Data quality

The costs of producing data increases as it passes through all the different pipelines and teams needed to refine the data into something useable.

Why would you create and maintain good quality data.

Nash Squared’s Digital Leadership Report from last year recorded that 64% of organisations they interviewed think that big data and analytics are the way to deliver competitive advantage, yet only 1 in 5 are using it to deliver increased revenue.

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

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”.

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.