This issue is coming to you from Arizona. It felt good to dust off the hiking shoes and see a new landscape.
Here's what I pondered on the hike to the peaks you see in the image above. Having a fine-tuned AI model, allows you to solve complex problems with a limited amount of data giving you a competitive advantage. Put another way:
when you are looking for a needle in a haystack, its a competitive advantage to know what the needle looks like
The MAD landscape
Last week Matt Truck released his annual Machine Learning, AI, and Data (MAD) report. It's worth a read if your work involves work involves AI/ML.
My two takeaways:
- It's crazy how much and quickly the landscape continues to evolve
- Every company is becoming a data company
the fundamental trend is that every company is becoming not just a software company, but also a data company.
Historically, and still today in many organizations, data has meant transactional data stored in relational databases, and perhaps a few dashboards for basic analysis of what happened to the business in recent months.
But companies are now marching towards a world where data and artificial intelligence are embedded in myriad internal processes and external applications, both for analytical and operational purposes. This is the beginning of the era of the intelligent, automated enterprise
I suspect that even though many firms are marching towards becoming a data company, very few (if any), are approaching it with a data-first view.
Two indicators I've been paying attention to:
(i) is the operating model changing to be digitally focused;
(ii) is data considered a primary asset?
Both of these are tough to achieve. They require rethinking from trying to fit the commonly practiced process into the digital world, to thinking about the new processes that capture data points that previously didn't exist.
Global privacy map
Here is a handy chart that maps data privacy laws around the world. Highlighting individual rights, business obligations, scope, and enforcement.
New world, new terms
A couple of new (to me) terms which I became familiar with:
- Data lake: a large pool of data, held in its raw/native form. The key component is that the schema or requirements are not defined until the data is queried.
- Data fabric: the architecture layer that links together different sources of data (e.g. data lakes) in a consolidated environment. The key advantage is to improve discoverability and access across different storage systems, allowing for across-application access.
- Data mesh: similar to fabric but a mesh is distributed i.e. the data sets are stored across multiple domains. So the focus becomes the decentralization of data ownership. This provides a more agile approach, though a downside is the possibility of data duplication.
Until next time. Stay well.
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