Modern organisations, even in normal operations, generate vast amounts of data. If you run a manufacturing unit, for example, you might already be recording operational and shop floor data extensively.

The data could be captured in files, or electronic folders and data sheets. The challenge at this stage is often that data is captured differently by different stakeholders, functions and locations. Let us say your regional sales teams record the sales data in excel sheets of varied formats. One team captures the date of proposal submission, while the others do not. Also, the format of data is different. Some capture customer names as , others capture it as .

A typical stage 01 scenario: In a large cement & steel conglomerate, air travel data was being maintained in spreadsheets, but in different formats and at different locations.  It took significant efforts to consolidate the data into a common format. Upon applying simple filters on the consolidated data, it was discovered that there were 30% cancellations across the board, causing losses of INR 75 lakhs ($115,000) to the organisation. 

At this stage, the data is available only as individual data points, e.g. PDF scans. To get usable insights, data from all individual files have to be mined separately. For example, say the sales team has the record of invoices sent to hundreds of customers, but only as scanned copies in separate customer files. Creating a quarterly report will require taking information from all individual files.

To mature to the next level, data across sources and locations have to captured uniformly in a transaction based system.