We live in a time when computers and modern technology are not only widespread, but are a minimum standard. It is difficult to imagine a daily life without a phone in hand and access to the Internet. What is more, managing an organization is no longer possible without the use of modern IT tools and a database. Information and data are crucial in making strategic decisions and planning future activities. However, in order to skillfully use the collected information, the right skills are needed. And it is data science that is the key to optimal data processing, which can be applied successfully at various organizational levels. What can data science do for HR? Read on to find out more.
Data science – table of contents:
What is data science?
Data science is a discipline that combines specialized knowledge, programming skills and knowledge of mathematics, econometrics and statistics. In general, we can say that it is science about data. Using various research methods, algorithms and processes, and based on a large amount of information, it allows the analyst to make significant conclusions and predictions.
Data science is based on special data mining algorithms, machine learning models and artificial intelligence. The task of the algorithms is to properly clean and structure a set of data, and then study the relationships and correlations between them.
Thanks to the advanced methods included in data science, it becomes possible to find hidden patterns that were otherwise impossible to observe. Skillful application of them allows companies to create a strong competitive advantage. The use of data science in an organization can be comprehensive, by looking for new sources of profit, optimizing costs and preventing potential losses.
Data science life cycle
The process that data undergoes is referred to as the data science life cycle. It is usually an iterative process involving repetitive operations and usually consists of six or seven stages:
- Defining the organizational problem, setting goals and planning activities.
- Exploring and preparing data by checking basic properties, detailed identification and problem solving when it comes to reformatting, recoding, grouping and merging.
- Data representation (including those of a special nature, e.g., acoustic data, images) and data transformation involving the implementation and transformation of data into a more “digestible” form such as text files, spreadsheets to SQL and NoSQL databases.
- Computing with data based on data languages such as R and Python, for example. This stage allows running a huge number of tasks in clusters and processing in the cloud, and developing packages that include abstract workflow elements.
- Generative and predictive data modeling. Generative modeling proposes a stochastic model that could generate data and introduce methods to make correct inferences. Predictive modeling relies on methods that make good predictions about certain data pointing to a particular set of data.
- Visualization and presentation of results using histograms and time series charts.
- Building experience based on data science by using frequency data in the system, measuring the effectiveness of standard workflows.
Using data science in HR
The functioning of HR departments is increasingly based on the use of data and its analysis. The most important personnel decisions are made based on Data science reports. However, for this to be possible it is important to understand that Data science is a process, not a one-time activity. That’s why it’s so important to organize and prepare data that will provide a reliable and credible source of analysis.
Well-conducted analysis supports the implementation of the business strategy and builds the credibility of the HR department. Data science is indispensable in such areas as recruitment, employer branding, managing staff turnover, assessing the competency potential of employees and evaluating the management effects of managers.
By combining data from various sources, using appropriate algorithms, it allows companies, for example, to plan where and what kind of employees to look for, what kind of employee to attract to the company, what are the chances of their interest in a new offer and what impact this will have on the business goals being pursued.
Only data science enables such a detailed analysis of human resources, which allows for a better understanding of the needs of employees both at the level of the entire organization, team or individual employee. The results, in the form of reports, determine the proactive management of training programs and increase employee retention, among other things by offering a change of position within the organization. In turn, the possibility for employees to view the reports enables them to shape their own career path and make decisions about their careers.
Data science is used in various industries, sectors and economic fields. It creates real business value, contributes to operational efficiency and reduces errors. It improves customer engagement, streamlines decision-making processes, creates products and builds brands, optimizes sales, and increases the efficiency of human resource management. Regardless of industry and size, organizations that want to maintain their competitive position in the market should effectively develop on the basis of data science and skillfully use the results of analysis.
Read also:The basics of data storytelling.