The Data Science process includes data capture, storage, analysis and exploitation.
TREELOGIC TEAM| 16/04/2019
Data is a very important source of value for companies. The ability to turn the information gathered into a valuable resource is synonymous with having a competitive advantage.
Obtaining the vast amount of types and sources of data, their optimal management and subsequent analysis help any company to better control its costs, increase efficiency and identify new business opportunities.
The concept of Data Science encompasses many disciplines: statistics, computing, analytics, dashboard design, mathematics and new methods of data study such as Machine Learning and Deep Learning. We are therefore faced with an innovative multi-disciplinary field, which thanks to its processes and algorithmic methods is able to process and analyse real situations through the data obtained.
The first step in the life cycle of data science is the collection of as much information as possible. At this initial stage, what is important is both the amount of data that can be collected and its quality, in order to avoid faulty analysis and learning. Depending on the type of data collected, its volume and sampling frequency, the most appropriate ingestion tools will be used according to the type of information source under analysis.
In many situations, data gathering is often made possible by the functionalities offered by the so-called Internet of Things and its ability to interconnect multiple devices, making it possible to generate information from a multitude of different processes.
After all possible data has been extracted, they must be stored.
The next stage involves storing all the information collected for storage in data centres to continue the management process. During this stage, powerful tools are needed to filter out information of value, distinguishing it from that which is not relevant. The data of interest will also be grouped and classified to facilitate subsequent analysis.
The extraction of valuable information is essential to Data Science methodology, as it speeds up the task of analysis by providing a summary of the initial situation and eliminating irrelevant data.
After obtaining the data, filtering it and classifying it, it is time to analyse it. Information processing is the critical point in the life cycle of data science. From this analysis will come the added value that every company seeks to achieve a better position in an increasingly competitive market.
Thanks to the different analytics that can be applied to the data (descriptive, predictive or prescriptive analytics), it is possible to elaborate predictive models, simulators, optimization tools, recommendations, classification, clustering, etc. that transform the original information into valuable results for the organization.
Finally, the results obtained through data analysis can be converted into any type of output that is useful for the organization, such as reports, alerts, dashboards, web services, recommendations, etc. that provide management with the ability to optimize the decision-making processes. This is what is known as Business Intelligence, and it is a concept that gives great flexibility to the organizations that implement it.
As the volume of information contained in Big Data increases, so does the need for people capable of analysing data of different types and nature. Data Scientists oversee converting mere raw data into valuable information for companies, relying on applications, tools and algorithms that fall within the framework of Artificial Intelligence.
One of the advantages of data science is that it is an approach that can be applied to virtually any sector of the economy, business or department. The digitization of society in recent years has led to the constant generation of data, in an infinite number of situations and places, which makes it possible to understand new ways of behaving that were previously unmeasurable. Within a company, the digital transformation linked to Data Science allows optimizing processes in all departments. Thanks to the deep analytical capacity offered by current technology, production and sales teams can anticipate new market trends and competitor moves, offering new products or services that meet the needs of their target market.
In the same way, the marketing department can more finely adjust promotional and advertising initiatives to their targets. Human resources departments also benefit from new talent management techniques; as does manufacturing by means of intelligent factories that improve the manufacturing process; as well as quite logically the transport and logistics industry through the optimization of vehicle fleet management.
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