storage has become so cheap that systems and applications are built to
routinely collect copious amounts information just in case. Making use of all
that data is another matter. Often, the first step in utilizing the data may
prove out to be the most difficult one – that is, to understand what kind of
information the data contains and what kind of business value could be
extracted from it.
data analysis techniques can be used to address the problem. However, the range
of methods and tools is extensive, meaning that exploratory analysis can take a
lot of time and effort. In order to quickly focus on the most essential
information in the data, exploratory analytics requires an approach that emphasizes
very fast experimentation
and effective visualization, enabling short cycles from idea to implementation
A data-driven, interactive, exploratory data analysis approach
Data sources are examined to understand what is available and to determine preprocessing needs such as data sanitization.
Data analysis done by VTT experts to visualize large amounts of raw data and to gain understanding of the data, without need for a clear focus on what is expected from the data.
Iterative focusing on analysis targets is done based on discussions with the customer on analysis findings.
Fast in-memory processing techniques are used to be able to quickly experiment with multiple analysis methods and parameters.
Existing data pipeline solutions can be used as a basis for analysis.
Help in tool selection, evaluation and use for building a data pipeline solution, up to production level quality.
gain understanding of data and its potential.
available data is used as the starting point.
visualization helps to quickly focus on essential analysis targets and provides
efficient means of communication.
Suitable for many event data types and formats, e.g., sensor-type JSON data.
Wide VTT experience of tools and methods in various business and industrial environments, up to production level quality.