Helping Simplify Optimal Decisions

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In the business world, there are many factors to consider when making the optimal decision. Rarely is it binary. There are so many data points to consider that it becomes a combinatorial problem. For example, consider when and how to raise room rates across a hotel chain based on locations and current events or how best to optimize airline ticket prices given fluctuating fuel costs, factoring in seasonal conditions and local / global events. This flows over into our social and personal lives as we rightly expect to find the nearest coffee shops located to the nearest public libraries or where to buy the cheapest gas closest to the supermarket that stocks the groceries we need.

Decision Optimization (DO) is the prescriptive element of the data science lifecycle and is key to delivering artificial intelligence, as Machine Learning (ML) and DO have somewhat of a symbiotic relationship. Consider the use of an ML churn model in the telecom industry to identify if a client is going to leave. While we can predict with a high probability of success that a client will leave, we need to add DO on top of that to decide if we want to retain that client and if so, what special offers to make. Maybe the client has not paid their bill in months, maybe the client has been abusing the service or maybe retention funds have been spent and it is better to let the client leave. The point is that DO can help make optimal decisions on whether to retain clients that were predicted to leave. In this way, DO may help customers save money and reduce business risk.

A DO Definition

I define decision optimization as a means to get an optimal solution to a complex combinatorial problem such as complex planning, scheduling, resource management.

It is based on an optimization solver engine that executes an optimization problem and provides results — best described by figure #1 below.

Figure #1: A Decision Optimization Flow

The science behind DO can be considered complex. It involves an optimization model which is a mathematical formulation of a business problem, such that an optimization engine can interpret it and find a solution that achieves the objectives while respecting constraints described in the model. And it usually requires Operational Research (OR) expertise.

Data science is a multi-persona discipline that requires collaboration between business users, data scientists and developers.

A wide span of IT skills is required involving a plethora of algorithms and modelling frameworks. Each needs installation, upgrading and integration. Business analysts are required for their domain / organization specific knowledge. Data engineers need to prepare access to relevant but often disparate data sources. Raw data and stats may be not meaningful to all stakeholders. Hypothesis and what-if analysis resonate far better. I think you get picture.

Decision Optimization — part of the IBM Data Science Experience

As part of the continued mission to democratize AI and make it more consumable, many Decision Optimization (DO) capabilities have been integrated into the IBM Data Science Experience (DSX) Local as shown in figure #2.

Figure #2: Using a Model Builder and Decision Scenarios created from DSX Data Assets

The benefits of integrating DO within DSX are :

Ease of use

  • Light weight web interface
  • Pre-installed essential software for performing analysis and visualization
  • Support for Python notebooks for general models
  • Natural language interface for scheduling problems

Project-based collaboration for data scientist and other team members

  • Project member management: adding, removing, levels of access (editor, contributor, admin)
  • Sharing of project assets, like data, models and results in current and future projects
  • Ability to work with different data and model variants through multiple scenarios for exploration and what-if analysis

Support for data preparation

  • Import of input data in common formats, such csv and xls

Persona Benefits

DSX can help data scientists focus on DO problems from a data perspective. Optimization modeling is achieved through a wizard to help define tasks and resources. Appropriate constraints and objectives can be selected and edited — even allowing the user to elicit additional constraints and objectives using Natural Language — and solve and visualize the solution on a Gantt Chart.

Figure #3: Use dashboards to compare multi scenarios

DSX also helps data scientists and business analyst collaborate by setting up dashboards where they can mix tables, graphics and Gantt charts, sharing the dashboard with stakeholders for them to validate their findings and conclusions as depicted in figure #3.

For developers that simply want to consume a DO service as part of their application without having to be a data scientist simply integrate the generated service in your application through a REST API. Simple as that.

Your Next Move

The next step is an easy decision. Try Decision Optimization (DO) now by clicking on this Data Science Experience link. My advice: just “DO” it.