Deliveries of online orders within 2 hours. Forecasts of flight times accurate to the minute. Identification of genetic problems based on reactions to drugs or diseases.
The possibilities of data science are extremely diverse. As a result of changing work and consumer patterns, new digital technologies and value chains, data science has become a necessity in many companies. Data scientists are more in demand than ever. It is therefore worth taking a look at which questions Data Scientists answer and what value they actually provide for the company and its stakeholders.
On a basic level, data science deals with the analysis, interpretation and utilization of data from various sources. Within this field of data processing the following four questions form core responsibilities:
1. What has happened?
Data is the documentation of facts or processes such as business processes, states or results of a program. It is therefore important to first understand what the data shows and what conditions are recorded. On the one hand, this requires a certain understanding of the context, the problems, the company and the target states. On the other hand, the data scientist has to implement certain steps, such as the ETL process, on a technical level. Within the ETL process, the data must be extracted, integrated and transformed from various sources, such as IT systems or databases. This means that Data Scientists first collect all relevant data together and transform it into a uniform and analyzable form. In the next step, they load the transformed data into one or more tools, such as Microsoft Power BI or IBM Cognos Analytics, for data analysis. These tools can then be used to answer what has happened. For example, are there any deviations from the target state? Bottlenecks or delays? Are there any previously undiscovered weak points? The next question arises directly from these questions.
2. Why did this happen?
As soon as the Data Scientists have gained an impression of the current situation and identified any anomalies, the intuitive question arises: Why? Why did this state or event occur? Depending on the context, different techniques are used to answer this and the previous question. Explorative data analyses for instance, which are a branch of statistics and are used in particular when there is a lack of knowledge about the data and its connections. For process data, i.e. data based on a process, process mining methods will also be used. The techniques “Process Discovery” and “Target-actual comparison” visualize the real process and identify unwanted deviations.
3. What will happen?
This is one of the most important questions for a company’s stakeholders. In order to answer this question, data scientists try to make as precise forecasts as possible on the basis of historical data. The aim is to predict what will happen when and how. Artificial intelligence methods, such as machine learning or deep learning, are usually used to generate this information. Machine learning is used to identify patterns or rules in the data that form the basis for predictions. We encounter such predictions every day in our professional and private lives: from weather forecasts, to expected travel times on public transport, to estimated reading times of blog posts or expected stock prices.
4. What is the best thing that can happen?
Finally, Data Scientists answer what is the best condition to realize. This goes hand in hand with the question of how this can be achieved. Here, too, artificial intelligence is often used. This enables Data Scientists to give explicit and data-based recommendations for action to stakeholders. For example, to avoid bottlenecks, time delays or to optimize processes.
Data Scientists must therefore not only deal with large databases, but also apply statistical methods and data mining techniques to answer specific questions. At the same time, they need a certain understanding of industry-, company- or process-specific relationships. Knowledge from industrial engineering is particularly relevant for data processing.
Why is data science so important for companies?
Data scientists transform data into relevant business information. They are able to analyze and explain past business transactions and processes. They can use historical data to make accurate forecasts. This not only creates transparency, but also identifies optimization potential and possible strategies for its realization.
Data analysis as a springboard to business transformation
The great potential of applied data science is obvious. Now it is time to realize the added value in your company. Process Mining takes data analysis to the next level. That’s why more and more companies are opting for LANA.