Many leading tech-companies have implemented advanced analytics in their digitalization strategy, but what does it mean and how does it work?
In general, advanced analytics is a comprehensive term and refers to a wide range of analytics techniques that are intended to give companies a greater insight to their data. In other words, it is an umbrella term for analytics techniques such as machine learning, artificial neural networks, data mining, predictive analytics and big data analytics.
We have researched a bit on the different analytical techniques and below is an overview of some of the various methods.
Machine learning is an artificial intelligence (AI) discipline directed towards the technological development of human knowledge. It is challenging though, to replicate human intuition into a machine, mainly because human beings often learn and execute decisions unconsciously. However, it is possible with extended training periods when developing broad algorithms directed toward the transcription of future behavior.
Machine Learning includes a study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, but instead are relying on patterns and inference. It is seen as a subcategory of artificial intelligence artificial. The algorithms in machine learning are grounded on sample data which is also known as “training data”. Through the data the algorithm builds a mathematical model in which it is possible for the machine to make predictions without being explicitly programmed to perform the task. Hence, machine learning facilitates the incessant analytical advancement of computing, through coverage of new scenarios, testing and adaptation, where patterns and trend discoveries improve the decisions in subsequent situations.
In order to perform the extended training periods, machine learning requires various training techniques including rote learning, parameter adjustment, macro-operators, explanation-based learning, clustering, mistake correction, multiple model management, reinforcement learning and genetic algorithms.
Consequently, machine learning enables computers to handle new situations based on analysis, self-training, observation and experience. Nonetheless, machine learning is often confused with data mining and knowledge discovery in databases (KDD), which share a similar methodology.
An artificial neuron network (ANN) is a computational model based on and inspired by the structure and functions of biological neural networks in animal and human brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Information that flows through the network influences the structure of the ANN because the neural network changes and progresses through learning based on these inputs and outputs. The ANN’s are perceived as nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled and as a result patterns are found.
Data mining is the process of analyzing and discovering hidden patterns in the data. Data mining is a loosely defined concept, but it basically refers to exploring data in order to find systematic patterns and trends. The purpose of data mining is data reduction, where complex data is reduced to something more simple and more interpretable. Data mining covers a wide range of methods and techniques, but the essential principles are almost always the same. In short, the principle is that units similar to the each other of observed conditions should be grouped into the same general groups, while units that are not similar should be grouped into different general groups.
Data mining can, in distinction to machine learning, be approached in two forms:
1 – supervised: where you have an idea of how the underlying patterns should look like (hypothesis testing)
2 – unsupervised: where you have no idea about how the patterns should look like (exploratory speech analysis)
Data mining is also known as data discovery or knowledge discovery. The patterns are according to different perspectives for categorization made into useful information, which is collected and assembled in common areas, such as data warehouses. Data mining is therefore an efficient analysis technique that facilitates business decision making, or cluster analyzes of different groups of people (ex. voters in political elections).
Big Data analytics
Big Data analytics refers to the strategy of analyzing large volumes of data, which is called Big Data. This Big Data is gathered from a widespread variety of sources though scraping data from social networks, videos, digital images, sensors, and sales transaction records. The intention of analyzing all this data, is to uncover patterns and connections that might otherwise be invisible. The uncovering of data might provide valuable insights about group of people who are analyzed. The method is therefore used in various areas and for different reasons, such as to analyze the user in business related perspective or an analysis of individuals or groups in social science research.
Because of digitalization and the increase of technology in our society, it is know possible to gather these huge data sets on the internet, where traditional quantitative surveys are outdated and may fall short because they’re unable to analyze as many data sources. Moreover, the question of representation of the individuals as an indicator of the population, is very unreliable in the classical surveys. The collection of Big Data is unbiased and is therefore much more reliable because almost everybody is online in our days.
The use of advanced analytics thus enables digital transformation and allows new insights for companies or social science research that otherwise is not possible.
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