What does predictive analytics mean?

It uses data from the past to predict future developments. A mathematical model is developed that contains the most important trends. Predictive analytics are used in the following areas, among others:

Finance
Meteorology
Security
Economy
Insurance
Logistics
Mobility
Marketing

Through predictive analytics, economic correlations are intended to facilitate management’s decisions for the future. Predictive analytics is based on business analytics, discussed in the next section.

The predictive analytics technique uses data from data mining for its predictive models. Assessments and empirical values from the past also flow into the analysis. The information used is extracted with the help of ETL processes. Predictive patterns are then calculated from this. ETL processes are data flow processes in a data warehouse or similar system. In an ETL process, data is pulled from various sources, converted, and imported into a target system. However, the predictive analysis also uses other methods, such as machine learning, elements from game theory, or various simulation methods.

Variables from past events and their effects, as well as other dependencies from data sets, which impact future events, flow into the data of the prediction models. How meaningful these predictions depend on the quality of the assumptions made. Classification is of crucial importance for predictive analytics. Type involves different grouping of objects with their respective characteristics into classes. Through variety, it is possible to create a classification system applied in various fields, such as science and information technology. In the context of predictive analysis, classification causes data to be extracted according to predefined rules. Subsequently, search words are defined for the document search. With the help of semantic analysis, the document search can be carried out even more precisely.

It is possible, for example, with predictive analysis to fine-tune marketing campaigns. In this way, website visitors’ behavior can be optimized in terms of click-through rate and conversion rate. The click-through rate (CTR) indicates the percentage of how many visitors clicked on an advertisement displayed to them. On the other hand, the conversion rate suggests in percent how many visitors to a website complete an action, for example, a purchase process in an online shop, as opposed to visitors who abandon this process.

Predictive analytics has become abundantly crucial in recent years as the technologies that support it have made great strides. This is especially true in the two areas of Big Data and Machine Learning. For large data sets from which analyses and conclusions for future events are to be drawn, the method of predictive analysis is best suited.

What questions are answered by predictive analysis?

Questions about what happened: What happened when?
Questions about the quantity of an event
Questions about the frequency of an event
Questions about the causes of an event

To answer these questions, various tools are used, such as reporting (KPIs and metrics), automated monitoring (an alarm is triggered when a specific value is exceeded or undershot), dashboards, ad hoc queries, and online analytical processing (OLAP).

In online analytical processing, hypotheses are made, and information is requested to confirm or reject the previous assumption.

How can business analytics be used?

Business analytics relies on statistical analyses of company data to look at future events. In doing so, business analytics answers the following questions:

Reasons of events
Effects of events
Interactions of events
Consequences of events

Scenarios are also run through, and alternatives are shown to how one can act differently. Which parameters must be adjusted to achieve this or that result or event?

Wich analysis tools does business analytics use?

To improve the planning process, Business Analytics uses various analytical tools:

A/B testing: different variants with different variables are tested to check decisions.

Statistical or quantitative analysis is used to answer why a particular event did or did not occur.

New patterns and correlations in data are discovered through data mining.

Future events are predicted through predictive analytics. Predictive analytics is thus a sub-discipline of business analytics.

What are the procedures in predictive analysis?

Defining the project: The project’s outcomes need to be determined, how much effort will be required, business objectives, and what data will be used.

Collecting the data: Data mining provides data from different sources for predictive analysis. This provides an overview of customer interactions.

Analysis: Analysing the data allows review, cleaning, and modeling. This filters out the valuable information that is then needed for further processing.

Statistical analysis: It is possible to support or confirm the assumptions and hypotheses made previously. In addition, this is verified by standard models.

Modeling: Predictive modeling is used to create predictive models for future events.

Predictive deployment: Predictive model provisioning is used to ensure that analysis results can be incorporated into the decision-making process daily.

Monitoring the models: The models are managed and monitored to check whether the expected results were ultimately achieved.

What types of models are there?

Different models can be used in the predictive analysis:

Predictive models:These models use mathematics and computer science methods so that results or events can be predicted. Changing the model inputs indicates an outcome of a future state. The model is fed with training data sets and tested in an ongoing process. Then it is checked how accurate the predictions are. For this purpose, different machine learning approaches are used to determine the most suitable model. The training samples are the available sample units whose properties are known. Pieces that contain known properties but unknown performance are referred to as training samples.
Descriptive models:Descriptive models provide data to group customers and prospects into different classes. Predictive models always refer to the prediction of single customer behavior. On the other hand, explanatory models always refer to several different relationships between customers and products. Customers are classified in descriptive models according to their preferences and life stage.
Decision models: These models show the relationship of the totality of elements of a decision. These include known data (including data from predictive models) and forecast decision results. As a result, the outcomes from these decisions are predicted with different variables. With the help of these findings, effects can now be optimized. Decision models are used when a decision logic or business rules are to be developed to lead to the desired response in any customer or circumstance. Because real-world decision problems are complex, the model usually needs to be simplified. One way to streamline the model is not to include all possible variations of the data in the model

Predictive analytics is currently one of the most critical trends for Big Data.

Big Data is about vast amounts of data. These are measured in zettabytes or even yottabytes. Big Data is used to collect, analyze and evaluate data to be marketed for products and services. Big Data is also used for monitoring purposes.

Big Data Analytics analyses the usage behavior of interested parties from a large pool of data. In addition, movement profiles are created, or exact forecasts are made that calculate information usage and purchasing behavior. Mass data are analyzed and brought into connection with each other.

The three terms Volume, Variety, and Velocity, also referred to as the 3Vs, describe the characteristics of processing vast amounts of data. Since large memory or mass storage has become relatively cheap in this day and age, large amounts of data can be stored in the course of Big Data. Whether booking and payment transactions, driving routes of vehicles, patient data, data from social networks such as Xing, Facebook, LinkedIn, etc.: every conceivable type of data that can be stored offers excellent potential to collect personal data.

The trend of Big Data can be seen in the marketing of personal data. This brings us back to predictive analytics. With the help of personal data from Big Data, future forecasts and events can be developed.
Big Data can be used to:
- Make predictions for future elections
- Determine predicted energy needs
- Determine survival data from specific therapies
- In the fight against crime, to identify locations and times where a crime is most the probability that a crime will take place
- It is finding out if a particular product can be sold. A company can save a lot, as supply and warehousing can be optimized using Big Data
Fast data is when big data can be analyzed and processed in almost real-time without long delays.
Business analytics (BA) is a method used to manage a company. Business transactions that have been made are analyzed, processed for statistics, and evaluated. Conclusions are then drawn to inform future decisions and courses of action.

Business processes are evaluated analytically to see why a business transaction went the way it did. In addition, the decisions that led to that business process are analyzed and looked at to see how they can be improved.

In addition to business analytics, there is also business intelligence (BI). Business Intelligence is a concept applied company-wide to manage the access, analysis, and reporting of a company’s stored data. For a proper BI solution, the most important key figures of the company are needed, as these have to be determined and documented. Knowledge about the structure of data streams and data quality analysis is also necessary. Business areas affected by the analysis process must be converted to the data warehouse.
Data mining is a process in which the relevant information for the company is filtered out of a vast data stock.

The filtered and stored data is examined for correlations relevant to the topic. Through the exported, filtered, and summarised data, it is possible to gain insights from hidden patterns and data related to each other, which are relevant for production, marketing, and sales. In addition, data mining can be used to conclude future events. Path analyses can be used to identify links that have been gained from the exported data.

There are two methods in data mining: validating the data and deriving hypotheses from the data. Data mining can draw connections from the data taken from the data warehouse. This makes it possible, for example, to determine the holiday frequency of specific population groups. It is also possible, for example, to relate the purchasing behavior of customers for certain products to one another. The classification and analysis of vast amounts of data are called big data analytics.
A data warehouse is used to manage and control business processes. Customer-relevant information serves as the starting point for a value chain oriented towards the customer. Therefore, customer data is obtained to derive conclusions from it regarding the market and the customers themselves. This knowledge can be used, for example, to develop services further.

Data warehousing is the cornerstone for management to conduct analyses and make decisions. Data warehousing is process-oriented, factual, and subject-related, thus not directly dependent on any source. It has a temporal reference and can be integrated and reproduced across the entire company. The data warehouse has certain similarities with the OSI reference model, as it also has a six-layer model.

In the first layer of the data warehouse, the feedback layer, the sources of the data warehouse are described. This layer contains communication, publication, workflow, and the web. The data warehouse infrastructure is defined in layers two (information generation), three (middleware), and four (data warehouse).

The data is calculated, summarised, analyzed, modeled, and visualized in layer two.

In layers three and four, the data is stored and managed.

Layer five is called the data access layer and contains only analytical processing, data mining, and data visualization.

The top layer six is the operational layer. In this layer, the process of quality assurance, the presentation of the result, and the delivery of the information via both internal and external sources occur.