When companies started to focus on data, with the creation of reports, the growth of CRM, ERP, storage and processing were expensive, companies were selective with what data to collect and store, reports were lean, there was not an abundance of information , it was difficult to analyze, creating a new source of information was costly, it required a project, prioritizing IT over other initiatives with better profitability. At the beginning of the dissemination of data culture, the extraction of reports was mostly done by the IT area.
Think of a company
Then within the business areas, people with knowledge in SQL and databases began to appear to carry out these extractions. The concept of DW was spreading along with BI. Then came data marts, data lakes, and today we have the Loan Cell Phone Number List storage hype. The data mesh, which assigns an owner to the data and helps to mitigate the problem of cemeteries. Think of a company that went through all this evolution, with text reports generated by the IT area, years later the creation of a database used by the business areas, then a data area centralizing information from the entire company, imagine how much data obsolete are stored until today.
Situations in which the
This lower cost of storage and ease of collection ended up reducing the requirement for defining business needs. Situations in which the requester of a new source of information barely knows the data he is requesting are increasingly Gamblingdat frequent, creating situations such as “when in doubt about which columns you will need, bring everything phrase apparently as innocent as this one, and as recurrent in the day to day of data teams, ends up being one of the villains creators of data cemeteries. Years pass, new sources are acquired, many reports are generated, studies and analyzes are carried out.
