Data Analysis and Interpretation

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Data analysis is the process of reviewing of raw data and its interpretation, to create insights. It encompasses both qualitative and quantitative interpretation. Qualitative data includes non-numerical data such as reviews and feedback. They can be used to identify patterns, trends and problems. Quantitative data can be numerical and is used for analyzing metrics like click-through rate and convert rates. Data analysis and interpretation, whether internal or external can assist businesses to better understand their products, industries and customers.

The first step is to define an objective or a query that you want to answer using your analysis. This will help you determine what types of data you should gather and guide your data collection strategy. Data can come from internal sources, such as your CRM software and internal reports, or external sources like public data and surveys of customers.

Once you have your objective and data collection strategy, you’re now able to collect the data for analysis. Data visualization software and spreadsheets are a great tool for this. Data visualization helps you see patterns in your data that may not be obvious when viewing it in the format of a table. Examples of data visualization include the ring chart, or hierarchical chart, network graphs, and stacked bar graphs. Geospatial data visualization is an additional option that displays data points in relation with physical locations.

The next step is to “clean” your collected 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 automatically clean up text data from all sources, including internal CRM data, emails, chatbots as well as news and social media reviews.