Hi.
Does your business rely on its data to make decisions?
Do you know where that data came from?
As more organizations adopt data initiatives and become more data driven, it's never been more important to understand where our data comes from.
The price of not knowing could cost you clients.
It could cost you revenue and even your reputation.
Let's take this apple, for example.
Have you ever thought about the trust we put in our food supply?
Do you have the same blind trust in your data that you do in your food?
Let's spend a few minutes exploring the concept of data lineage.
Data lineage helps you determine the history of your data, ultimately understanding its origin.
It helps you validate the accuracy and consistency of your data, helping you improve data quality.
And it helps you understand the transformations your data has undergone to help you achieve regulatory compliance.
All of this adds up to having more trust in your data.
Data lineage tools help automate this process, providing a record of the data throughout the lifecycle, including all the source information,
all the data transformations, and even provide impact analysis.
For data driven organizations and AI initiatives, lineage is a vital part of delivering trusted data to its consumers,
whether that's a data scientist, whether it's an analyst or even an auditor.
It's also critical to our AI models and the data which we feed them.
It's the foundation of trust.
Let's go back to our analogy for a minute.
Most of us shop at a grocery store where we see baskets of apples on display and select the ones we want.
But how do we know we can trust it?
Where did it come from?
Where was it grown?
Who picked it and when?
How did it get to the warehouse?
Which warehouse was it stored in, and for how long?
These are all questions we don't necessarily ask ourselves about apples, but we do ask similar ones about our data.
Let's start from the beginning.
Apples are grown on a tree, in a farm, where farmers have hundreds, even thousands of trees on their farm.
They water them, they grow them, they curate them.
They protect them from animals and insects.
Once it's time to harvest them, the apples are put into bushels and collected on the farm.
Those bushels are then loaded onto a truck, and then that truck delivers them to a warehouse where they're sorted and selected for quality
and sold to the grocery stores where they are then put on a truck again and delivered out to the grocery store.
What I've done here is walk through the lineage of that apple.
I've given you its lifecycle.
I'm able to show the history from the inception of the apple in the field all the way to the shopper who is buying it for their favorite snack or recipe.
Let's take it a step further.
Each apple carries a significant amount of metadata.
It can tell you which distribution center it went to, which truck transported it all the way back to the tree in the farm where it was grown.
That's a lot of information for one apple.
But that metadata is key to its lineage and ensuring that it's trusted.
It demonstrates compliance.
Knowing where the apple came from, who harvested it, and who transported them.
In the data world, having this type of information is crucial to ensuring the process and standards are in compliance with regulatory rules.
Now, let's say a grocer has complained about the quality of the apples they've been receiving from their distributor.
The distributor identifies the apples have come from one particular farm.
The data also shows the apples in questions have come from a specific set of trees on that farm.
Now we're getting to the root cause.
Understanding that level of history and impact can help the farmer identify which trees are producing the bad apples and take corrective action.
It can help the distributor understand which farms are producing good apples and eliminate purchasing bad apples
from a specific farm or even a specific part of a farm.
And it helps the grocer deliver high quality, trusted and delicious apples to their consumer.
In the past, it was a manual effort requiring significant labor and prone to human error to understand the limit lineage of the food supply.
RFID tags and tracking technologies have revolutionized our food supply, ensuring higher quality, fresher, more consistent food from farms to tables.
Automated data lineage will do the same for our data and our AI models.
What was once a laborious manual effort requiring thousands of man hours can now be done in minutes.
To recap, automated data lineage solutions help clients create dynamic, real time lineage views of their data
that show the history of the data all the way back to its origin, validate the accuracy and consistency,
improving data quality, and understand all the different transformations that data has undergone to ensure regulatory compliance.
All of this leads and adds up to having more trust and confidence in your data.
Don't let one bad apple ruin your data initiative.
Click on the link below to learn about how IBM can help you achieve lineage in all your data driven initiatives.