Fire propelled ancient intelligence – Results as a Service can light up analytics


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A big part of our existence is about making choices. Preferably the best ones. Evidence-based decision-making matters more than the type and quantity of the data, and by establishing evidence-based decision-making as a deep corporate culture, you could fulfill data’s vast potential.

I find the resemblance between this way of thinking and the evolution of human intelligence compelling. By using fire as a technology, we boosted our intelligence. How could your organization boost yours?

Fire made us smarter

Fire had a big impact on our ancestors, not only did it provide a level of comfort, but it was used for cooking food as well. Cooked meat is easier to digest than raw meat. Having a steak instead of raw meat, your body uses less energy digesting it. Less energy digesting means spare energy for other body functions. Put simple, cooking contributed developing our intelligence and the rest is history. We are the only species that are able to control our surroundings. We are now at a stage where evolving technologies may even outperform our cognitive abilities.

Fire was this game changing technology back then, and meat was the source of energy. The combination of meat (data), fire (technology) and cooking (process) boosted our intelligence.

In today’s digital landscape, how do you make sure you grow more intelligent than your surroundings?

Data science

Applying data science is for me all about transforming new knowledge into bottom-line improvements. Brendan Tierney’s drawing gives an overview of the skillsets and the process that makes up data science. I use the image to get a top of my mind view of what it’s all about. You can use it to place the hypes in the context of all the other good work and skillsets you’re working on already.

This also leaves me to the point that there’s no magic wand of AI or Machine Learning that will make all your worries go away for now. They will at their best make your work more efficient. The real magic happens when you continuously combine skillsets into the lifecycle of ever evolving business problems. If you know what skillset to apply to which problem, GREAT!

Data science may require roles like developer, statistician, business analyst, data warehouse expert and project manager. That adds complexity in succeeding. The executions of your data science team should be swift, agile, fast and transferable to other parts of your organization. Your team needs technology that makes efficient use of their different contributions and skillsets. It may consist of open source and vendor solutions in combination. Anyhow, your teams need the technical environments that makes the process run smoothly. Even across development, test, QA and production. Losing out on technology will make the process less efficient. As always, great technology can only unlock the potential, the rest is up to you and your processes.

If you don’t have the resources that can make up an efficient data science process, we do. Results-as-a-Service is meant for this purpose. SAS can provide resources, process and technology that deliver intelligence to your organization when you need it. If you’re up for a chat about how we can contribute to the smartness of your actions, please get in touch with me on LinkedIn. I have colleagues far more intelligent and brighter than me, and together we are capable to tackle about anything. 😉

Attached are some papers I find of relevance:

  1. MIT Sloan Study: Beyond the Hype: The Hard Work Behind Analytics Success
  2. MIT Sloan Management Review: The talent dividend

This post is also available on our German blog Mehr Wissen with a title Results-as-a-Service – der Turbo für intelligenteres Business


About Author

Helge S. Rosebø

Senior Analytical Consultant

I strongly believe in technology as a facilitator for time to value performance. Great technology should unlock potential. The potential of moving and acting smarter in a world with increasingly detailed and rich data is huge, so why perform any less smart than you could have done?

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