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How to organize an analytics project

Often, the success of any reform or development project is based on how well the project can proceed as a process following a certain order and logic. That is in order to succeed, organizing, methods and approaches are vital. You have to ensure that the organization has the required know-how both to process data and information and to define business questions. Execution needs alternative methods to gather know-how and data.

One way to organize an analytics project is the Problem-Solution model. For a project-based organization this is a functional way to organize and work together. Work is done in two teams, both of which include people from the service provider and the customer sides.

The Problem Team’s task is to define use cases, i.e. the problems to be solved. The Solution Team finds out the best way to solve these problems. The members of both teams are often, but not always, the same persons.

This reflects the CRISP DM, flexible model that demonstrates a simple approach to bring analytics to production in a business-oriented and systematic way, but divides the phases between the teams in the following way: The Problem Team is responsible for the Business Understanding phase and the Solution Team for the Data Understanding, Data Preparation, and Data Modeling phases. The Evaluation and Release phases are carried out together. One functional model of progress is the way of dividing the actions into three phases:

1) In the Launch Pad phase, team members clarify the business need (Business Understanding) and dismantle it into parts that are understandable from the point of view of analytics understanding. The Problem Team defines the decision-making points and the questions that the project seeks to answer. Respectively, the Solution Team goes through the data sources and data contents that are required and usable for the solution.

2)In the Rocket Launch phase, the Solution Team forms a synchronized data unity as the basis for analytic models and creates the first analytical models and their processes. For evaluation, the team makes a first version in which the gathered data and its models are linked to the practical planning process.

3) In the Mission Control phase, the created and tested analytics process is implemented in the customer’s everyday decision-making. In practice, Mission Control takes care of bringing the first product or solution into production, and after that the operations can continue as a maintenance service, for example.

- The main goal of the Problem-Solution model is to create a clear and easy-to-understand process model that describes the work phases of the data mining project, emphasizing that data mining is not simply a technical task but also relates directly to business objectives and processes, emphasizes Antti Syväniemi, CEO of Houston Analytics.

AGILE ANALYTICS PROJECT MODEL REFLECTS CRISP