Infer from the past .
Descriptive analytics means describe or summarise raw data and present it in a user-friendly easily interpretable format.
It uses two techniques, namely data aggregation and data mining that help describe the past.
It gives an idea of “what has happened” in the past – one minute ago or one year ago. It is very critical information as it allows us to learn from our past behaviours, and to rectify our wrong deeds that might influence our future outcomes. Common examples of descriptive analytics are reports that give us historic insights regarding the organization’s operations, production, financials, sales, inventory and customers.
Descriptive Analytics Methods .
Descriptive approach is considered to be the foundation of research. Its logical design is based on statistics of the research analysis. Since this analysis doesn’t explain the cause of result, hence it can’t take into account the validity of research results. Here are the common methods used in descriptive analytics:
- Observation Method: In this method data is observed in both natural and artificial ways in order to draw meaningful conclusions. It is an effective way of inferring from natural observation since we can obtain original results of the research. On the other hand, in the artificial method results would depend on the quantity of the data provided for observation.
- Case study Method: It involves a deep study on all the problems discussed. It makes us understand a particular situation more closely.
- Survey Method: In this method questionnaires are prepared and given to the participants. After receiving the answers the research is preceded and results are concluded.
Activities involved .
We at Intellectyx create a comprehensive design and implementation process that includes the following activities:
- Offer this backward looking approach for finding forward looking business trends
- Focus on category descriptions and comparisons
- Review requisites for use cases
- Review existing hardware and recommend changes, if required
- Detect and describe patterns
- Categorization of data groups and elements using MECE (mutually exclusive and collectively exhaustive)
- Design and develop a procedure for loading data from different sources
- Design and execute a concrete system for data storage, schema and partitioning
- Design and model a process for data integration
- Design and execute explicit data processing jobs
- Discrete assignment of individual data set members based on similarities and differences