Data analytics and data becomes knowledge
Data analytics with the latest tools and methods
Identifying trends with data analytics
Data analytics is the process of examining data sets to draw conclusions about the information they contain, increasingly using specialized systems and software. Data analytics technologies and techniques are widely used in commercial industry to enable companies to make more informed business decisions. As a term, data analytics predominantly refers to a range of applications, from basic Business Intelligence (BI) and reporting to various forms of exploratory data analysis (data mining). Data analytics initiatives can help companies increase revenue, improve operational efficiency, optimize marketing campaigns and customer service, respond more quickly to new market trends, and gain competitive advantage over their rivals – all with the ultimate goal of improving business performance.
Our approach to data analytics
For data analytics, Bross & Partner Consulting Engineers build on CRISP-DM according to Shearer, a cross-industry standardized procedure model for data mining. The partially iterative procedure according to CRISP-DM proceeds in the following six phases: Operational understanding, data understanding, data preparation, modeling, evaluation and application.
The phases of data analysis according to CRISP-DM
The process of data analysis begins with the understanding of the company, which includes the description of the situation of the company and the task, as well as the formulation of the goal to be achieved.
The goal of data understanding is reproducible data collection as well as checking the data quality for completeness and content relevance.
In the data preparation phase, measures are carried out to ensure that the data can be modeled. This includes cleaning, transforming and formatting the data.
In the modeling phase, the first step is to select a suitable modeling technique. Depending on the purpose of the data analysis, simple statistical methods up to artificial intelligence methods are available.
The evaluation is based on the separation of the data set into test and forecast data sets, which is intended to ensure the quality of the model. This is done by comparing how close the model is to the historical test data.
After a successful test and a satisfactory data analysis as well as a positive evaluation of the model, the application with the forecast data is carried out. Finally, the modeled result is available.
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