Tag Archives: Big Data

Zenser vs Citizen Data Scientist vs You

Data in a computer meant something to me, well, at least as 1’s 0’s, of, say, in ASCII coding. Data is just a string of one’s and zero’s in the binary world. Data, in the digital world, is coded asset that somehow carries meanings to a privileged few, by design, such as that string is your driver’s license number for a DMV, or the arrival time of your flight to your next destination on the flight display board in the departure hall of an airport. There is Big Data. There is IoT Data. And we might agree, data carries information, if properly processed. Given that, what is a science of data? Or is “data science” the same? I am not really ready to discuss this but you will help right?

The word Science, by online Merriam-Webster, is, in one definition, ‘the state of knowing.’ Let me take a leap of faith to say Data Science is a practice, grounded whenever possible by laws and theories, to know about data. And if I randomly look up the definition of a data scientist (DS), and for no specific reason, include a definition here (from this site https://bigdata-madesimple.com/what-is-a-data-scientist-14-definitions-of-a-data-scientist/) as “a person who has the knowledge and skills to conduct sophisticated and systematic analyses of data.” Ok, let’s just go with that. Then, the DS is to perform some guided analyses on available data to bring a know in some context to try to meet the need of the acquirer. The gained knowledge might help the acquirer to make some adjustment or decision.

Let’s agree that a DS knows the tools to bring the know out from data, and the context of the know is for the zense of an acquirer, or consumer of the know from the data. Or, a DS knows how to make zense for a consumer. The DS, now in the world of Big Data, needs to keep a comprehensive knowledge of what data is available and how. This knowledge continues to grow, especially with the diverse amount of IoT data, that in some sense, provide a time-sensitive element into the analysis. Zense as defined in www.ubizense.com is the state of calmness. The calm could be the knowing that the never-ending trade war will bring a 20% reduction in business revenue, instead of the uncertainty of what the magnitude would be. The calm could simply be that your Uber is not likely to be delayed in arrival with the sudden tornado warning in your vicinity. Zense is not measured in negative or in positive energy, just a sense of knowing that brings down the level of emotion from a natural reaction of a consumer.

In sum, we prefer a Zenser label than a DS label, that DS carries out a purposeful analysis every time. Given the numerous data sources, different ‘computing’ is needed. The term ‘ambient computing’ has been used, and we will just leave it as that. Computing is computing just like data is the same as they are one’s and zero’s. Edge-, Cloud- and Fog-computing are the same, just one must consider if there is an advantage to do the computing far from where you stand, or at where you stand, or in-between?

Anyone, that is You, cannot be a data scientist, and any data scientist must develop that purposefulness to be a zenser. We need Zensers in our digital world.