~ From off-the-track thoroughbred to sport horse ~
Data analysis is the process of reviewing of raw data and the interpretation of that data to provide insights. It can be used to provide both qualitative as well as quantitative interpretation. Qualitative data isn’t numerical and includes feedback and reviews. It can be used to identify patterns and trends as well as customer-related issues. Quantitative data can be numerical and is utilized to analyze metrics like click-through rates and convert rates. Data analysis and interpretation can be conducted in-house or outsourced and can aid businesses in understanding their own products, industries and customers.
The first step is to define an objective or a problem that you are trying to answer using your analysis. This will guide your data collection strategy and help you determine which types of data to collect. Data can be gathered from internal sources, like your CRM software and internal reports, or from external sources, like customer surveys and public data.
Once you’ve established your goals and created the strategy for collecting data The next step is to collect all of the necessary information to analyze. This can be accomplished using spreadsheets or software for data visualization. Data visualization lets you detect patterns that aren’t evident when looking at your data in the format of a table. Data visualization is represented with 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 data analysis patterns).
Next, you’ll need to “clean” the data you’ve collected. This involves removing white space or duplicate records as well as other basic errors from the raw data. This procedure can be automated using tools like MonkeyLearn that uses machine learning to cleanse text data from any source, including internal CRM data, chatbots, emails, social media news reviews, and much more.