When we explain our consultancy in mathematical optimization, we are usually asked where our solution fits in terms of technology. Actually, that is a good question. If we are talking about an Industry 4.0 solution, we are necessarily talking about something absolutely new, right? Of course not! Many technologies that are trending were actually developed a long time ago and are just more accessible, more developed, or simply more disseminated today.
Many of those "trendy words" that we always hear in conversations about industry 4.0 are concepts, sciences, or tools. Mathematical optimization is a tool that is part of the Advanced Analytics concept and can be used along with other tools such as data warehouses, artificial intelligence, big data, databases… Do you see what I mean? They can work together! – and it is essential to be aware of that. We discussed these ideas and the problem of choosing technologies instead of solutions in a previous article called "technology-oriented projects".
People usually get confused about the different concepts and sciences so, to end all questions once and for all, we prepared a short dictionary for these "trendy words". Share it with your colleagues and if after reading this post you are still confused, please, contact us and we can clarify.
Advanced Analytics represents a collection of techniques and tools used to model internal and external data to yield valuable insights that can drive business-improving actions. It wields the power to drive deeper, more strategic, and more actionable insights from your data than traditional BI reporting.
Gartner defines advanced analytics platforms as capable of providing an end-to-end environment for model development and implementation. These platforms must include (1) access to data from multiple sources; (2) data preparation, exploration and visualization; (3) the ability to deploy models and integrate them with business processes and applications; (4) platform, project, and management model performance capabilities; and (5) high-performance scalability for development and deployment.
Advanced Analytics can be used in different areas such as technology, marketing, risk analysis, and operations, for example. Decision-making becomes faster and based on relevant information.
From the technologies we use daily to industrial strategic control systems, Artificial Intelligence is a concept that belongs to computing and is the ability that machines (or software and other systems) have to interpret external data, learn from this interpretation and use the learning to troubleshoot specific tasks and achieve specific goals.
Artificial Intelligence makes machines think like humans so that they can analyze, reason, learn and decide logically and rationally. To succeed in this entire process, it is necessary to combine different technologies such as data modeling, big data, and processing power.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sourcing. All of these in real-time.
Although information is centralized and analyzed in a single place, it can come from different internal and external sources, such as market analysis, social networks, electronic devices, internal processes or even surveys in offline media. Data arrives in an unstructured format and works with highly complex algorithms capable of grouping and relating this data.
With this immense volume of data, IT and business areas use Business Intelligence (BI) to organize data and perform analysis both efficiently and quickly in order to identify patterns and predict trends with greater accuracy.
BUSINESS INTELLIGENCE (BI)
Business Intelligence is a term used to define the internal analysis of the business and the market in which it is inserted, through structured data that the company has (tables, performance reports, dashboards) with the help of software. With the data organized and analyzed in an explanatory manner, decision-making becomes more strategic and assertive.
Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data using, for instance, BI reporting.
A systematized collection of data stored in a computer system that can be easily accessed and/or manipulated by a data-processing system for a specific purpose. It is commonly used with CRM.
A datacenter (DC) is an environment designed to house servers and other components such as data storage systems and network assets.
Data-Driven is a methodology applied in companies that base decisions and strategic planning on the collection and analysis of information. These companies have data-driven organizational processes and tools to apply this methodology.
The data science culture that decisions are made based only on intuition or experience does not apply. Data collection and crosschecking tools enable organizations to be more accurate and more capable of seizing opportunities, as well as to anticipate trends and issues. After all, the panorama of internal and external factors to the organization becomes clearer.
Data mining is when information is processed with Artificial Intelligence methods to find patterns that can be useful for specific goals.
It is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining, machine learning and big data.
Data Technology or DataTech (DT) is a sector that uses big data analysis, Artificial Intelligence and machine learning algorithms for data management. This sector includes many solutions that process digital information, such as IoT (Internet of things) and Data Consulting. Data Technology can be used to manage growing data streams, discover insights or find solutions to integrate various data sources.
It is a system that works as a robust Database to centralize data taken from different sources; storing and organizing it. The Data Warehouse supports the work of Advanced Analytics, querying historical data to obtain insights and aid in decision-making.
This data is obtained from various sources, which create an organizational history, such as spreadsheets, ERPs, CRM, among others. As it is fed by reliable sources, it is most often considered the organization's main source of information.
It is a deepening of Machine Learning. However, with the ability to learn more complex systems and provide even more accurate results.
Deep Learning uses complex neural networks, which are inspired in the connection between neurons in the human brain.
Combined with advances in computational power, the system learns complex patterns and interprets large amounts of data.
One of the most common applications of Deep Learning is in image and speech recognition. It is also used in the development of autonomous vehicles.
It is one of the technologies used by AI to achieve the expected results. This methodology makes systems capable of learning partially or fully autonomously from large volumes of data.
That is done by processing data and identifying patterns, which makes it possible to make decisions based on experience, without the need for the system to be programmed to reach a certain conclusion.
The so-called Artificial Neural Networks (ANNs) are interconnected computing systems that work much like neurons in the human brain. Using algorithms, the collection of these artificial neurons can recognize hidden patterns and correlations in raw data, cluster and classify it, and continuously learn and improve.
A large artificial neural network can have hundreds or thousands of processing units. Computational techniques present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience.
Most neural network models have some training rule, where the weights of their connections are adjusted according to the standards presented. In other words, they learn from example. There are different kinds of deep neural networks – and each has both advantages and disadvantages, depending on their use.
Industries have used these technologies to make the so-called digital transformation and provide competitive advantage. The most common applications are for scenario simulations, predictability analysis, automation, data interpretation, investment analysis and many others. So, after reading this, have you had any insight on solutions applicable to your business? Always look for a specialized company or consultancy to diagnose the best solution and ensure implementation that will actually generate results and improvements for the organization.
Vinícius Mello - Consultant at Cassotis Consulting
Fabio Silva - Senior Manager at Cassotis Consulting