The advent of Big Data made incredible amounts and types of data available to organizations. Today’s teams can take advantage of data from enterprise systems, SaaS solutions, websites, mobile apps, IoT devices, and more. But due to the massive volume and variety of this data, mining it for insights requires capabilities beyond traditional BI tools. Advanced analytics allows organizations to maximize the value of their data to achieve their business objectives more effectively.
What Is Advanced Analytics?
Advanced analytics uses sophisticated techniques and tools to uncover deep insights, make predictions, and recommend plans of action. It leverages advanced operations including predictive modeling, modern statistical methods, data mining, and machine learning to discover correlations and relationships and predict outcomes of various scenarios. This collection of practices and technologies has the power to efficiently analyze data in new ways to reveal important insights that would otherwise remain hidden in the vast stores of data that modern organizations collect.
Although advanced analytics doesn’t claim to rival the accuracy of a crystal ball and it does have significant limitations, implementing an advanced analytics strategy helps organizations plan more effectively and implement strategies that help them reach their goals.
Techniques Used in Advanced Analytics
Advanced analytics describes a basket of modern data analysis tools and practices. Each one approaches the available data differently to uncover useful patterns and relationships to reveal key business insights. Here are a few of the most common.
Data mining organizes and filters data, surfacing potentially valuable patterns and trends. It harnesses artificial intelligence, statistical processes, machine learning, and database systems to comb through mountains of data in an efficient manner. For example, a marketing team can use data mining to significantly expand the number of customer attributes they analyze. With standard analytics, this team may be limited to choosing five to focus on, but using advanced statistical processes and other data mining techniques allows them to cover 100+ attributes.
Predictive analytics uses qualitative analysis, predictive modeling, and deep-learning techniques to examine historical data in order to more accurately predict the probability of future outcomes. This analytical method is a powerful tool to more accurately forecast what’s likely to happen next based on a range of specific business conditions. Predictive analytics is often used by financial institutions to improve fraud detection and reduce risk.
Prescriptive analytics is the most powerful and potentially useful advanced analytics technique. The value in this method comes from its ability to prescribe the best way to achieve desired business outcomes. Prescriptive analytics goes one step beyond predictive analytics to provide informed suggestions on the best actions to take to achieve a range of potential outcomes. For example, prescriptive analytics can power product recommendation engines that suggest relevant products to online shoppers.
Augmented analytics leverages the power of artificial intelligence, machine learning, and natural language generation to complete data preparation and offer deep insights into large data sets. Augmented analytics automates complex analytics processes but it goes beyond automation—it offers a methodology to guide users to uncover insights they might not find otherwise. Augmented analytics can be divided into two primary use cases: augmented business intelligence and assisted machine learning.
Augmented business intelligence: AI can be embedded into BI platforms to provide assisted insights through the combination of UI and natural language processing.
Assisted machine learning: Also known as autoML, assisted machine learning helps users automate steps in ML to reduce barriers to adoption.
Advanced Analytics Use Cases
Advanced analytics practices aren’t just a theoretical possibility. Organizations in many different industries are applying these processes to solve real-world business problems today. Here are just a few examples of how advanced analytics strategies are being used to reduce risk, increase efficiency, cut costs, boost sales, and maintain or create a competitive edge.
Financial institutions are using advanced analytics in a variety of ways, but one of the most significant is risk reduction. Analytics-powered advanced early-warning systems are capable of mining transactional data in real time to detect fraudulent activity as it happens. And digital credit assessment and credit-collection analysis can be deployed to more thoroughly vet potential borrowers before extending credit.
Advanced analytics makes it possible to more accurately predict future demand for products by spotting trends early. This helps retailers to more effectively manage inventory, preventing unwanted goods from expiring and avoiding stockouts of in-demand products.
Serving the right ad at the right time to the right person is the Holy Grail of advertising. Advertisers want the ability to display highly personalized marketing messages to their market segments. Media companies have the ability to offer in-depth insights into audiences and specifically target ads accordingly using advanced analytics.
Manufacturers are using advanced analytics techniques in a variety of ways. Accurately predicting what manufacturing equipment will need to be serviced and when helps manufacturers avoid unplanned production outages. Analyzing real-time feeds from sensors on the manufacturing floor offers in-the-moment insights that can be used to spot quality control issues before they spiral out of control. Advanced analytics is also used to anticipate fluctuations in labor needs based on surges and lulls in product production demands.
Healthcare providers can use advanced analytics techniques to identify risk factors for certain medical conditions and respond by identifying treatments. Recognizing potential health issues early on and responding with appropriate interventions can dramatically improve healthcare outcomes.
Snowflake for Advanced Analytics
Advanced analytics processes require vast computing resources to analyze the enormous volumes of data involved. Additionally, advanced analytics requires bringing together disparate data sets to ensure all relevant data is being brought to bear.
The Snowflake Data Cloud offers near limitless compute power that scales as needed, providing an ideal platform for executing advanced analytics processes quickly and efficiently. It supports both structured and unstructured data and has built-in performance optimizations, data management, and administration as a service. Snowflake also offers an extensive partner network to expand advanced analytics capabilities, including automation, low code/no code, and acceleration of development and deployment. With Snowflake, you can unlock the power of advanced analytics for any organization.
See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.