Tired of Data Quality Discussions? Five steps to avoid these discussions
I have been in tons of meetings where data and results of any sort of analysis have been presented. And most meetings have one thing in common, data quality is being challenged *and most of the meeting time is used for discussing potential data quality *issues. The number one follow up of this meeting is to verify the open question, and we start all over again. Sounds familiar?
It can be different. There are meetings where these discussions don’t take place, or perhaps were started, but immediately taken care of. I have seen and been involved in a few. And there was ONE difference *between these types of meetings that I have seen over and over again. The person presenting the data was *not on top of their data, *was *not anticipating and not thinking a step further.
The person presenting the data was not on top of their data, was not anticipating and not thinking a step further.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
Why Data Management remains a challenge in the Data and AI-first era. What challenges companies face with data management and how to begin tackling them
Only data-driven companies can compete in the era of digitization. In the increasingly complex world of data, enterprises need reliable pillars. Reliable data is a critical factor.
Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue.
Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see.