Business Intelligence Solution built on Big Data for Internet Telephony Enterprise
Growexx provided a dedicated team that worked as an extended part for an MNC offering business intelligence solutions for big …
Our Data
Analytics
process
01
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?
02
Data collection
Once we established a clear objective, we start creating a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data is needed. This might be qualitative (descriptive) data, such as customer reviews, or quantitative (numeric) data, e.g. sales figures. All data fit into one of these three categories: first-party, second-party, and third-party data. At this stage, we make it clear which Data management platform we’re using in this case.
03
Data cleaning
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.
04
Data Analysis
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
05
Presenting the results
Now, as we complete data analysis, we have the insights which needs to be presented to the clients in the most logical, self-explanatory way. We deploy a few data visualization tools such as Google Charts, Tableau, Datawrapper, etc. Apart from these, our team’s familiarity with Python allows us access to many data visualization libraries and packages available. Our team has worked with multiple clients over these many years and have gained immense experience in presenting complex data in the easiest way by using reports, dashboards, and interactive visualizations that support our findings
