Data analysis is the process of looking over raw data and interpreting its findings to come up with insights. It involves both qualitative and quantitative interpretation. Qualitative data includes non-numerical data like reviews and feedback, and can be used to determine patterns, trends and issues. Quantitative data is numerical and is used to analyze metrics such as click-through rates and conversion rates. Data analysis and interpretation can be done in-house or outsourced and can help businesses understand their own industry, products and customers.
The first step is defining an objective or a problem that you want to answer by conducting an analysis. This will help you decide what kind of data you should collect and help guide your strategy for data collection. Data can be gathered from internal sources, such as your CRM software and internal reports, or from external sources such as public data and surveys of customers.
Once you’ve identified your goal and data collection strategy, it’s time to collect the data to analyze. This can be done using tools like spreadsheets and software for data visualization. Data visualization lets you observe patterns that aren’t evident when looking at your data in tables format. Examples of data visualization include the ring chart, or hierarchical chart or network graphs as well as bar graphs that are stacked. Geospatial data visualization is another option that displays data points in relation with physical locations.
The next step is to “clean” your data. This means removing white space and duplicate records as well as basic errors from the raw data. This process can be automated using a tool such as MonkeyLearn which employs machine learning to clean text data from any source–including internal CRM data, chatbots and social media, emails news reviews, and much more.