Every time we interact with a digital device, we produce data. Some of it is important, some less so, and some of it seems irrelevant simply because we don’t recognize its potential. Companies that have relied on Data Scientists have discovered how to transform even marginal data into useful information. This has greatly simplified decision-making processes. Let’s look at what data science is and why it’s indispensable today for accurate forecasting.
What is Data Science?
Data Science arises from the intersection of disciplines that help define the meaning of data. It brings together mathematical, statistical, and computer models. The result is the extraction of meaningful data that, properly monitored, gives us insight into a phenomenon.
“Make your data become advice for your business decisions.”
Antonio Grasso
Here’s a practical example. The production flow of industries implies that there are machines constantly running, the interruption of which could jeopardize the entire process. If equipped with sensors, these machines will produce data that cannot go unnoticed. Such information will, in fact, be indispensable for implementing in-depth predictive maintenance.
How to make the most of the data stream? By relying on a data scientist who will be able to create predictive models for maintenance, thus guaranteeing constant monitoring of the machinery. In this way, production will not have to stop and will also reduce the risk of machinery breakdown. In fact, knowing in advance when I will have to replace a component always translates into time and cost savings.
After the brief parenthesis on the meaning of Data Science, it’s time to show how we can achieve meaningful Data Mining. An operation that allows us to create models check for anomalies and establish connections between Big Data.
Data Science facilitates computer vision, natural language processing, and generative models. How? Through Artificial Intelligence and data mining. Click To Tweet
What is the purpose of the extracted data?
The information we generate every day is collected in databases stored remotely on the PC or in the cloud. If the data are few, we can manage them manually but when they become significant figures it is the case to rely on software or automatic algorithms. So we enter the field of Artificial Intelligence, Machine Learning, and Deep Learning: all technologies that automate the process of data association.
What we can do with data science. Here are some examples:
- Hypothesis. That is, through deep analysis of data over time, with relative comparisons over the years, it is possible to make reliable future predictions about a given phenomenon.
- Classifications. Data science also offers support from an organizational point of view. It helps, in fact, to group patterns or data that respond to a predefined objective. It can be used, for example, to segment the target audience or to create a personalized customer experience. But also, more simply, to define which data to store and which to eliminate.
- Improve business efficiency. Data mining can help in the decision-making process to understand how to optimize processes or discover and identify new goals.
- Monitor. By exploring data and choosing goals to pursue, we can measure results to improve future actions. Or, in predictive maintenance, through data monitoring, we can gain useful information about machinery and the wear and tear on its components.
Who are Data Scientists?
Software, algorithms, and technologies are valuable support in extracting meaningful data. However, let’s not forget that machines and automated processes can only go so far. The real difference in data analysis is made by human ingenuity, namely the Data Scientist.
The Data Scientist is a person who possesses various skills: he or she must understand statistics, mathematics, computer science, programming and must also have good communication skills. Why do we need such a comprehensive figure? Because data is affected by a multitude of variables, some of which are current and some of which are to be predicted. Also, software and algorithms that leverage artificial intelligence need to be trained, and the choice of information to feedback for training can only be made by someone who knows how the process works from a technical standpoint. Finally, a mathematical approach when handling numbers serves to reduce the steps needed to answer a given question.
And where does communication fit in? The study of data often results in reports or tables that will need to be presented to executives in written and oral form or displayed in public. Therefore, the use of appropriate terms and clear, compelling exposition are key elements in getting the results of the research understood.
How can I increase business competitiveness with Data Science?
Here we come to the crucial point: why should I invest in such qualified professionals? What benefit do I get? Data mining, i.e., the association of meaning to a specific piece of data, allows you to obtain strategic advantages that impact increasing performance.
Data Science allows companies to gain insights into Big Data along with three different time frames: present, past, and future.
In fact, data can give me information:
- Descriptive: analytics provide a descriptive statistic of what has already occurred. For example, an analysis of the previous year’s revenue divided by-products. This aspect serves me to quantify the impact of my actions.
- Prescriptive: These are comparative data that allow me to understand what I can improve. How? By analyzing cause/effect relationships. Through the data, the analysis software evaluates possible scenarios of the use of a machine, for example, showing which actions could delay a failure or improve productivity.
- Predictive: we are in the field of statistics and future predictions. In this case, the data should be used to hypothesize actions. Having software in the company that shows the timing of supply of raw materials allows not only to predict an action but also to prevent the block of production. We don’t speak about the certainty of the hypothesis that will have a greater percentage of reliability how much more are the inserted data.
All companies, large or small, can benefit from data analysis. The important thing is to have the right business culture and some familiarity with automation and artificial intelligence technologies and software.
The valuation of the investment depends on the amount of data to be handled. There are analysis tasks that can be done by management software and others that require the expert eye of a Data Scientist.
