The role of big data in business decision-making

Big data has become a new ‘mantra’ of both innovation and strategic decision-making in digital businesses of all types. Big data is characterized by big volume, high velocity, and a great variety of data sources. For many organizations, Big data is nothing less than a new frontier in how data is collected, analyzed, and used to derive insight from data.
One of the main empirical justifications for reliance on big data is that it reveals hidden patterns, as well as correlations and trends in data, that might otherwise have been invisible to conventional data-processing methods. Tapping into just a fraction of the terabytes, petabytes, or even exabytes of information that flow from social networks, IoT objects, and transactions, firms can obtain valuable insights about market dynamics, types of customers, and operational efficiencies, resulting in better decision-making.
Big data has the ability to help decision-makers find answers to a range of questions on an increasing basis. This is an important shift, spurred by the presence of a wealth of high-resolution data, faster computers, and competitive venture capital in Silicon Valley pursuing game-changing concepts.

Understanding big data

The defining qualities of Big Data are often described in terms of the 3Vs (or sometimes the 5Vs): Volume, Velocity, and Variety. These three dimensions exemplify the distinctive challenges posed and the unique opportunities afforded by the large-scale datasets of today’s digital world.

Volume

Scale (also called volume) enables us to capture the sheer quantity of data from various sources being generated and gathered by organizations, industries, and society. The traditional technology systems used for collecting, processing, and storing information cannot handle Big Data’s massive scale, ranging from low terabytes to multiple petabytes or even higher. For instance, social networking sites generate staggering quantities of data every day. The number of data bytes users exchange, create, or upload while visiting a social networking site – or through other activities such as image and video sharing, comments, taking quizzes, and others – is enormous. The same holds true for Internet of Things (IoT) devices, which generate millions of data and metadata payloads every second and are primarily made up of sensor data streams.

Velocity

One is the velocity (the speed at which data can be generated and processed and then stored and analyzed in near-real or real-time). With digital technology and networked systems, data can stream into organizations faster than ever before. A bank or online retailer needs to process financial transactions or store order information in near real-time to respond to customer demands. Sensor readings from equipment inside factories and assembly lines need to be processed in near-real time to stay on top of developing problems. Data with high velocity helps businesses respond quickly to market changes, adjust to interpret shifts in customer behavior or address emerging operational problems, which helps them to make more nimble decisions and gain a competitive edge.

Variety

The third dimension of Big Data is variety. It is about the disparate types of data and its sources. The data could be in a structured form (e.g., databases, spreadsheets), semi-structured form (e.g., XML, JSON), and unstructured form (e.g., text documents, emails, tweets, images, videos, sensors, etc.). The variety also represents its diverse data sources such as clicks, purchases, location, demographics, device specification, sensor data from things such as electric meters, washing-machine pumps, embedded chips in prescription bottles, mobile phones, tablets, geospatial networks, blogs, network, machine-generated logs, emails, tweets, etc and many more.

Importance of big data in business decision-making

To make a solid business decision, a leader analyzes all the reliable information, uses math to create numbers that provide more insight than the original information, and identifies trends or correlations that can be used to predict outcomes. This is the fundamental goal of analytics – gathering data, synthesizing it into information, then gaining insights with smart algorithms. In the best-case scenario, arming the decision-maker with the finest predictive elements so they can make the optimal decision. Big Data is poised to change this value stream, as illustrated below.

Enhancing data-driven decision-making

By increasing our capacity for evidence-based decision-making, Big Data broadens the parameters of ‘good decision-making’ to include insights that emerge from analyzing big, diverse data sets. Unlike classic analytics, based mainly on small sample data sets or a limited number of query-based information queries, Big Data draws on machine learning, predictive modeling, and data mining to discover hidden patterns, correlations, and trends. Through processing huge amounts of structured, semi-structured, and unstructured data in real-time or near-real time, institutions can better understand marketplace dynamics, behaviors of customers, and operational efficiencies.
For example, a retail company can use big data analyses to tailor marketing messages on online and social media according to the purchase histories of different customer groups or to better monitor and manage inventory stock. In healthcare, Big Data can create personalized medicine and predictive diagnostics to better prevent or treat patient illnesses while efficiently allocating and managing healthcare resources.

Case studies

• Netflix - Big Data helps Netflix to improve their recommendation engine, i.e., the system that suggests movies and TV series you might be interested in, based on what you have watched already and how long you spend watching shows. This data also helps the company in self-improvement, and by understanding the customer’s experience and habits in real-time, Netflix is also able to, in turn, tailor a user’s experience to their taste, likely keeping that user around longer and increasing the company’s business growth and competitive advantage.
• Amazon - Amazon uses Big Data to make supply chain management and logistics processes as effective as possible. Through analytics, Amazon can anticipate fluctuations in demand, reduce shipping times, and lower operational costs by analyzing customer orders, inventory levels, climate variables, and transportation data. Because of this, Amazon ensures high service levels and efficient customer expectations.
• Uber - Uber uses Big Data analytics to better optimal dispatch service and provide superior user experience. By analysing real-time traffic data, driver location, user preference and pricing dynamics, it is able to adapt its algorithms to accommodate shifting demand and supply curves, shorten waiting times, and to generally improve the efficient delivery of services. This data-driven human-system dynamic has helped fuel Uber’s runaway growth and worldwide expansion.

Applications of big data in business decision-making

Big Data is changing the nature of business decisions across a wide range of sectors, giving organizations new insights into their markets and the power to act on that information. This section provides examples of how Big Data can be used to improve market analysis, identify operational inefficiencies, and enhance risk management.

Market analysis and customer insights

As an example of the positive impact of big data analytics, companies can gain a better understanding of their potential market and consumers’ individual or group behaviors and preferences in order to develop marketing strategies and establish specific products. With geo-demographic information, behavior data from social networks, online shopping history, feedback, and queries, the following market-related activities, which are essentially strategic decisions, become easier:

• Personalize marketing campaigns - Tailor the marketing approach and the messaging dynamically based on personal tastes, purchasing histories, or a segmentation approach.
• Forecast demand - Identify patterns and trends in your market by processing actual historical sales data, swings in consumer sentiment, and other external factors that can impact purchasing decisions.
• Improve customer experiences - Anticipate and enhance customer needs by providing targeted recommendations, personalized promotions, and support.

For example, online retailers such as Amazon use Big Data to study customers’ browsing habits, purchase history and product reviews in order to provide better product recommendations, price points and overall experiences, which then increases customer engagement and sales growth.

Operational efficiency

Big Data analytics improves efficiency by optimizing processes, resource allocation, and workflow, among many other things. Companies utilize insights from analyzing analytics in:

• Streamline supply chain management - Make more accurate predictions of demand fluctuation, and decrease the amount of ‘dead’ stock by anticipating changes well in advance. Streamline supply-chain management through real-time information.
• Enhance production efficiency - Optimize manufacturing processes, reduce downtime, and increase product quality by analyzing sensor data, production metrics and equipment performance.
• Optimize financial performance - Analyze financial transactions, cost structures, and revenue streams to find cost-saving opportunities, manage cash flows, and improve profitability.

For example, Big Data analytics helps manufacturing companies predict equipment performance, plan maintenance, reduce downtime, and maximize production output and operational efficiencies.

Risk management

Big Data can be put to work to facilitate proactive risk management by using predictive analytics to identify, prioritize, and neutralize a risk before it turns into a more serious issue. The corporate world uses Big Data for:

• Fraud detection and prevention - Identify outliers in user behavior patterns, transactional data, sales, and billing to catch fraud in real-time, based on learning from past fraud cases.
• Credit Scoring and financial risk assessment - Assess creditworthiness, analyze loan risks, and manage the lending process using high-powered computing of financial data and predictive analyses.
• Cybersecurity - Strengthen your cybersecurity defenses by tracking network traffic, detecting suspicious activities, and preemptively mitigating security threats.

Banks and insurance companies use Big Data analytics to calculate credit risks and identify fraudulent claims, but they also minimize risks to their customers’ security—including through measures to comply with regulations to protect their customers' personal data.

Challenges and considerations in big data adoption

Despite any transformational impact of Big Data on business decision making in case adoption succeeds, enterprises must overcome a host of key challenges related to data privacy and security, as well as institutional barriers to wider uptake. This part outlines the critical challenges.

Data privacy and security

In Big Data environments, a vast amount of personally sensitive information can be handled. Individuals have a right to be concerned about their privacy and the safeguards in place in an organization for data protection. Therefore, when considering Big Data, it is clear that adequate measures must be in place to:

• Ensure regulatory compliance - Process personal and sensitive data in line with General Data Protection Regulation (GDPR) in the European Union or Health Insurance Portability and Accountability Act (HIPAA) in the United States, both of which regulate how data is collected, stored and treated.
• Implement encryption and access controls - Use encryption to protect data at rest and in transit, and fence off access to it using tight access controls.
• Monitor data handling practices - Take steps to regularly monitor data handling practices, including running risk assessments and reviewing data processing activities so that legal and regulatory obligations can be met. Third, build trust. Lastly, monitor and manage reputational factors. Undertaking Data Responsibility practices builds trust.

For example, any health organizations collecting and processing health records and medical data of their patients are bound to protect the records and requested data against any unauthorized access; so, health data needs to remain confidential, ensure integrity, and, at the same time, can be exploited for personalized (Big Data) medicine and healthcare.

Overcoming barriers to adoption

The adoption of Big Data analytics may encounter several barriers that hinder organizational readiness and implementation. Key considerations include:

• Cultural shifts - Create a data-driven culture in which decisions are based on data-informed insights rather than gut instinct or precedent. Develop new ways of working and generate cross-departmental collaborations that embed data analytics into strategic planning and core business workflows.
• Skills gap - Overcome the shortfall in the number of suitable data scientists, analysts, and IT specialists who can translate Big Data into business-critical insights, create predictive modeling systems, and identify certain patterns and correlations that might otherwise escape our reach.
• Infrastructure requirements - Paraphrase the input into human-sounding text while maintaining citations and quotes. Infrastructure needs – Provide scalable infrastructure, cloud computing resources, and data stores to manage Big Data’s volume, velocity, and variety. Ensure your existing IT systems and hardware can integrate with the new analytics tools and technologies.

Conclusion

Big Data has dramatically changed the decision-making landscape by generating insights previously unavailable to many organizations. Vast amounts of data are created daily as raw material for studying past events and predicting the future. These data, collected from various sources, are then processed to derive meaningful information. This, in turn, is used to make more effective strategic decisions.
Looking ahead, there will be tremendous growth and transformation in the realm of Big Data. The integration of artificial intelligence (AI) and machine learning will make predictive analytics tools more powerful, with businesses able to anticipate needs, make better decisions act on them in near real-time, and engage in customized interactions on a large scale. Real-time analytics will grow in importance. Already, it facilitates round-the-clock interactions with customers, with near-instantaneous responses to their queries, needs, wants, and preferences. In the future, it will empower businesses to react instantly to new market conditions and better anticipate competitors’ moves, giving them an edge in time, agility, and foresight. Edge computing will integrate data-processing functions closer to the source, which will be particularly important to businesses that use large numbers of IoT devices across distributed operations.