Advanced Analytics and Predictive Analytics

Potential and processes of modern analytical methods
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Advanced analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools. This typically goes beyond traditional business intelligence (BI) to gain deeper insights, make predictions, or generate recommendations. These advanced analytical methods include data mining, machine learning processes, neural networks, and predictive analytics.
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Application areas for advanced analytics and predictive analytics
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Advanced analytics refers to data analysis that goes beyond simple mathematical calculations like sums and averages or filtering and sorting. These advanced analyses use mathematical and statistical formulas and algorithms to generate new information, identify patterns, and detect trends. Machine learning also plays a central role in advanced analytics. Typical application areas for advanced analytics include:
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• Segmentation (creating groups based on similarities)
• Association (determining the frequency of co-occurrences)
• Classification (e.g., of previously unclassified items)
• Correlation analysis (identifying relationships)

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Predictive analytics focuses on identifying future events and their respective probabilities. It primarily uses historical data to build a mathematical model and capture trends. This model is then applied to current data to make predictions about future events. There are a variety of potential use cases for predictive analytics:
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• Aerospace: Condition monitoring of engines and other critical machine components
• Energy production: Forecasting electricity demand and pricing
• Financial services: Credit risk forecasting
• Mechanical engineering and automation: Failure prediction
• Medicine: Pattern recognition algorithms for disease detection
• Automotive industry: Development of driver assistance algorithms

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Other general examples include forecasting income, prices, or revenue, as well as demand or customer value, for instance to minimize contract cancellation and churn rates. Big data and machine learning also play a role in predictive analytics processes.
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Predictive analytics workflow
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Predictive analytics processes involve creating mathematical models (predictive models) to identify current trends and subsequently make forecasts about future events. To build these models, these processes use data (including big data) in combination with analytics, statistics, and machine learning techniques.

Such forecasts serve to optimize resource utilization, save time, and reduce costs. They also allow for the creation of optimized timelines for the launch of new products or services. The models developed during the process are intended to help achieve or support established goals.
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The data foundation
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Step 1: Data import

First, all relevant data required for the forecast is imported. This is done from various data sources such as databases, web archives, spreadsheets, or other types of files.
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Step 2: Data preparation
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To ensure the analysis yields valuable results, the imported data is first prepared. This includes cleaning outliers, identifying missing data, and combining various data sources.

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The model

Step 3: Developing the predictive model

Developing a predictive model often involves supervised machine learning methods. Supervised learning is one of two types of machine learning. In this approach, an algorithm is applied to a dataset to identify patterns and make predictions. This so-called training dataset contains input data and corresponding response values. The supervised learning algorithm uses this to build a model capable of predicting response values for new datasets. Consequently, using larger training datasets often results in models with higher predictive power that perform well on new data.

Step 4: Integrating the model into the system

Once a suitable model has been developed using machine learning techniques, it is implemented into the business environment or a production system. This makes the analyses available to other software programs and devices, such as server applications, mobile devices, web applications, and enterprise systems.

This workflow is similar to the iterative process of the CRISP-DM model – the CRoss-Industry Standard Process for Data Mining. This cross-industry model describes the underlying process behind every data analysis project in six phases. The six phases are:
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• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deployment

Within this model, the phases do not run strictly in sequence; instead, they often overlap and repeat.

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The next step: Prescriptive Analytics

For many companies, the goal after successfully implementing predictive analytics is to introduce prescriptive analytics. Complementing the forecasting function of predictive models, prescriptive analytics provide actionable recommendations on how best to respond to specific future events.

An example of prescriptive analysis is determining production and inventory levels to match a forecasted demand. Prescriptive analytics methods make it possible to provide recommendations, such as how much stock individual retail locations should hold to respond efficiently to the corresponding forecast.

Predictive models can therefore be extended beyond simply forecasting events. This allows them to also abstract actions so that these events lead to optimal outcomes.

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Challenges of Advanced Analytics

Traditional BI reporting often merely maps data, visualizing only the current state of affairs. If the dataset is of high quality, the reports are highly likely to be reliable, especially since most modern BI environments are now quite mature and their reporting methods and concepts have reached a high level of development. However, there is no 100 percent guarantee that advanced analytics will always deliver the desired results.
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Today, a multitude of standard algorithms and methods are available for specific use cases, such as customer classification. Finding the most suitable solution for a dataset depends heavily on the skills of the user and the software being used. However, it is also possible for algorithms to fail due to missing or incorrect data. If an advanced analytics process shows that no results can be found, the process should be aborted and the data reprocessed.
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Furthermore, users of advanced analytics should have knowledge of methods for working with probabilities. While classic BI reporting almost always provides the correct figures, business users must interpret the probabilities generated by advanced analytics. For example, the quality of a sales forecast or customer classification must not only be noted and communicated for each individual analysis, but also continuously monitored and optimized.