I discovered HDR a while back. It’s been a while. I stopped paying attention to cameras a long time ago. Then, just browsing through the photography section at B&N, I came across my first mention of HDR. I came to a rudimentary understanding of it. I thought it seemed like a data warehouse and I left that thought there to bounce around in my head. So it’s been bouncing ever since.
I bought a book on some photo manipulating software last year. It’s not like I use that kind of software. But, it was something to read on an airplane, but not what you’d call an airplane read. Tonight, I was wondering if I could sell it at Half-Price Books, but no, there was a chapter on HDR, so I have to read that. I have a better understanding of what it is.
What is HDR?
HDR Imaging, high-dynamic range imaging, captures a larger dynamic range than camera w/o it. It captures 32-bits where a normal camera captures 16-bits. In a photograph that translates into a larger tonal differences between white and black. It lets you do the Zone System in your camera rather than in a darkroom. It attempts to see they way an eye sees. A camera takes one image at one point in time. The human eye sees a series of images and adjusts the contrast on the fly as it attends to various locations in the scene. In the mathematical sense, HDR is local to the normal camera’s global. In the ordinary photograph the global swamps the local and details get lost for better or worse.
In the old days you bracketed the exposure and took three shots -1,0,and +1 hoping that when you got back to the darkroom you’d have a shot you could use. HDR automatically takes a much wider sequence of exposure settings, and constructs a photo where all the details were captured and then puts all the pieces back together from the different shots. Back in the old days, one of the shots was going to be the picture, the best one. In HDR, each shot might have something to contribute to the final picture.
So enough already. How does that get me to something a product manager could use?
Frequency of Use Histograms
As product managers (PMs) we might be envious of the product marketing managers (PMMs) histograms that they get from their SEO analytics and log files. They get a long tail. They understand which conversions worked and which ones didn’t work. They know how their content network is pushing prospects, buying roles, customers, and users to their next sale or use. They can optimize their content to their audiences. Product managers could have the same thing.
Each content conversion has a frequency that you get from the analytics that sum up the clicks recorded in the server logs. Notice the clicks. They don’t look like UI elements, but at an abstract level, that exactly what they are. So in the parallel universe of product managers, we get the use frequencies for every control in our UI. Since that isn’t an off-the-shelf thing, it would have to be built in. When a user click on button A, the button makes a request to a server, the server logs the request, serves nothing finishing the request. Then, using the same SEO analytics tools, sum up the requests in various ways over various periods, and that gives you the frequency of use histogram for the use of the controls in a collection of controls inside your application or across a collection of applications. Product marketers and product marketing managers would have analytic equality. They both have their frequency of use histograms.
I’ve written about these frequency of use histograms in other posts.
- Feature Terrains, Networks of Frequencies discussed feature networks, how they end up being networks of frequencies, and how they end up, for our purposes, in frequency of use histograms.
- Requirements as Circles hinted at requirements having frequencies, so many layers of a system have frequencies and could have frequency of use histograms.
- Factor Analysis, Long Tails, and Stacks, demonstrated how broader views can be built and integrated.
- In More on Innovation Visualization, I used power law distributions, or long tails to build another integrated view.
I wrote a post where I put the PM histogram on the x-axis and the PMM histogram on the y-axis and coordinated them across the axis of symmetry of an exponential curve. But, it must be lost on a prior blog. That axis of symmetry is one point of control. It would determine the length of the long tails of the product document set/touchpoint collection and product controls.
So we have our frequency of use histograms. In the product managers histogram, each bar would represent a single feature or a rollup of feature frequencies in a give use case. The aggregation would depend on the analyst, the product manager.
A data warehouse aggregates data in different ways. The summing of a single data item could be represented by a histogram; aggregates another. Aggregates can also be represented by pie charts. In the end, data warehouses contains histograms.
Back to the HDR
In photo editing software a photo is, likewise, represented via histograms. A data warehouse is like an ordinary photo. It represents a firm at one moment in time, one interval. Cameras use exposure setting to define the time interval that becomes a photos one moment in time. HDR captures a sequence of various intervals of a given scene, and aggregates the various components of the scene through a wide range of aggregations or data fusions. A data warehouse has captured all of its data, all of its light. A wide range of aggregations, exposures within a data warehouse would be delivered as a result of different SQL queries later aggregated to show the local objects in a global picture. Integration with the firm’s or customer’s factor analysis might drive the contrasts within the system.
Prospects talk to marketers every time they click. Users talk to product marketers every time they click. Make sure every click in your lean experiments get logged. Listen to what users are saying to you with every click.