The theoretical foundations of Artificial Intelligence (AI) were created over 50 years ago, but its business potential has been used only recently. This is possible due to the development of information technologies and the fact that the cost of mass storage and computing power has decreased over the years.
Every day, business generates terabytes of data, from point-of-sale transactions to online and offline customer journey tracking. The omnichannel approach provides extremely accurate data on customers’ buying behavior and preferences. However, the data does not have any business value in itself. Moreover, its quantity and dispersion makes finding actual data-related business benefits a challenge. AI helps companies to identify patterns and trends, as well as cause-and-effect relationships that translate into concrete business effects. This allows making better just-in-time decisions. The proper use of data leads to success and enables building a real competitive advantage in a dynamically changing world with increasingly demanding customers.
We develop advanced statistical models based on Machine Learning and Data Science.
AI is a combination of mathematics and computer science. Machine Learning joins statistical knowledge with programming, data quality analysis and model tuning. It consists in modelling complex business events using automated algorithms that can be customized and taught on the basis of updated historical data.
At 3Soft, we place great emphasis on the business perspective of Data Science teams, so that the models developed are closely tailored to the business realities of a given sector and a given customer. Data Science specialists cooperate with the customer, analyzing the availability, quality and business value of their data. Statistical hypotheses are later verified in terms of the selection of appropriate statistical methods and the results obtained. In the next step, the developed statistical models are tuned for automatic operation and the required performance.
Data mining, machine learning and deep learning are the cornerstones of AI. We offer a wide range of AI solutions by combining different statistical methods in a single model, which allows us to meet high business demands by achieving high-quality results.
Forecasting and prediction
Predicting business events requires a sophisticated approach to data analysis and advanced statistical methods. The aim is to answer the question “What will happen?” through analysis of historical data and identification of factors affecting a given phenomenon. Forecasting often involves time series analysis, while prediction is based mainly on cause-and-effect relationships.
Parametric and non-parametric modelling
Whenever, despite the transformation, the distribution of data remains excessively affected and strongly asymmetrical, non-parametric equivalents of statistical modelling should be used. Non-parametric methods, i.e. methods free from distributional assumptions, are also a reasonable alternative in the case of a small amount of data describing a given phenomenon or when the available data is only “count data” or “rank data”.
Segmentation and analysis of clusters
These are used to isolate homogeneous sub-sets that are similar in terms of the information provided. Clusters reflect real differences and arise as a result of automatic data analysis, without the a priori assumption of the rules for division. An example is the precise definition of groups of customers or products that require adopting different approaches.
Probability modelling allows including uncertainty in the analysis of a given phenomenon. When a phenomenon is deterministic in theory, but is not known in detail, probability modelling can be useful. Examples of methods used include Bayesian networks, probabilistic graphic models or Markov models.
It is used to find interdependencies and relationships between several variables simultaneously. It allows studying the internal or hidden structures of data sets and predicts how a change in some factors will affect others. Multivariable analysis is also used when it is necessary to reduce the number of dimensions of the analyzed attributes.
The objective of descriptive statistics is the analysis of the past in order to draw some basic conclusions and find generalizations about the analyzed phenomena. Descriptive statistics are mostly used as the first and basic step in data analysis. In practice, tens or even hundreds of statistical measures are calculated.
Additionally, we offer solutions in the field of Management Science, including:
Linear and non-linear programming
This involves a mathematical model used in the context of optimizing the decision-making process (e.g. profit maximization, cost minimization). This approach consists in defining linear/non-linear functions, including objective functions, decision variables and limiting conditions that may arise in the course of achieving a goal. Linear and non-linear programming is used to make quantitative decisions, including planning and solving logistic problems.
A technique which aims to analyze how independent variables affect a dependent variable under specific conditions. It also helps to explain how a selected parameter will change with other parameters unchanged, so that the overall solution remains optimal.
Queue modelling makes it possible to manage queuing systems in the most efficient way. It allows predicting the length and duration of queues and optimizes the resources used to handle processes. The queue theory is widely used in industry to minimize losses caused by consecutively occurring events.
Artificial Intelligence makes it possible to carry out automatic analysis of large data sets by identifying hidden patterns and dynamics of changes, as well as providing a precise (quantitative) description of a given issue. The range of AI applications in business is wide – from technical and manufacturing systems, through medicine, transport, to sales and marketing. The benefits include:
Identifying business knowledge hidden in data through statistical analysis
Precise description of phenomena and their variability and timeliness of conclusions drawn
Automatic analysis of large data sets in a short time using AI