VyAnalytics
Put your data to work
You know that analytics can improve your business: increasing revenues, reducing costs, and managing your processes more effectively. But how do you go about incorporating analytics?
| VyAnalytics | Internal Development and Open Source |
Enterprise Heavyweight |
||
|---|---|---|---|---|
| One-time project | Full platform | |||
| Costs | Low | Medium | Very High | Very High |
| Analysis Stage (months) |
1 - 3 | 4 - 8 | 18 - 24 | 3 - 5 |
| Deployment Stage (months) | 1 - 2 | 2 - 4 | 6 - 12 | 2 - 3 |
| Risks | Low | Moderate | Low | |
| Novel Custom Analytics |
Yes | Yes | Limited | |
| Intellectual Property Ownership |
Partnerships available |
Yes | No | |
Mike worked as a software engineer and researcher for almost two decades building analytics software for prediction, optimization, and decision making. Mike used best-in-class open source and commercial tools and he also worked with top-notch teams to develop these challenging projects. But despite these advantages it took significant time and effort to solve these problems at an organizational level.
At the same time, interest in analytics exploded. Companies stepped up efforts to warehouse data in the hope to gain a competitive advantage. Simple charts and statistics provided benefit, but only the largest companies had the resources to gain the full benefit from understanding patterns in their data. Another trend that Mike saw was that more companies were incorporating analytics into their real-time decision making. This powerful ability gave those companies serious advantages: overall performance was improved and quantified, response times for decision making was reduced, and their systems could dynamically adjust to changing conditions. So how could more companies benefit from serious analytics? And how could the obstacles to entry be lowered?
The key was to build a complete software service which would address a complex set of related issues. The software would not just provide powerful analytics but it would streamline the entire process of using analytics from initial analysis to final deployment. This includes analytical concerns such as validation, parameter determination, data preprocessing, and reporting as well as software concerns like logging, high-performance computing, and well developed APIs. This change in perspective would eliminate the large up-front investment for companies and help bring advanced analytics into the mainstream.