Eventually the data maintenance Tortoise will catch the new data work Hare
TD:LR
When you work in a data team you have to split your time between building and delivering new Information Products and maintaining the ones you have already delivered.
DataOps patterns can help reduce the time you spend on the maintenance work.
Eventually the data maintenance Tortoise will catch the new data work Hare.
New data work is often faster
When you start out as a new organisation, a new team, or working on greenfields data work in your organisation you have the benefit of zero maintenance work, zero feeding and water overhead, zero BAU.
And this makes you fast. You can spend all your time delivering that new data work.
And then the cost of maintenance starts to hit
And then stakeholders, consumers and systems start consuming that data and information. And now you have to spend some time maintaining it.
But that’s ok, it’s not a lot of work, the odd update for changes in the systems of records your Information Products rely on, the odd monitoring to make sure it’s still healthy, the odd change to add new things based on feedback from the stakeholders and Information Consumers.
You can still spend most of your time delivering new data work.
And then the maintenance overhead continues to grow
But overtime you will find more and more of your time is spent maintaining and iterating the things you have already delivered and less and less time working on new data work.
You will start to get frustrated, as you are now working on old boring stuff.
Your stakeholders will get frustrated that new Information Products that used to get delivered in days or even hours now take days, weeks or even months between them asking for them and them turning up
And if you have succumbed to the quick delivery rush/drug of doing a lot of the data tasks manually it will be even worse, you wont just be doing maintenance tasks when you need to change something or when something breaks, you will be spending more and more of each day doing that manually drossy tasks, just to make sure the Information Products are refreshed and trust worthy each day.
Adopting DataOps patterns can help
And this is where we should look to adopt DevOps patterns from our Software brethren’s, and implement DataOps patterns.
When doing the new data work we should also spend time building things that will reduce the time and effort of maintaining those things in the future.
Yes our new data work Hare will be slightly slower, but using this approach means data maintenance Tortoise should never catch up.
Keep making data simply magical
AgileData has DataOps patterns built in.
So you can focus on working with your customers complex and messy data.
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