How we can inject speed into Data Science, Artificial Intelligence and Organisational Data
Edosa Odaro
AI | Value | Advisor | Data | Author | LinkedIn Top Voice | Board NED | Keynote Speaker
What is the value of data? Over $5,000,000,000,000 - alludes Mckinsey, in "The Age of Analytics". This is more than the GDPs of Italy, Canada, and Australia - combined - and over 5% of global "Gross Domestic Product". Given such scale, can business leaders afford to “have a lot of data" and not be able to do "anything with it” - as suggested in a notable Harvard Business Review article on "Why you're not getting value from your Data Science".
But given significant advancements in technology - with big data, the cloud, data science, machine learning, AI, and so on - why does data still feel, for most, like being back in the stone ages?
The simple - single word - answer seems to be "friction".
There seems to be a kind of friction that causes data to slow down - as it travels from its sources to us - as and when we want it. There seems to be a sort of friction that drags out timelines when we try to deliver data projects - even within environments that should be Agile. There also seem to be some organisation friction between people and teams who work across data - such as the frustrations sometimes evident between data engineers and data scientists.
So, what might help?
The #SpeedInjectionModel is an approach for radically driving towards frictionless landscapes - by injecting speed into our data. This holistic framework focuses on enhancing, not just pace, but the availability and integrity of data, data science and artificial intelligence pipelines. It is concerned with all aspects of our data value chain - including acquisition, processing and consumption.
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It does this by assessing data landscapes - across the 3 key dimensions of People, Design, and Lifecycle - to establish the intersection between leadership vision, organisational capability, and environmental conditions.
In its assessment, the Speed Injection Model benchmarks the response to these three basic - yet fundamental - questions, against business or organisational aspirations:
Comment or drop me a line if you would like to know more - or have come across other credible alternatives.
Thank you
Hashtags: #SpeedInjectionModel #data #dataScience #artificialIntelligence #AI #economy #value #frictionless #speed #pace #architecture #lifeCycle #team #business #organisational
AI | Value | Advisor | Data | Author | LinkedIn Top Voice | Board NED | Keynote Speaker
6 年Thanks Arthur Kozinets?- for sharing - and happy to discuss...
Technology
6 年My assessment is consistent with Rotimi R. Ademola. It seems the conversation should start with the end goal, "What value proposition am I looking for?" "What executable am I looking to push forward with this accumulation of data?" In essence, start at the end and work backwards. That would, or should, lead to how the database or machine learning model would be designed. Just my opinion. That and a dollar will get you a cup of dark roast from Dunkin. ??
Data Leader | Data Architecture | Data Management | Analytics
6 年Good read Edosa Odaro, thanks. The #SpeedInjectionModel looks interesting. Would it help if it was three dimensional with a “z-axis” to represent the “Business Impact”. Accelerating data delivery should be under the twin criteria of “actionable” and “beneficial” to those who hold the purse strings. Well maybe not a third axis (??), but just thought to enrich the conversation with an oft ignored angle.
Data Leader | Data Architecture | Data Management | Analytics
6 年Good read Edosa Odaro, thanks. The #SpeedInjectionModel looks interesting. Would it help if it was three dimensional with a “z-axis” to represent the “Business Impact”. Accelerating data delivery should be under the twin criteria of “actionable” and “beneficial” to those who pull the purse strings
Associate Commercial Director | Private Equity | Hedge Funds | Venture Capital | Strategic Business Planning
6 年Great read. Thanks for sharing Edosa !