A typical data analytics workflow looks like this: First, businesses collect data. Next, they perform data quality and governance processes to ensure that the data is properly formatted and structured for their goals. They then analyze the data to detect relevant patterns or anomalies. Finally, they summarize their findings in reports, whose purpose is (in theory) to help stakeholders within the business make informed decisions based on data.
The problem, however, is that it’s one thing to mexico whatsapp number data generate a data report, but it can be quite another to generate a report that’s actually useful to whoever is making decisions.
Again, this isn’t simply an issue with the way reports are formatted or how they present information to users. The problem runs deeper. It stems from the fact that in many cases, reports are based on siloed data, which leads to siloed decision-making.
For example, imagine that you are an HR leader making decisions about how many sales staff you’ll need to sustain projected sales activity over the coming year. This decision requires insight from two distinct types of data: sales data (which details anticipated sales activity) and HR data (which tells you how many sales your existing staff can support).