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Data Analysis and Interpretation
Data analysis is the examination of raw data, and the interpretation of that data to provide insights. It involves both qualitative and quantitative interpretation. Qualitative data is not numerical and includes feedback and reviews. It is used to determine patterns or trends, as well as customer issues. Quantitative information is numeric and can be used to analyze metrics such as conversion rates and click-through rates. Data analysis and interpretation both internal and external can assist businesses to better understand their products, industries and customers.
The first step is to identify an objective or a question that you are trying to answer with your analysis. This will guide your data collection strategy and help you determine the types of data to collect. Data can be gathered from internal sources, such as your CRM software and internal reports, or external sources such as customer surveys and public data.
Once you’ve established your goals and developed a data collection strategy, the next step is to collect all the data needed to analyze. Spreadsheets and data visualization applications are a great tool for this. Data visualization lets you see patterns in your data that might not be obvious when looking at it in a table format. Data visualization can be represented through network graphs or hierarchical graphs and stacked bar graphs or ring charts. Data visualization can also be geospatial, which translates data points in relation to physical locations (such as a map of political patterns of voting patterns).
The next step is to “clean” your data. This involves removing empty spaces as well as duplicate records and basic errors. This process can be automated with software such as MonkeyLearn. It utilizes machine learning to remove text data from any sources including internal CRM data, emails, chatbots as well as news and social media reviews.
