In the digital world, where automation and advanced technologies are the new norms, a lot of data is generated. Internet of Things (IoT) devices and smart machines generate industry-specific and real-time data sets that were untapped a decade ago. Forward-looking organizations and businesses are now leveraging these data sets to derive insights and a new form of knowledge that can aid decision-making. But what is big data, and what role does it play in the current decision-making processes?
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What is Big Data, and Why Should You Care?
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Big data is a collection of semi-structured, structured, and unstructured data collected by organizations that could be used to extract information for use in advanced analytics applications such as predictive modeling and machine learning projects.
AI-powered systems analyze millions of data sources and offer predictions and insights relevant to the industry or problems being solved. And the systems used to store and process big data are some of the most critical components that aid decision-making.
Large tech companies have succeeded in using big data to make critical decisions. For instance, Amazon’s recommendation algorithm finds a correlation between customer searches and previous purchase history, helping predict products that customers may be interested in. This can help improve the customer experience while boosting sales.
That said, big data comes from customer databases, emails, medical records, mobile apps, and social networks. These data sources are characterized by the “Five V’S”: volume, variety, velocity, veracity, and value. Here’s what each of the V’s represents:
- Volume describes the large amounts of data in terabytes or petabytes that are doubling over months.
- Value stands for the economic and social importance of the obtained data.
- Velocity represents the increased rate of data accumulation in businesses and organizations.
- Variety explains the different sources of big data.
- Veracity describes the quality of data necessary for accurate decision-making.
Role of Big Data in Decision Making
Companies use big data in their systems for various reasons. For instance, to make operational improvements, boost customer service, create personalized marketing campaigns, etc.
Organizations that use big data effectively can enhance their competitive advantage over those that don’t. This is because they can make faster and more informed business decisions based on real-world data instead of relying on their gut feeling.
Additionally, big data provides valuable customer insights that businesses can use to boost their marketing, advertising, and promotion with the end goal of increasing customer loyalty and conversion rates. And by analyzing both historical and real-time data to assess consumers’ evolving preferences, companies can better respond to their needs.
In the medical field, big data is used by researchers to identify signs and risk factors of a specific illness. Doctors can also use diagnostic analytics to diagnose a patient’s condition and the probable causative agent. Similarly, a combination of data from electronic health records, social media sites, the Internet, and other sources provides health and government agencies with up-to-date information on threats or outbreaks of infectious diseases.
Last but not least, big data also finds several applications in the energy industry. Here, big data helps oil and gas companies locate potential wells and monitor pipeline operations. Utility companies can also leverage big data to track electrical networks and make informed supply and demand decisions.
Decision Intelligence in Resource-intensive Industries
Decision intelligence is the application of AI in business with a commercial motive to enhance decision-making and therefore increase revenue and profits. Three critical aspects are needed when setting up your organization’s decision intelligence:
- An AI-ready data, i.e., well-structured data that is ready for manipulation.
- A centralized intelligence that serves as the company AI.
- An interface or a way for the AI to connect data to leaders and people.
Several operations benefit from data-driven insights in resource-intensive industries such as financial services, retail, and healthcare. Supply chain management, for instance, relies on accurate and real-time data from suppliers, consumers, and competitors. Without a system that can collect and analyze these data sets to yield helpful information, business operations would be a total mess.
Here’s how data intelligence, with the help of AI, ML, and data analytics, can streamline decision-making in resource-intensive industries.
Big data and data analytics can help organizations develop a sound customer care management strategy. Knowing what the customers need and what the market can offer makes it possible to go beyond the current limitations guided by accurate and real-time data. Here’s how data intelligence can help with customer care management:
- It helps with developing good pricing strategies and assessing customer satisfaction and quality of service.
- It helps improve supply chain management and ensure accurate predictability analysis and verification of customer data.
- It helps with acquiring new customers and retaining existing ones.
With big data and good analytics, companies can effectively improve their waste management processes. The main advantage of using big data analytics is the accuracy it provides. Here, the focus is on measurement; hence it’s easy to identify the most wasteful business processes. When using big data in waste management, determine the data you want to measure, e.g., fuel consumption, raw materials, time, etc. You also want to use the right software to analyze the available data and make the necessary adjustments to reduce the identified waste.
Before launching a product to market, you are expected to collect and research data related to competition, customer experience, pricing, and product specifications. You also need to know the major market trends, your customers’ needs, and the strengths and limitations of competitor products, among others.
Compared to traditional approaches, big data analysis provides a more accurate and comprehensive approach to product development. This approach also ensures that every product developed is suitable for solving specific customer needs.
Nowadays, talent recruitment and management are all done digitally, thanks to big data and analytics. From sampling the most qualified candidates based on their resumes to picking those with the most desirable personality traits, AI-powered systems are the unsung heroes in today’s human resources departments.
Besides helping with recruiting the best talents, AI and data analytics can also help manage employee performance. Today, AI systems help HR managers choose the best candidates for a promotion based on their performance, absenteeism report, error rate, profile data, etc. By tracking and recording all these details, AI and data analytics minimize the chances of hiring the wrong candidate or promoting a lazy employee who doesn’t deserve the corner office.
Additionally, big data in talent management helps business leaders identify productivity issues, helping them target talents with the skills needed to boost certain functions, such as sales, marketing, or R&D within the organization.
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Get Started With Big Data Today
Now that you know how big data plays a role in decision-making and how your organization can benefit from it, it is time to embrace this technology in your business or organization. Investing in big data means you are digitizing your operations and helping your company safeguard its competitive advantage.
Remember, as technology continues to evolve, more data will be generated, and how you use these data sets will determine your level of success a year, five years, or even a decade from now.
