flexibility, and power to address their challenges. This data is entered in different forms, which are discussed below. 

#1 Structured Data

Well-defined and organized data is structured data. This data type has a fixed format and is easy to understand, store, and access. Well-structured information can be easily accessed using conventional methods. This category includes information like address, employee details, age, financial card numbers, expenses, and billing data stored in spreadsheets and tables. 

Also read: A Detailed Insight into Structured Data in Big Data

#2 Semi-structured Data

Semi-structured data combines structured and unstructured data, such that the data complies with the attributes of a defined structure but includes unstructured information that does not fit in the fixed data format.  The illustrations include NoSQL, JSON, XML, and CSV files. 

#3 Unstructured Data

Covering the broadest portion of the big data, most incoming data is unstructured. Due to its unfamiliar structure and heterogeneity, it is called complex data. It combines various unstructured content from the internet, including formats like video, audio, images, and logs. The examples are data generated from satellite images, emails, and Facebook. 

#4 Geospatial Data

Geospatial data is a part of big data that includes information on locations or other features relative to the Earth’s surface. It is a combination of spatial information and the attributes of the data. The data stored in nature may be static (e.g., the location of an earthquake or a building) or dynamic (e.g., a moving vehicle). 

#5 Open Source Data

Open source data is an infinite pool of information. This can be customized to meet users’ demands and professional requirements. It is mostly used in innovative solutions, being free of cost and open to sharing. This type of big data makes data analysis affordable and agile. 

#6 Operational Log Data

Also known as machine data, operational log data stores information produced by the computer without human intervention. Machines’ continuous use increasingly accumulates data from everyday operations like call detail records and application log files. Operational log data helps discover user behavior and patterns. 

Elements of Big Data- The Vital V’s

Big data is data collection from multiple sources into the data pools. Businesses that know their data are bound to succeed. Big data management systems are used to understand big data functions and characteristics. Businesses apply machine learning to discover patterns and build insights from big data. This section explains big data’s five vital V’s: Volume, Variety, Velocity, Veracity, and Value.

• Volume

Volume is the most prominent feature of big data, making it ‘big’. It is the size of data stored and processed in the data system. The sudden surge in devices and internet usage increased data volume in the previous decade. The volume of data is measured in gigabytes, petabytes, zettabytes, yottabytes, and exabytes. Data processing tools and technology are used by the giants to access large data volumes.

• Variety

Big data is heterogeneous. Depending on the data formats entering the system, it may be well-organized or unstructured. This format corresponds to the attribute variety of big data. The system’s diversity must be seamlessly merged to ensure hassle-free data management.

• Velocity

The pace at which the data accumulates and is processed in the system is attributed to Velocity. With real-time evaluation, well-integrated systems handle enormous quantities and pace of data.

• Veracity

Data is an asset only when reliable and authentic; this virtue is attributed to its veracity. This information source must be checked for its credibility. The veracity of the data must be checked at regular intervals to eradicate the noise and destructive data from big data.

Also read: Understanding the Basics of Data Veracity in Big Data

• Value

Value is the benefit businesses can receive as a return by investing their efforts to manage and handle big data. Big data comes up with opportunities and challenges, but effectiveness comes up when it can serve the business requirements. 

Know Various Use Cases of Big Data 

Big data has contributed to various business operations. Incorporating big data analytics in business operations returns value as growth and increased revenues. Improved network capacity, Financial regulatory and compliance analytics, Fraud detection, Tourism, and Agriculture are some industries that use big data for development. Some other big data use cases and examples of companies using big data are listed below.

• Intelligent Manufacturing Systems and Product Optimization

The advent of the digital revolution has positively shifted the manufacturing industry. Industries harness big data to derive new techniques and solutions in manufacturing and product development.

Businesses use big data to analyze and evaluate production, consumer criticism, and profits to shrink outages and predict upcoming demands. Intel and Tata Consultancy Services have implemented customized product development to gain high revenues. Also, Netflix and P&G utilize big data to satisfy the expected customer demands.  

• Predictive Maintenance

Big data can help predict system vulnerabilities and failures. By analyzing structured and unstructured data, these susceptibilities can be foreseen. Timely audits can help apply maintenance, maximize equipment uptime, and apply mitigation measures to ensure systems’ smooth functioning.

• Customer Experience and Value

Companies that use big data tend to attract and retain their customers. Big data leverages the company with customer behavior, buying patterns, and retention. Detailed analysis of big data helps companies offer customer-centric promotions to generate higher revenues. E-commerce shopping platforms attract their customers using big data analytics.

• Healthcare Research and Analytics

The healthcare industry collects massive amounts of data and uses it to save lives through its advancements. Big data helps science evolve with technological developments. Analytics helps discover hidden possibilities, threats, and opportunities in the business through predictive modeling.

Genomic research is one such field that can grow tremendously by using big data. This will help in designing personalized treatments based on genetic disease identification, biomarkers, and future health issues.   

• Oil and Gas Discovery and Production Optimization

Big data supports innovation in the oil and natural gas industry. Data sensors help monitor and locate oil repositories, machinery, and drill operations. Oil and gas companies use big data to apply predictive equipment maintenance, oil exploration, discovery, and production. 

Also read: Oil-Gas Industry and Big Data Analytics

Conclusion

Big data is as vast as its name suggests. It brings endless opportunities for the companies that use it in operations. Big data aligns businesses with specific goals to make knowledge discoveries and bring revenue to the business.

Deriving sentiments, understanding customer behavior, optimizing knowledge transfer, and incorporating cloud models help companies achieve new milestones. Mining complex data from various information sources can be an exigent job.

Despite the challenges, businesses harness data to stay in the race as precise information, experienced manpower, resources, comprehensive coping approach, and compliance towards data integrity and safety offer better customer experience.  

FAQs

• What is Big Data in companies?

Big data in companies is a novel procedure of using voluminous data to find innovative solutions to business problems. It effectively answers questions like finding customer trends, gaining insights, finding loopholes, security breaches, and maintenance issues.

• How do companies use big data?

Businesses can follow five simple steps to use big data in their operations. Build a big data strategy

1. Identify the data sources
2. Access, control, and store data
3. Evaluate the data
4. Make fast and intelligent data-driven decisions. 

• Which company is best for big data?

Oracle, IBM, HPE, and Teradata are a few big data players.