What is BigData Analysis
Big data analytics for your digital business
What is Big Data Analytics?
Big data analytics stands for the investigation of dynamic and diverse amounts of data. This includes, for example, metadata of surfing behavior on websites, geographic data, weather information, image data and also text information.
A particular challenge is the handling of these data from different sources due to their different format, resolution and quality. For such an analysis of large amounts of data, data scientists rely on a special tool kit for extracting, capturing and transforming the data into the form suitable for further processing.
Big data analytics - as preliminary work for AI applications - can be divided into three work steps:
1. Obtaining data from various sources
2. Optimization and utilization (i.e. cleaning and structuring) of the data room created in this way
3. Use of different methods of modern statistics. The necessary models are based on the special, customer-specific Question aligned.
The first step in the big data analytics process is Data acquisition. It has to be clarified how I get the data, what format it is in, what size it is and what a data input pipeline could look like. For the first step, static exports are usually sufficient, which provide the data science team with the data in different formats such as CSV, JSON or XML. Later in the Big Data project, they have all kinds of automation developed for continuous data supply in order to transfer the data from A. to B. to shovel for further processing. The technologies to be used for this depend, among other things, on how critical the time is to process the data and what update cycles the algorithms require.
In the second step, the data scientists familiarize themselves with the data and test it with regard to it Further processability. Among other things, this rather exploratory approach is about understanding the data. They are examined for quality, consistency and potential errors. Depending on the result, additional strategies may have to be found in order to convert incorrect data in a preprocessing step to an acceptable data status for further processing. Above all, it is important to get a feel for the data and to limit the direction in which further analyzes could go on the basis of the database and the existing quality.
After successful extraction and utilization begins with the help of the Big data analytics Toolbox to search for hidden connections within different data streams. The enrichment of non-annotated data with missing information and cross-connections leads in many cases to the fact that connections come to light that were not initially expected.
A use case of big data analytics in the field Digital marketing is for example tracking down market trends and customer preferences; so that entrepreneurs can make informed, data-driven business decisions. Often it is necessary to develop overarching metrics and KPIs to capture these nebulous values such as “preference” and “trend”.
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