{"id":3872,"date":"2022-07-07T22:39:46","date_gmt":"2022-07-07T20:39:46","guid":{"rendered":"https:\/\/mspowerbi.pl\/blog\/bez-kategorii\/proces-analizy-danych-5-krokow-do-sprawnego-podejmowania-decyzji\/"},"modified":"2024-07-15T00:30:05","modified_gmt":"2024-07-14T22:30:05","slug":"data-analysis-process-5-steps-to-efficient-decision-making","status":"publish","type":"post","link":"https:\/\/powerbi.pl\/en\/blog\/from-the-bi-industry\/data-analysis-process-5-steps-to-efficient-decision-making","title":{"rendered":"Data analysis process – 5 steps to efficient decision making"},"content":{"rendered":"

Data analysis in the company<\/strong><\/h2>\n

Every day, companies have access to a vast amount of data. They come from CRM systems, financial and accounting systems, marketing campaigns or sales results. With such a large amount of data, it is easy to make a mistake. That is why we need proper preparation for their analysis. Where to start? Try to answer the following questions:<\/p>\n

– Are the data you have, are appropriate to answer your questions?<\/p>\n

– Can you draw specific business conclusions from the information you collect?<\/p>\n

– What evidence do you need to support the decision-making process?<\/p>\n

With the right data analysis process and tools, such as\u00a0Power BI,<\/a>\u00a0a large number of different types of data becomes a simple, clear signpost for decision making.<\/p>\n

5 steps in data analysis<\/strong><\/h2>\n

To improve your data analysis skills and simplify your decision-making process, you should take five steps:<\/p>\n

1. Define the questions<\/strong><\/h3>\n

It would be best if you started your analysis of organizational or business data by asking the right question. They should be measurable, unambiguous and concise. For this purpose, define a research problem.<\/p>\n

2. Define clear measurement criteria<\/strong><\/h3>\n

We need to define what we measure and how we measure it. So, think about what kind of data you have and choose the right measurement indicators for it. Examples of questions you might have to ask yourself at this stage are:<\/p>\n

– What timeframes should be analysed? (e.g. year, quarter, month);<\/p>\n

– Which unit of measurement should I choose? (e.g. number of downloads, rejection rate, number of products sold);<\/p>\n

– What additional factors you should take into account? (e.g. seasonality, personalisation, target group). Are they effective?<\/p>\n

3. Collect data<\/strong><\/h3>\n

With a clearly defined question and established measurement priorities, it is time to collect data. When aggregating and organizing data, make sure that files are well prepared, and their formats are correctly saved. They must be compatible with the analysis system we plan to use, e.g. Power BI.<\/p>\n

4. Analyse the data<\/strong><\/h3>\n

After collecting the relevant data needed to answer the question in step 1, it is time for a more in-depth analysis. Tools such as Power BI allow you to create different views of data and present them as intuitive graphs. This makes data analysis a simple task, even for those with little technical knowledge. So, if you want to\u00a0improve your data analysis<\/a>, you need to learn more about the capabilities of this tool.<\/p>\n

5. Interpret the results of the analysis<\/strong><\/h3>\n

Data analysis does not end with clear visualization. The most important thing is yet to come. Of course, this is about interpretation. For this purpose, ask yourself the following key questions:<\/p>\n

– Does the data answer the question you asked at the beginning of the analysis?<\/p>\n

– Are there no objections to the data used for the analysis?<\/p>\n

– Are there any limitations to the analysis that you have not taken into account?<\/p>\n

If your interpretation of the data is in line with the above questions, you have probably reached productive conclusions. The only remaining step now is to use the results of the data analysis process to do even better business. In our case, we found out whether the e-mail marketing campaign is valid and whether it should be continued – if so, in what form, and if not, then why.<\/p>\n

By following these five steps in the data analysis process, you will be able to make better decisions for your business because your choices will be supported by data that has been solidly collected and analysed. By using tools like Power BI, data analysis becomes faster and more accurate – which means we can make better and more accurate business decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"

Many companies do not feel a problem in the lack of data, let alone in the correct analysis of data. In reality, however, this results in difficulties in making the right business decisions. Those who are aware of this are one step ahead of the competition. How should data be analysed to support the development of our company?<\/p>\n","protected":false},"author":1,"featured_media":2145,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[25],"tags":[146],"class_list":["post-3872","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-from-the-bi-industry","tag-data-analysis-process"],"yoast_head":"\nData analysis process - 5 steps to efficient decision making<\/title>\n<meta name=\"description\" content=\"The process of analyzing the data is extremely important and determines the rationality of the information extracted. 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