Data analysis is key to understanding your IT workload and improving your service quality. This is unquestioned.
To achieve good results you firstly have to ask the right questions. In the case of process automation you might want to know where irregularities occur in your business, what certain behaviour patterns of your customers look like, which processes are executed most or least effectively, or upon which data automated decisions have been made in the past. These kind of questions not only determine but delimitate the results of the detective-like part of the analysis work.
But the best questions are worthless if the answers look like hieroglyphs. So, secondly you have to understand the answers as well. That’s why data visualization is a crucial part of data analysis. With a good data visualization you can time travel through the history of data and easily surf the whole event tunnel for patterns, trends and anomalies.
Granted, these two aspects of data analysis are commonplace, although not easy to achieve. But what if the data itself is poor or not even accessible because they are lying on isolated sources, platforms, applications? To achieve an end-to-end view in nowadays businesses you might need access to data coming from such diverse parts of your enterprise, like IT-systems, production systems, communication devices, logistics and business partners. Otherwise your analysis effort is not insightful and no more than a nice pastime. That’s why intelligent and selective data integration is the third key success factor for efficient data analysis.
To learn more about this, read the brand new Whitepaper on UC4 Insight.