How we work

Bringing analytics into reality in a business-oriented and systematic way

Crafting improved business operations through agile analytics projects


Regardless of the application area or industry, Houston Analytics experts have the ability to quickly identify, create and iteratively refine the best solutions for our customers’ businesses.

Houston organizes projects around our two-team Problem-Solution model. In practice this means that the work is done in two teams, both of which include experts from Houston Analytics and the customer’s side.

By combining the customer’s domain expertise and Houston Analytics' analytical capabilities, we ensure the best possible outcome. Houston  typically has the main execution role in many tasks but not all. However, it is guaranteed that all information and skills related to created processes will be transferred to customer during the project.



Co-creation with Problem-Solution teams

  • The Problem team is formed to work on business themes based on shared understanding of the outcomes to be achieved. The Problem Team’s task is to define use cases and their related decision points in such a way that they can be addressed analytically.
  • The Solution team is formed to work on and iterate over the practical solution driven by desired business outcomes.

Structured project model

To define the data and analytical models necessary to support business decisions, we follow an agile analytics production model based on the Cross Industry Standard Process for Data Mining (CRISP-DM).

This model provides a structured way to leverage customers' industry-related knowledge as we apply our expertise in analytics and automation.

Project model simple v2

Define and Prioritize

Define, clarify and prioritize needs.

Describe solvable parts.

Collect data with qualitative and quantitative methods.

Phase 1 – launch pad

Phase goals

Identify business/operational pains, challenges and requirements; quantify potential value and define success metrics.

Create understanding of current operations and processes.

Define key points where analytical intelligence will be injected into systems and processes.


Develop and Test

Identify steps, synchronize data and set goals.

Develop and test the analytical models and associated processes.

Create scenarios.

Phase 2 – Rocket Launch

Relate data to business problems, plan analytical data view, connect to current data assets.

Transform raw data to produce variables that describe the activity and state of systems/entities being modelled, and dynamics of how these are changing.

Design, build and test predictive models, evaluate performance technically, then test effectiveness against business objectives.

rocket launch

Deploy and Automate

Combine analytical outputs with specialist domain knowledge.

Implement validated analytical processes into everyday decision-making.


Phase 3 – Mission Control

Configure delivery of outputs – scores, alerts, recommendations, etc. – to operational systems and end-users.

Publish production-ready analytical processes for continuously monitoring / scoring latest data and triggering outputs / actions . 

Install in automation environment. Configure to run, monitor and manage the processes and the models they include.

mission control