Data cleaning near the source
High-quality data drives good business decisions, increase in revenue and saves cots. Cleaning of data is expensive, it may reach up to 40% of total project costs. Some organizations willingly leave out 20 or 30 percent of data in their analytics because bits are missing. In effect, they accept blatant inaccuracy as normal. Sweeping this multi-billion dollar challenge under the carpet is fundamentally misguided and creates shaky foundation on which to build a data-driven actions for companies or government.