What is Predictive Analysis
Predictive Analytics is an Advanced Analytics technique that allows you to predict future results based on historical and current data to increase profits and gain a competitive advantage over competitors.
Predictive analytics models use technologies such as machine learning (ML), statistical modeling, and data mining. This type of advanced analysis represents a sort of continuum or evolution of Business Intelligence (BI) towards Business Analytics (BA):
- The starting point is descriptive analysis (what happened and why) developed thanks to BI systems, so it is assumed that the company has already passed the transition to Digital Transformation.
- The next step is predictive analytics (what will happen) which is the first significant step within the BA: it is the basis for implementing automated systems that predict problems and solve them before they even occur.
- Up to the development of prescriptive analysis models (how to react or intervene) in which BA tools not only predict future events, but advise the actions to be taken to prevent them or achieve the desired results.
Why Use Predictive Analytics in Business
Thanks to this advanced analysis technique, companies can predict and anticipate future trends, behaviors and results, transforming this information into strategic and economic value for:
- increase and proliferate their business;
- seize business opportunities before competition;
- minimize the risk of adverse events and repercussions on your business.
Today, predictive analytics has entered companies and marks the gap of the “analytics divide”: who owns the data and “predicts” the future wins the challenges in the global market.
Estimating the probability of future events (or results) based on historical data, has always been the preserve of mathematicians and statisticians, What has made it possible for small and medium-sized enterprises to have access to these technologies has been the combination of some changes in market and technology conditions (both in terms of development and diffusion):
- Availability of data (Big Data): thanks to the Internet of Things, instruments and sensors of always connected surveys, and the richness of the data provided by the company systems (transactions, behaviors, marketing, production data, sales, etc.);
- More accessible (also economically), user friendly technologies, reduced learning curves and higher levels of adoption;
- Increase in computing capacity;
- Possibility to analyze unstructured data (texts, images, videos, etc.).
This puts in the hands of company management tools with great potential.
Applications of Predictive Analytics
Predictive analytics is applicable in all business areas and industrial sectors. Some examples of use are:
- Fraud detection: thanks to Anomaly Detection algorithms it is possible to identify anomalous behaviors and take preventive action.
- Risk reduction: profit in the banking, insurance and corporate credit sectors thanks to the solvency score (e.g. credit score).
- Forecasted debt recovery: predictive indicators indicate in advance any anomalies in the timing of liquidation by creditors.
- Optimization marketing campaigns: identify paths of behavior, promote up-selling and cross-selling actions, retain customers, increase the satisfaction rate and customer retention.
- Predictive Maintenance: Prevent machine failures and downtime on production systems and machinery by avoiding production downtime and delays.
- Transport optimization: real-time control of the state of transport on the territory, cost reduction, optimization of transport routes, saturation of vehicles, optimization of the fleet, improvement of quality (e.g. constant temperature control in drug transport).
- Logistics optimization: align order, sales and warehouse data with future production and supply forecasts (e.g. avoid stock out phenomena).
- Optimization of production and operations: In manufacturing as well as hospitality, predict turnout/demand and optimize resources to maximize profits.
- IT infrastructure management: predict and resolve the occurrence of errors and failures, sometimes by adopting autonomous prescriptive systems.
- Demand analysis: in relation to recurring or exceptional events.
Some sectors that have benefited most from Predictive Analytics are that of Energy (estimate production and plan mining according to demand and other factors capable of conditioning the market), Insuranceand Banking (for risk estimation and calculation of insurance premiums), Manufacturing, Hospitality, Retail and GDO, Luxury & Fashion to anticipate new trends, predict sales and production estimates based on geo-territorial data (turnout, travel times, etc.)the Pharma and Life Science sectors, which could react in a timely manner to impactful and unpredictable events such as Covid-19.
How Business Predictive Analytics Works
There are two types of predictive models that are useful for business analytics:
- classification models (discrimination between two states: 0 and 1)
- regression models (generate a numerical result)
These models make use of several predictive modelling techniques:
- Decision trees: ideal for representing path flows in the presence of alternatives (e.g. a decision-making process);
- Regression: estimates the relationship between variables (e.g. effects of certain factors on demand for a given asset or for estimating bank credit risk);
- Neural networks: techniques suitable for modeling complex relationships;
- Machine Learning: the model learns and evolves independently, without being reprogrammed;
- and many others.
Techniques and predictive models are developed ad hoc on business/ company issues, presuppose the presence of a BI system, a data warehouse with clean and certified data and a team of people with business management skills, data management, modelling and predictive algorithms.
AI and ML processes in predictive analytics models
Business Intelligence and Predictive Analytics perform different functions within the same process: AI features allow you to combine BI and Advanced Analytics analysis in the same tool/ solution.
Simplifying a Machine Learning system to the extreme, we could summarize it as follows:
- Data collection operations in a data warehouse;
- Creation of a ML model/algorithm to investigate a question;
- Data processing: software that works externally and returns enriched data with more meaning to dwa, where they are again available for reading/analysis or to power other models.
All in a continuous process, which feeds itself and is enriched with new information independently.
How to do predictive analytics in the company?
To implement predictive analytics models, or Advanced Analytics in general, in your company you need:
- High volume of available data (predictive analysis models are all the more reliable the larger the database);
- Ability to integrate heterogeneous sources (structured and unstructured data).
In our experience, to implement BI and predictive analytics solutions, some preliminary steps are essential:
- Implement a real business digital transformation;
- Generate a cultural paradigm shift towards data-driven processes.
Blue BI, as partner of Business Intelligence & Analytics, offers a Laboratory path to better understand the current situation and therefore the possibility and modalities of insertion of ML processes within its own reality.