4. Tarun was doing performance reviews of different people at St. Mary's. He realised that Mr. Anand being a counselor was involved in revenue generation activity of the college, so Tarun started collecting sales data to review the performance of Mr. Anand. Tarun observed that Mr. Kamal being a principal was responsible for fee collection (revenue realisation) , Tarun started collecting data around student feedback to review the performance of Mr. Kamal.Tarun realised that Mr. Jagjeevan, who is the maintaince head, is handling the cost centre of the college. Maintenance is an input for revenue realisation and revenue generation, it does not directly contribute to revenue .So Tarun started analysing the maintenance problems faced by revenue generation and revenue realisation team. He analysed Mr. Jagjeevan's contribution and this way he indirectly analysed Mr. Jagjeevan's contribution to revenue.How would you decide on the data to be collected to do performance review of various people in an organisation? How would you build your basic data architecture before gathering the data which could be harvested for data analysis? *Your answer

To decide on the data to be collected for performance review of various people in an organisation, it's important to first understand the roles and responsibilities of each individual. This will help in identifying the key performance indicators (KPIs) for each role.

For example, in the case of Mr. Anand, who is involved in revenue generation, the sales data would be a relevant KPI. For Mr. Kamal, who is responsible for fee collection, student feedback could be a relevant KPI. For Mr. Jagjeevan, who is the maintenance head, the number and severity of maintenance problems faced by the revenue generation and realisation team could be a relevant KPI.

Once the KPIs are identified, the next step is to build the basic data architecture. This involves deciding on the data sources, the data collection methods, the data storage and the data analysis tools.

The data sources could be internal (like sales records, student feedback forms, maintenance logs) or external (like market data, competitor data). The data collection methods could be manual (like surveys, interviews) or automated (like data scraping, APIs). The data storage could be on-premise (like databases, data warehouses) or cloud-based (like AWS, Google Cloud). The data analysis tools could be statistical (like SPSS, R) or visual (like Tableau, Power BI).

The data architecture should be flexible enough to accommodate new data sources and collection methods, scalable enough to handle increasing volumes of data, and robust enough to ensure data quality and security.

Once the data is collected, it can be cleaned, transformed and analysed to review the performance of the individuals. The insights gained from the data analysis can then be used to improve the performance and productivity of the individuals and the organisation as a whole.