noun: equilibrium; plural noun: equilibria
a state in which opposing forces or influences are balanced.
The key to losing weight is to focus on inputs and outputs. If you are eating more calories than you are burning, you gain weight. If you are at a state of equilibrium, you maintain your current weight, and if you reduce your calories, you lose weight. Sounds simple right?
But when that simple equation meets the reality of our lives, the effort it takes to reduce calories or increase the amount that you burn (or both), the investment often is too much for most to consider or to maintain. Why else is losing weight one of the most common New Year’s resolutions and simultaneously one of the first resolutions that are given up? The input of cutting calories and/or increasing exercise often is too significant to merit the potential outcome.
Ok, why, you wonder, am I talking about losing weight in a blog dedicated to IDP? It’s because, just like in our personal lives, the equilibrium equation remains the same. The effort it takes to achieve an outcome should not be one of equilibrium. We often refer to this as return on investment or ROI.
Unfortunately, when it comes to most IDP solutions, the preference is to focus on all the gains without acknowledging all of the input required. I recently sat in on a presentation given by an industry analyst that provided the following benefits of IDP:
- Faster turnaround times
- Better compliance
- Lower overall costs
- Increased accuracy
But what if the solutions require so much effort that the benefits above become drowned out in protracted, risky, costly projects? Why hasn’t anyone focused on reducing the inputs to make IDP a truly useful, approachable, and less-risky proposition?
Everyone is claiming that their solutions have the ability to learn, but most of that learning is done on-the-job effectively asking organizations to kick the can down the road and trust that expensive solutions will ultimately pay off. What about using learning to reduce the tremendous effort to configure and optimize the system, to begin with?
The answer from even the most well-heeled vendor is to pre-build capabilities also known as “skills” so that organizations don’t have to think about the inputs. But that results in a very specific, inflexible capability that might meet 50-70% of requirements at best but cannot go any further. That’s hardly a great option.
But we have another proposition: What if you don’t have to focus on data science, using a bazillion samples, and constantly babysit a solution. That is Parascript’s vision: to give organizations of all sizes their cake of high performance but let them eat it too without all of those empty calories. Grab a few samples, tell the system what data you want in an easy-to-use GUI and let the system figure out the best, most reliable machine learning techniques to use. Oh, and it will also identify the best way to identify when the data needs to be reviewed giving you the highest amount of data going straight through with no exceptions.
Lose a lot of weight. No gym required.
We’re so confident of the results we’re building an entire program around it. Watch this space…