Achieving Agile Analytic Systems

We build a consistent and robust process for the platform of your choice. With our agile approach, these activities are not necessarily sequential, but rather proceed in parallel iterations with a high degree of interaction between them.

1. Identify data-driven business and operational processes

   We start by leveraging our agile requirements efforts, to identify key business and operational processes as well as the analytic tasks that drive them. We look for decision processes involving specialist expertise that involve similar, repeatable analytic tasks performed by multiple decision-makers. We can then prioritize analytic candidates based on ease of implementation, importance to a specific business process, overall value to the organization and the number of decision-makers who would benefit from the solution.

2. Evaluate decision requirements

   For each decision-making process that a company chooses to implement, we need to carefully consider the decision-maker's needs. By closely examining specific information, visualization and analytic requirements, we can determine how to most effectively incorporate analysis techniques and information links into a decision support workflow.

3. Perform information audit and evaluate information infrastructure

   A rigorous audit of the data and information that influence the decision-making process will determine necessary information links and surface underlying database infrastructure issues, such as normalization, curation, and ETL impacts. This evaluation will provide the basis for defining a consistent information base for the various analytics that will be produced.

4. Define analytic and decision-making process

   In this task, we work with clients to map out specific decision processes and add or develop analytic tools.

5. Test and refine

   This task involves rolling-out an analytic workflow either on a pilot basis or more broadly as a general deployment to a select group. There will be an ongoing process of revisiting and refreshing existing analytic workflows to ensure they remain relevant as business requirements and data availability evolve. Once a company reaches a certain comfort level with the application, a broader roll-out is appropriate.

   When the analytic application is deployed widely, we conduct a review to ensure it has met specific pre-determined business objectives. Success criteria may include measurable improvements in decision-making efficiency and accuracy as well as specific cost-savings attributable to decisions reached with the application. By following this straightforward approach, companies can easily build custom analytic solutions that provide them with significant value.