What problems do entrepreneurs have with data analysis?
CIMA’s survey shows that 86% of companies have problems with processing large amounts of data. What is more, over 30% of respondents admitted that incorrect analyses harmed their company’s or organization’s revenues. The surveyed finance specialists also noted that difficulties in comparing data from several databases were the main problem in selecting the correct conclusion.
What to do with the huge amount of data?
According to Microsoft’s estimates, global data volumes are growing at a rate of up to 40% per year. Such a situation gives entrepreneurs great opportunities but also generates numerous problems. The possibility of aggregating data from many sources, their correct analysis, clear visualization in the form of interactive reports and drawing business conclusions from them nowadays offer modern systems for data analysis, such as Power BI.
Thanks to them, we get access to all company data without time limits or the location we work in. What is essential, such permissions are granted to all persons involved in data analysis. In turn, the ability to share reports, drag and drop functionality, or create commands in a natural language of queries makes the Power BI system used even by people with less technical knowledge.
You can also read about the benefits offered by Power BI on our blog in the article “5 reasons why data analysis with Power BI is important for every company”.
How to avoid bugs in data analysis?
Thanks to the implementation of an organizational culture based on data analysis, companies can increase the effectiveness of their decisions. However, it should be remembered that the quality of analysis is significantly influenced by both correct data aggregation and proper data segmentation. So how to properly prepare data analysis?
- Specify the goal – each business analysis must have a clearly defined goal. Then, the obtained information becomes valuable and facilitates decision making.
- Rate the reliability of the data – the data set may be incomplete or have duplicate records. As a result, any dependencies detected in them may turn out to be erroneous.
- Compare analogous sets of data – in analytics dominates one basic principle, which is: what data at the input, such value of the analysis at the output.
- Analyse data in relation to the context of the situation – seemingly worthless data in relation to the situation of a given company or the specificity of the market in which we operate, may gain a completely different meaning.
- Take care of transparent results of the analysis – the final effect of business analysis should be information that we can use further to develop the company or correction of previous activities. Focusing on answering the questions “how”, “where”, “when”, “for what” and “why” instead of showing as many calculations, tables or charts as possible is of crucial importance here.
