Data Analysis and Interpretation

Data analysis is the study of data in raw form, and its interpretation, to create insights. It involves both qualitative and quantitative interpretation. Qualitative data is non-numerical and includes feedback and reviews. It is used to determine patterns of trends, customer issues and trends. Quantitative data is numerical and can be used to analyze metrics such as conversion rates and click-through rate. Data analysis and interpretation is possible in-house or outsourced and can aid businesses in understanding their own products, industry and customers.

The first step is defining an objective or a problem that you are trying to answer using your analysis. This will help you decide what types of data you should collect and guide your data collection strategy. Data can be gathered from internal sources like your CRM software and internal reports, or external sources, like customer surveys and public data.

Once you have your objective and data collection strategy, it’s time to gather the data to be analyzed. This can be done using spreadsheets or data visualization software. Data visualization allows you to see patterns that are not obvious when you view your data in tables format. Data visualization can be represented by network graphs, hierarchical graphs and stacked bar graphs or ring charts. Geospatial data visualization is another option that displays data points in relation to physical locations.

Next, you’ll need to “clean” the data you’ve collected. This involves removing empty spaces, duplicate records, and basic errors. This process can be automated using a tool like MonkeyLearn which makes use of machine learning to clean text data from any source, including internal CRM data, chatbots and social media, emails news reviews, and more.

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