Many things would be easier to plan by predicting the future. This is a very tempting idea, particularly for companies. With predictive analytics, this is, in a sense, no longer wishful thinking, but can be implemented in reality at least to a limited extent.
But what exactly is predictive analytics? How does it all work? And in which areas can I really use it successfully? Today, we will try to give you an overview of all these questions in this blog entry.
What is predictive analytics?
Predictive analytics is an analysis method that uses historical data to make predictions about how a situation will or may develop in the future. Based on this historical data and using various statistical analysis techniques, mathematical, predictive models are created which, for example, calculate a numeric value for the probability of a specific event occurring. These models are then applied to current company data in order to make predictions about activities, behavior and trends and thus optimize company results in the future.
The method is by no means new, but it is becoming increasingly popular as more and more company data is being generated and available as a result of digitization. In addition, great progress has been made in areas associated with this, such as big data and machine learning. Of course, companies want to use this data intelligently to predict complex economic relationships and make better decisions accordingly. This is how you hope to gain a competitive advantage.
Predictive analytics: Classification of the term
Predictive analytics is often used in conjunction with business intelligence, business analytics, big data, and data mining. But what do all these terms mean and how are they related?
Predictive analytics is a subset of Business Intelligence (BI) and Business Analytics (BA), which are often used interchangeably. Strictly speaking, however, BA is a more advanced stage of BI evolution. BI is often used to describe all forms of data analysis in companies. BI enables companies to answer questions about the current economic situation by systematically collecting, evaluating and presenting company data in order to make better operational or strategic decisions.
And what does that have to do with big data and data mining?
In summary, big data simply describes the vast sea of structured and unstructured corporate data that is generated and that needs to be made use of. Big data therefore provides large amounts of data and, if necessary, the technical platforms also efficiently process it. It therefore provides the basis for predictive analytics. On the other hand, data mining involves the actual gain of insights from existing data and is often used synonymously with predictive analytics. Data mining therefore aims to read patterns from large amounts of data using statistical methods and using artificial intelligence (AI) and to identify relationships in order to ultimately make predictive analytics possible.
You could also illustrate the whole thing as follows: To cook a delicious dish, several things are necessary. On the one hand, you need the right utensils, such as ingredients (data) and the corresponding kitchen (suitable technical platform). Without the right recipe (data mining), however, it is highly likely that the result would be an inedible dish. With the right recipe, however, the ingredients can be used in such a way that an excellent meal would be the end result.
Predictive analytics methods
As already mentioned, there are several methods to successfully implement and implement predictive analytics. Machine learning techniques are used here to find valuable patterns in data and to create models that in turn predict future results. Some of these techniques are listed and briefly explained below.
1. Regression analysis
Regression analysis is probably the oldest and best-known method of predictive analysis. Regression analysis is used to determine the relationship between a dependent (e.g. purchase decision yes/no) and one or more independent variables (e.g. age or expected benefit of a product). A distinction is often made between logistic and linear regression. The main difference here is the quality of the dependent variables. While in linear regression analysis, the dependent variable is a continuous variable (e.g. age, height), in logistic regression it is a categorical variable (e.g. gender).
2. Time series models
Time series models are a special form of regression analysis. As the name suggests, the data is viewed over a specific period of time, which is why it is particularly suitable for forecasts.
3. Decision trees
Decision trees are a defined representation of decision rules and are always presented in the form of one or more tree diagrams. The decision tree consists of several nodes that divide the incoming data into two or more subgroups. Each of these nodes is characterized by a decision rule in the form of an if-then condition, which checks and differentiates between new input data. The decision tree is characterized by the gradual and ever smaller distribution of the initial total of data. In the end, all data records must end in one of the end nodes, which means that they must be clearly assignable to a node by running through the various nodes and then rules.
4. Cluster analysis
Cluster analysis is used to divide huge amounts of data into smaller homogeneous groups. In this case, the groups are formed from phenomena with the same characteristics. The characteristics between the groups should differ as much as possible.
A clustering algorithm called “k-Nearest Neighbor” is often chosen for this purpose. In principle, this means nothing more than using the nearest neighbors (nearby points) for each new case or data point considered to decide which group the new data point is assigned to. The group that occurs most frequently among the k neighbors becomes the group to which the new data point belongs. The value k only describes the number of reference data points that are taken into account in the assessment. As is often the case, this should be selected neither too small nor too large.
5. Neural networks
Neural networks are the combination of artificial neurons that are based on the human brain. A neural network basically consists of at least two layers. The first layer is the input layer. It consists of neurons that receive the inputs. The second layer is the starting layer. Depending on the design, there is one or more hidden layers in between, the so-called “hidden layers”. With their activity levels, the contained neurons indicate the results of the neural network. Accordingly, in addition to neurons, the connection between these individual neurons plays a very important role, as they are weighted. They are therefore given a different meaning. Here, a distinction is made between
- No weighting at all, which means the neurons have no influence on each other at all.
- a positive weighting, which means that neurons have a positive influence on each other. If the value of one increases, it increases the value of the other.
- a negative weighting, which means the neurons have a negative influence on each other. If the value of one increases, this decreases the value of the other.
6. Naive Bayes method
Naive Bayes methods are based on the well-known Bayesian formula for conditional probabilities. For each class, the probability that an object belongs to that specific class is estimated. In the next step, the class with the highest probability for predicting the class of the object is selected. This approach assumes that object properties occur independently of each other within classes.
7. Support Vector Machines (SVM)
SVMs are a nonlinear method for data analysis. The SVM model consists of a certain number of objects in a room, which are grouped in such a way that they are clearly separated from each other by a dividing line. The aim of the algorithm is to group in such a way that the distance between the groups is as large as possible. New objects are thus placed in the existing model by determining which group they can be assigned to.
Predictive analytics use cases
Predictive analytics is used in many areas. Predictive analytics is frequently used, particularly in areas such as marketing, finance and insurance, or retail. But industries such as healthcare are also increasingly using this type of data evaluation.
A specific example of business use is
- For example, analyzing customer behavior to determine buying behavior. In this way, online advertising can also be targeted in a more targeted manner.
- For example, forecasting electricity prices and requirements in the energy sector.
- For example, the detection of imminent part failures of industrial plants. This field is also known as predictive maintenance. Applications for condition monitoring and predictive maintenance are used here. In this way, downtime can be reduced and waste minimized, which ultimately leads to an enormous cost reduction for companies.
- Can also be found in medicine, for example. Here, specific disease patterns can be identified at an early stage by using pattern recognition algorithms. On the other hand, patients could be identified who are at risk of developing certain diseases.
- For example, the development of credit risk models in the financial sector to predict credit risks.
- For example, analyzing sensor data from connected vehicles in the automotive sector in order to create driver assistance algorithms.
A typical predictive analytics process at a glance
Basically, the predictive analytics process or workflow can be divided into a rough structure and various sub-items, which are typically carried out in a certain order.
- Data Access and Exploration: Importing data from various data sources (e.g. web archives, databases, etc.)
- Preprocesing of data: The cleansing of data by systematically removing outliers and combining the various data sources (data aggregation)
- Development of Predictive Model:
- Development of an accurate predictive model based on aggregated data using various statistical methods and predictive analytics methods
- Test the model with a test data set to verify and ensure its accuracy
- Integrate Analytics with Systems: Integrate the best model into a production environment
Conclusion
Basically, predictive analytics is a continuous, iterative process whose use leads to ever better or more accurate forecasts — provided that the necessary data basis is available. It is therefore a promising way to profitably evaluate data for companies in order to derive processes from it and make decisions that lead to future business optimization.
Have we sparked your interest in predictive analytics? Would you like to integrate predictive analytics into your business processes in the future? We can help you with that. Just contact us by email at info@pacemaker.ai