April 5, 2021
By: Lyat Avidor Peleg
Advanced Analytics vs. Predictive Analytics vs. Descriptive Analytics
What is the difference between these 3 types of analytics, and what business benefits do they bring?
Analytics are nothing new for the business world. Every industry and vertical taps into the power of analytics to better understand their market, their audience, and their internal performance and productivity. But analytics is a dynamic field. With the evolution of computing ability and development of artificial intelligence, the analytics domain has grown and developed.
Today, businesses can choose between a number of different analytics platforms and tools, applying new advanced data theories, algorithms, and approaches to get more out of their data.
The wealth of options can be overwhelming, so here’s our overview of three main types of analytics: advanced analytics, predictive analytics, and descriptive analytics. Each has its own characteristics, and each can bring business benefits to various use cases.
Advanced analytics is a very broad term which includes a number of different analytics tasks. Essentially, advanced analytics is distinct from traditional descriptive analytics, or business intelligence, because it applies automation and artificial intelligence (AI) to cope with far more complex datasets and produce far deeper insights and predictions. It draws on real-time data as well as historical data to produce faster conclusions.
Advanced analytics can include machine learning (ML), neural networks, forecasting, complex event processing, data modeling, semantic analysis, and more to identify patterns and trends in enormous datasets. It uses data mining to unearth more information from multiple different sources to develop a more complete picture of the situation.
Unlike traditional analytics, advanced analytics can cope with and extract meaning from complex data, unstructured data, and partial or incomplete data. Like BI tools, advanced analytics platforms generate data visualizations to help users grasp the information before them, but advanced analytics visualizations are more complex.
Advanced analytics helps business leaders to make data-based decisions, recognize opportunities, detect and mitigate risk, spot and stay ahead of trends, and understand customer demands.
For example, streaming media providers analyze user data to understand which customers are more likely to churn and investigate which actions would increase retention. Marketers apply advanced analytics to a whole range of activities, from optimizing social media ad spend to deciding which promotions to offer each segment of the target audience and identifying the most relevant audiences to target in the first place.
Retailers were quick to embrace advanced analytics, using them to forecast shopping trends so they can ensure they are stocking the right products and to optimize inventory levels so that they can meet consumer demand without overstocking. Ecommerce businesses also turn to advanced analytics to gain visibility into their supply chain and improve their fulfilment, logistics, and last-mile delivery capabilities.
Predictive analytics is a subset of advanced analytics, although often the two terms are used interchangeably. Predictive analytics uses deep learning and machine learning to crunch enormous datasets, recognize patterns within them, predict outcomes, and then weight probabilities. Predictive analytics draws on contextual data and real-time data from multiple sources and fills in missing data with its “best guess,” based on observations from the rest of the datasets.
Predictive analytics itself includes a number of sub-sectors, like predictive modeling to test out possible scenarios; forecasting; pattern identification; sentiment analysis; and root cause analysis to understand the true causes of events like a sudden spike of customers or a big fraud attempt.
With predictive analytics, executives can make predictions about what will happen next and use that guidance to plan for the future, set goals, manage performance expectations, and mitigate risk. Finance industries like banks, insurance companies, and loan companies use predictive analytics heavily for credit scoring and fraud detection. Predictive analytics enables fast decision-making about the risk profiles of each applicant or customer so that the business can make an offer within minutes instead of days.
Predictive maintenance and predictive monitoring are common use cases for predictive analytics in manufacturing industries. Predictive maintenance provides early alerts about anomalies that indicate that a part might be about to fail, so that maintenance teams can schedule repairs more efficiently and fix parts while it’s still possible. Predictive monitoring moves beyond tracking parts to apply the same principles to the entire plant, identifying signs of inefficiencies in plant processes.
Businesses across various vertical industries apply predictive analytics to gain visibility into supply chains, especially complex supply chains that cross continents and suppliers. It is also used to improve customer service and gain a better understanding of the customer demands; for HR departments to increase employee retention; and for cybersecurity teams to identify vulnerabilities and assess potential threats. Healthcare providers use predictive analytics solutions to sort digital medical images like X-rays or MRI scans, to improve diagnostics, and ecommerce businesses use it for recommending products to customers.
However, data-based predictions are never completely accurate. Users also need more training to understand predictive analytics outcomes correctly and interpret data visualizations, unlike descriptive analytics, which are far more accessible. Before businesses can apply predictive analytics, they need to unite and prepare data and create and train models, which takes time and skill. Additionally, predictive analytics is only effective if you have sufficient data.
Descriptive analytics refers to traditional business intelligence (BI). It involves the collection and processing of historical data, and interpreting it to understand past events, without making forecasts about the future or attempting to predict what may happen next. Descriptive analytics uses simpler data visualizations that help users understand the implications of the results quickly and easily, like line graphs, bar graphs, and pie charts, rather than the more complex, multi-dimensional models produced by advanced analytics.
Descriptive analytics includes most social media analytics, like page views, response time, or engagement rate. It’s often the foundation for more complex analytics further down the line. Descriptive analytics still requires users to set metrics, aggregate data, clean and process the data for use, and present it in an accessible format, although most modern BI tools automate many of these tasks.
Descriptive analytics has the advantage of being relatively easy and quick to apply, making it useful for day to day operations, producing financial reports, managing marketing strategies, and tracking inventory and sales. Employees can usually access descriptive analytics without much, or any, training, lowering the bar to entry and democratizing the availability of relevant business insights. It helps businesses to measure progress, check that goals are being met, and identify areas that need improvement. However, descriptive analytics solutions only scratch the surface of data insights and can’t dig deeper into data intelligence.
It’s hard to think of any business, no matter how big or small it is or what vertical it relates to, that does not make use of descriptive analytics. Descriptive analytics use cases include refining marketing campaigns by tracking engagement and conversion rates; tracking sales levels through different seasons; and comparing revenue and profits to keep the business on track.
Analytics Keep on Evolving
Advanced analytics, predictive analytics, and descriptive analytics all have their role to play in different business use cases. By helping companies and business leaders to understand past events, predict possibilities for the future, and compare patterns, different types of analytics solutions can improve data-driven business decision-making and bring value to your company.