At Software Innovations, we have added some interesting new customers including Green Charge Networks and Prosper Software. In addition to other projects that we cannot mention, these development shows how Visual Rules, modeling approach can solve complex challenges in our Big Data, highly dimensional, customer-focused social ecosystem.
The Juila set is a highly recognizable symbol for complexity as develops in the study of fractals and chaos.
From our discussions on the internet of things ( and here) and other developments, we know that today’s world of mobile-networked-social media presents new opportunities for business models and customer engagement. Example models include freemium where a service is provided free of charge, but a premium is charged for advanced features. Another style is gamificationwhere game thinking is applied to non-game applications to encourage people to adopt them. These seemingly simple, yet powerful concepts often require deep layers of implementation business logic.
This ecosystem is also being extended by sensors and edge devices in the IOT. For instance; heart monitors provide data to doctors, or fitness appliances through ordinary smart phones. Sensors on bicycles provide detailed data. These products are a visible outgrowth of the internet of things and services (IoTS); yet, to create applications one must leverage the extreme granularity of this.
In addition to the exotic, new world of the IoTS, traditional data sources have exploded in granularity and accuracy. Most of the decisions of the inputs into the operational decision include time based vectors of:
- Location, derived GIS stored locations, and proximities
- Awareness of proximal individuals and groups of individuals,
- Edge device feeds, heart rate, temperatures, video, weather
Emerging business models use applications: mobile, cloud and on-prem, to operationalize decisions. The decisions will tell customers what to do or make decisions- this is critical. This means that companies must make effective use of this highly dimensional and time dependent data, often in nearly real-time. To achieve their objectives in this new world, these business require perception of environmental elements with respect to time and/or space or situational awareness.
The complexity arises in problems areas when ordinary, yet challenging business rules areas such as consumer retail and end-use applications, security command and control, complex financial transactions, and products with highly hierarchical catalogs, are combined with real time motions of people, vehicles or traceable financial instruments. The characteristics of this might include large, time dependent vectors of business objects. In this highly dimensional area, each data point exists in the time-location space defined by its attributes and by its relative relationship to all other time-location data points. That relationship is the domain of business rules and the pivot points for the decisions that we are describing. In this realm, the goal of the decisioning and operational decision algorithms is to assign the data point to membership in the most appropriate cluster—even as the subject is in flight. Business Object Data Collections can be linearly separable or non-linearly separable. We have found, particularly these data are often arranged in a vector that must be efficiently traversed.
The company building applications in this complex, operational world faces the dilemma of hand codding solutions or using visual modeling paradigms. These problem often entail an huge number of logic steps with the need to make subtle changes at deep levers. At Bosch Software we believe that only the latter is sustainable.
In our experience, visual metaphors are mandatory for solutions in this ultra-complex, highly evolving world. The business analyst must be able to use their intense focus on business aspects while the technical teams provide a mastery of the technical aspects for a seamless transfer of logic changes. As a demonstration of this, visual rules, business modeling has recently solved problems in these domains:
- An athletic consumer products company seeking to operationalize decisions about fitness equipment
- A company with an innovative financial model built a multi-dimensional, dynamic pricing engine
- A financial institution created dozens of credit score cards, each with over 90 equations and 190 dimensions.
In solving these problems for many customers we have learned the following:
- Complex dimensional decisions can only be understood when they are ‘chunked’ into simple visual metaphors, otherwise business analysts get lost in code or simply give up.
- Visual paradigms including decision graphs, decision tables, state-transition diagrams and composites of these are the only way to quickly solve these challenges.
- Because they cannot traverse and filter vectors of business objects, Decision table-only approaches tightly constrain the type of problem that can be modeled and miss important dimensional aspects of these classes of problems.
In Summary, business operating in this new realm should expect change and build methods that create the proper agility.