Growexx provided a dedicated team that worked as an extended part for an MNC offering business intelligence solutions for big …
Defining the questions
It is a general practice that in organizational or business data analysis, one must begin with asking measurable, clear and concise questions. For example, we start with a clearly defined problem: A team is experiencing rising costs and is no longer able to submit competitive contract proposals. One of the questions to solve this business problem might include: Can the company reduce its staff without compromising quality?
Once the data is collected, the next step is to get it ready for analysis. A good analyst spends maximum time in a project cleaning the data. This process includes the below tasks-
- Removing unwanted data points
- Removing errors, outliers
- Structuring the data
- Filling data gaps
Our team uses many open-sources data tools for these purposes. However, for very large database or ‘heavy scrubbing’, we use Python libraries (e.g. Pandas) and some R packages.
Now comes the real deal- analyzing the data. The type of data analysis we carry out largely depends on the customized goals of different clients. However, we deploy many techniques; Univariate or bivariate analysis, time-series, and regression analysis are just a few to name. Broadly speaking, all data analysis can be described by the following categories-
- Diagnostic analysis to focus on why something has happened
- Descriptive analysis to identify what has already happened
- Predictive analysis allows us to identify future trends based on the above two analysis
- Prescriptive analysis allows us to make recommendations for the future