How can we manage the ever growing number of data inputs for continuous intelligence?

The way I encounter customers is that for every stream of data, there is a hero, there's somebody whose life we have to make easier or better or more productive in their job function. And so an organization is the sum of all the heroes. I think of these almost as layers in a map, if you know GIS, then you could layer different views on the underlying substrate. So you can have somebody who's responsible for a layer and a hero, but not lose the ability to go top-down and see everything. That's what we're trying to build at Swim. And we have one implementation thus far in Dubai. We do all the traffic, so we have every Uber, every taxi, every public transit vehicle in marine and trains and trucks, each of which is owned by a different person and they'll never share data. The fiefdoms are all about how much data you have, but the overall view or the overall intelligence, say around an accident or something, is derived from a top-down view of everything altogether. The goal has to be to make everybody a hero in their world, let them own insights rather than just raw data and then find some way in which organizations can combine different layers and views.

Anonymous Author
The way I encounter customers is that for every stream of data, there is a hero, there's somebody whose life we have to make easier or better or more productive in their job function. And so an organization is the sum of all the heroes. I think of these almost as layers in a map, if you know GIS, then you could layer different views on the underlying substrate. So you can have somebody who's responsible for a layer and a hero, but not lose the ability to go top-down and see everything. That's what we're trying to build at Swim. And we have one implementation thus far in Dubai. We do all the traffic, so we have every Uber, every taxi, every public transit vehicle in marine and trains and trucks, each of which is owned by a different person and they'll never share data. The fiefdoms are all about how much data you have, but the overall view or the overall intelligence, say around an accident or something, is derived from a top-down view of everything altogether. The goal has to be to make everybody a hero in their world, let them own insights rather than just raw data and then find some way in which organizations can combine different layers and views.
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Anonymous Author
What I'm hearing from a lot of people in manufacturing is how do we take the one-to-many relationship of the single piece of data into that kind of multi-tenancy model and apply the different perspectives. That is their challenge. And they're trying to do it in the broadest way possible, whether it's through manufacturing execution systems or product life cycle management, both of which are cloud-based. We're not talking about 20-year-old SAP sitting in the background, it's not that way. It's much more of “how do I do this so that the value that I'm driving for the organization is insight to the customer, the sales guys, the end-user consumer who buys the electronic device. The car, for example, because now everybody's making autonomous EV. There's a tremendous amount of security that's required with it, and how do I qualify or quantify that?
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Anonymous Author
The key is to identify pertinent data. For example, Quest Diagnostics has a portfolio of about 30,000 different tests they offer. So one would think that if a physician did all 30,000 tests, they would know everything about the patient. That could not be further from the truth. Same thing with data inputs. It is so easy to get overwhelmed and obtain data that offers little value. Identify the specific good and valuable data first. Once you do that, you will find you are likely not overwhelmed. Most firms only get overwhelmed when they don’t know why they have the data input in the first place.
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Anonymous Author
It’s about sifting through the ever-expanding mountain of data and reading out meaningful content. More is NOT better. More is just more. One of my preferred techniques is combining multiple pieces of data into single indexed results — i.e. making more into less and building digestible meaning.
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