«If you want to run an efficient company by using data, you need to understand what your processes look like, you need to understand your data, you need to understand how this is all tied together.»

Join us as we unravel the complexities of data management with Olof Granberg, an expert in the realm of data with a rich experience spanning nearly two decades. Throughout our conversation, Olaf offers insights that shed light on the relationship between data and the business processes and customer behaviors it mirrors. We discussed how to foster efficient use of data within organizations, by looking at the balance between centralized and decentralized data management strategies.

We discuss the "butterfly effect" of data alterations and the necessity for a matrix perspective that fosters communication across departments. The key to mastering data handling lies in understanding its lifecycle and the impact of governance on data quality. Listeners will also gain insight into the importance of documentation, metadata, and the nuanced approach required to define data quality that aligns with business needs.

Wrapping up our session, we tackle the challenges and promising rewards of data automation, discussing the delicate interplay between data quality and process understanding.

Here are my key takeaways
Centralized vs. Decentralized

• Decentralization alone might not be able to solve challenges in large organizations. Synergies with central departments can have a great effect in the horizontal.
• You have to set certain standards centrally, especially while an organization is maturing.
• Decentralization will almost certainly prioritize business problems over alignment problems, that can create greater value in the long run.
• Without central coordination, short-term needs will take the stage.
• Central units are there to enable the business.

The Data Value Chain

• The butterfly effect in data - small changes can create huge impacts.
• We need to look at value chains from different perspectives - transversal vs. vertical, as much as source systems - platform - executing systems.
• Value chains can become very long.
• We should rather focus on the data platform / analytics layer, and not on the data layer itself.
• Manage what’s important! Find your most valuable data sources (the once that are used widely), and start there.
• Gain an understanding of intention of sourcing data vs. use of data down stream
• «It’s very important to paint the big picture.»
• You have to keep two thoughts in mind: how to work a use-case while building up that reusable layer?
• Don’t try to find tooling that can solve a problem, but rather loo for where tooling can help and support your processes.
• Combine people that understand and know the data with the right tooling.
• Data folks need to see the bigger picture to understand business needs better.
• Don’t try to build communication streams through strict processes - that’s where we get too specialized.
• Data is not a production line. We need to keep an understanding over the entire value chain.
• The proof is in the pudding. The pudding being automation of processes.
• «Worst case something looks right and won’t break. But in the end your customers are going to complain.»
• «If you automate it, you don’t have anyone that raises their hand and says: «This looks a bit funny. Are we sure this is correct?»»
• You have to combine good-enough data quality with understanding of the process that you’re building.
• Build in ways to correct an automated process on the fly.
• You need to know, when to sidetrack in an automated process.
• Schema changes are inevitable, but detecting those can be challenging without a human in the loop.