Crossing the data divide

Did you know that the tech elite does not work at all like you do? Most people don’t, and don’t want to know. The State of DevOps report concluded a span of 1000x in delivery time and reliability between the elite and low performers. There is a similar gap for delivery time of data or ML pipelines to production. The gap in ability to compute datasets is higher, somewhere around a million times. We call this the data divide or the AI divide. It is widening over time, since most companies are not aware of its width.

• We will share the principles we applied in the most successful Scandinavian crossing of the data divide. We never explicitly shared or described, nor fully understood the principles at the time, but it is long due to explicitly enumerate them.
• The presentation will likely be uncomfortable and surprising, because it does not match what you do and what your vendors say. You will have no practical use of the information, since you cannot apply the principles, because they contradict many contemporary trends and popular technologies on the market, and you would be unable to overcome the forces of trends, popularity, and messages from vendors. They worked beautifully for us at the time.

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Lars Albertsson – Founder | Scling

Lars Albertsson is the founder of Scling, a data engineering startup based in Stockholm. Scling provides data-value-as-a-service – customer tailored data engineering, analytics, and data science. Lars is a frequent conference speaker on data engineering and data strategy. Before founding Scling, Lars has worked at Google, Spotify, Schibsted, and as an independent consultant, helping organisations create business value from data processing and machine learning.