All businesses sooner or later face data security threats. If they use data analytics to diagnose the causes of past data breaches by analyzing the relevant data, they could prevent potential threats in the future. IT departments can use data analytics applications to analyze the logs to determine the origins of an attack. IT departments can also use statistical models to prevent the more organized type of attacks. This could be a load-based attack such as a distributed denial-of-service (DDoS) attack. Businesses can build statistical models including monitoring and alerting systems layered on top to detect which run continuously and flag anomalies. In that way, countermeasures could be immediately put forward to defend the system.

Besides acquiring new customers, companies are increasingly under more pressure to better understand their customers’ needs. Knowing the customers better, which is a formidable competitive advantage, gives them the ability to optimize customer experience and develop longstanding relationships. Today's customers expect their companies to know them better, to provide better interactions and a seamless experience.

Companies now collect customer data from many different channels, like physical retail, e-commerce, and social media. By using data analytics, companies are able to create comprehensive customer profiles from this data. Then, businesses can gain insights into customer behavior to provide a more personalized experience.

There are always various types of risks in businesses. These risks could be a customer or employee theft, uncollected receivables, employee safety, market shrink, new players, and unconventional competitive environment, and legal liability. Data analytics can help an organization to better understand these types of risks and give them the ability to take preventive actions.

Businesses can also use data analytics to minimize the losses after unpredictable events occur. For instance, If a business overestimates demand for a product, and some products remain unsold, later it can use data analytics to estimate the optimal price for a clearance sale. Businesses can even build statistical models for this type of situation so that the model automatically makes recommendations on how to resolve recurrent problems.

Businesses can improve operational efficiency through data analytics. Gathering and analyzing operational data can give valuable insights to understand for instance where the production delays and bottlenecks occur, which components give frequent fails. Data analytics help businesses to predict where these types of future problems may arise. For instance, if a specific vendor will not be able to provide the volume required for seasonal sales, the company can understand this type of situation beforehand by using data analytics. Another important use case is inventory management. Businesses are struggling to optimize their inventory levels to minimize their stock costs. Data analytics can help businesses to better anticipate the optimal supplies of enterprise’s products based on factors such as seasonality, holidays, and trends.

Businesses can use data analytics to help managers to make the right business decisions and hence maximize the profit or minimize the financial losses. Predictive analytics can give valuable suggestions about what would happen in response to changes in the market. For instance, a business can build a model regarding changes in pricing or product offerings and how those changes would affect customer demand. We call this data-driven decision making (DDDM). It is a process of involving collecting data, based on measurable goals or KPIs, analyzing patterns and facts from these data, and utilizing them to develop strategies from which the business can benefit in a number of ways.

Data-driven decision-making can give various types of advantages to companies including creating new business opportunities through predicting future trends, increasing revenue from current products and services, and optimize current operational efforts. In that way, companies develop actionable insights.

All businesses sooner or later face data security threats. If they use data analytics to diagnose the causes of past data breaches by analyzing the relevant data, they could prevent potential threats in the future. IT departments can use data analytics applications to analyze the logs to determine the origins of an attack. IT departments can also use statistical models to prevent the more organized type of attacks. This could be a load-based attack such as a distributed denial-of-service (DDoS) attack. Businesses can build statistical models including monitoring and alerting systems layered on top to detect which run continuously and flag anomalies. In that way, countermeasures could be immediately put forward to defend the system.