Why More Industries Depend on Data Science Now Than Ever

Data is one of the most valuable resources available to any organization. In order to monetize it effectively, however, companies need to be able to store, analyze, and retrieve it right away. They also need to make sure that their data is both accurate and current, which is difficult to do through traditional means that rely on the manual transfer of data between disparate systems. These days, industries and business users everywhere are relying on big data in their attempts to gain insights. Such large quantities of unstructured data are impossible to analyze in a timely manner manually, which is why digital transformations have become necessary for all industries.

Using digital, integrated solutions allows business leaders to gather all data from each system into a single source of truth. This type of shared data is called master data, and managing it is becoming a required skill for business leaders everywhere, rather than just in computer science. Today, more industries than ever depend on data science to find useful patterns in data via machine learning models and data analysis. Here are just a couple of reasons why data science has become so important.

Data analytics help with risk management.

Enterprise risk management is the process of identifying potential events that could put achieving strategic objectives at risk or prevent the business from gaining a competitive advantage. Risk management should be implemented into all ongoing business activities. A data scientist with a master’s degree in finances, for example, could help a large bank develop an algorithm for determining acceptable risks for loans. Data science is crucial in risk management to explain complex findings to shareholders, test new solutions to problems, and validate the test data to ensure that new solutions will work.

Master data is invaluable to risk management since you’re attempting to enact safer practices for the entire enterprise rather than trying to fix up disparate silos. Deep learning artificial neural networks can process information in a similar way that a human would, but at much greater speeds. This allows for advancements in predictive analytics, which can detect problems before they even happen.

Data visualization makes it easier to offer personalized customer service.

Regardless of your industry, you have no greater resource than your customers. Providing exceptional customer service is the easiest way to inspire loyalty and retention, which is always easier than having to bring in new customers. These days, companies use data visualization in plenty of ways, from presenting complex data sets to shareholders to providing customers with convenient ways to customize products.

Configure, price, quote (CPQ) software uses data science techniques like predictive analytics and AI-driven pricing to provide a better ordering process to customers and boost sales. With a CPQ solution, customers can check visualizations of customizable products and see how their changes affect it in real-time. Even better, pricing information is updated in real-time as well, so customers always know how every change impacts their quote. Dynamic quote documents even let sales people make adjustments when needed, and customers can see every adjustment made and who made them, along with explanations.

Data science is also important for customer relationship management (CRM) systems. Data scientists can create analytics systems that determine which products and services are the most popular, what factors are leading to the most lost sales, or even predict how customer behavior will change in the near future. All of these insights are crucial for the sales team to adapt and for the customer support team to keep providing the highest level of customer service. Data managers can use all of this information to propose new initiatives to align company goals with what customer want and gain a competitive advantage.

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