Data Analysis can simply be defined as the way or process in which organizations breakdown and collection of raw data or information collected by the different sectors in the organization to strategize and predict the business next step in decision making to have a better outcome.
Data Analyst intends to examine, cleanup, transform and model the data collected to help the organization study to gain insight on past to future trends to come to conclusions on the decision making of business development and growth.
Below is a diagram on all sectors data is collected in organizations
A Data Analysis advice its organization on its financial income (Sales numbers), market research and the technical expertise to ensure that the quality and accuracy of its data output and also helps to collect data in the customer returns and product trend in the new world system.
In the FMCG sector a data analysis helps to predict sales and knows where to increase the production and also the competitive product smart trends to competitor pricing.
A Data Analysis collects its data from the companies past, present and future trends from the different departments shares its findings with he’s team then create different scenarios in which the organizations can solve this reoccurring problems , then visualize the outcome of this strategy share he’s findings and then implements the next action plan.
The data analyst is responsible for the following.
- Generating reports on sales income and loss
- Evaluating the production systems
- Processing organization personal data
- Providing support for data warehouse and reporting the requirements needed for change.
- Providing technical expertise in the data storage for future references
- Identifying the increase and the decrease in the organization production and customer patronage rate
- Break down of data to help strategize.
Stages of analyzing data.
There are simply steps in which a data analyst takes in analyzing data form its organization platform.
Descriptive Analysist:
This looks into the old (past) events and patterns of the business which includes the visualization used to describe analytics on pie charts tables etc. the scales helps with the understanding of the data e.g the scale shows a periodic increase and decrease ( 12month periodic table).
Diagnostic stage:
this stage helps to examine data, at this stage old past data’s are been recovered that is data mining going back to the company archives and examining sales from period to period to identify the period of growth increase and decrease loss .
Predictive Analytics;
once the organization understands when, what and why certain event took place it now be able to pin point some statistical reasons look into either the re-modelling to further forecast pattern matching again
Prescriptive Analytics.
This is the most difficult part this relates with the implementation and change to be applied for the future purposes, this can be characterized by the techniques used to achieve the process. Includes graph analysis , recommendation and advanced mechanical learning by most faculties in the organization.
This technique brings about the hard core decision making of the organization on the analyzed facts presented rather than assumptions and can guarantee a profitable result to generate more drive and sales to the organization and loose end closed up.
