Data Quality

On Thursday I’ll be presenting at the AIDA User Groups Pi Day event.

I’ll be talking about data quality, and why prevention is better than the cure.

𝗧𝗵𝗲 𝟭-𝟭𝟬-𝟭𝟬𝟬 𝗿𝘂𝗹𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆

One of my favourite talks at Big Data LDN was by Hannah Davies on The Building Blocks of Data Culture During Transformation.

The quality of your data isn’t simply “good” or “bad”.

Nor does a particular quality dimension necessarily need to be improved.

I talk a lot about data quality and how it could be improved, because I strongly believe that with a little bit more discipline we can do a lot better.

Someone reached out to me and asked me to present on data contracts to their organisation after they had a data quality issue that directly resulted in multi-million dollars of lost revenue.

In the absence of set expectations users tend to be overly optimistic about the quality, reliability or dependability of the data.

The reason we talk so much about data quality is because we see the impact of it every day.

The further away from the source the data is, the lower the quality will be.

You can’t use data contracts - or anything - to try to gain control over something owned by someone else.

I came across this Steve Jobs quote recently:

It’s not the tools that you have faith in — tools are just tools. They work, or they don’t work. It’s people you have faith in or not.