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.
today we have the storage hyp
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 storage hype Loan Cell Phone Number List 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.
reducing the requiremen
Given this scenario, business requests for new sources of information were becoming more and more comprehensive, aiming at deeper and more detailed analyses. 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 EK Leads barely knows the data he is requesting are increasingly frequent, creating situations such as “when in doubt about which columns you will need, bring everything”, a phrase apparently as innocent as this one, and as recurrent on the day of the data teams, ends up being one of the villains.