Use-case identification

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Introduction

The use-case identification workshop focuses on understanding project objectives and requirements from a business perspective, and then converting this into a data mining problem definition, and a preliminary plan designed to achieve the objectives. This view is often understood as the data scientist who needs to understand the business issues while the business knowing exactly what they want. In reality, often the business intends to ‘make smarter decisions by using data’, but they lack the understanding of what analytical models are, how they can or should be used and what realistic expectations are around model effectiveness. As such, the business needs to be educated in order to work with analytical models.The workshop contains a brainstorming/discovery process of looking at the different areas where models may be applicable. It involves education of the business parties involved on what analytical modeling is, what realistic expectations are from the various approaches and how models can be leveraged in the business. Discussions on the use-case identification involve topics around data availability, model integration complexity, analytical model complexity and model impact on the business. From a list of identified use-cases in an area, the one with the best ranking on above mentioned criteria should be considered for implementation. The result of this phase is a chosen use-case, and a roadmap with the other considered initiatives on a timeline.

Objectives

Understanding

  • Get an understanding of analytical maturity of the company.
  • Get an understanding of the goals and priorities of the company.
  • Get an understanding of the analytical aspiration of the company.

Broadcasting

  • Education around the working of analytical models (non-technical).
  • Education on the process/phases to build such models.
  • Education around model use, integration and model effectiveness.
  • Education around best practices/avoiding pitfalls.

Brainstorming session around

  • Data availability,
  • Model integration complexity,
  • Analytical model complexity,
  • Model impact on the business.

Selecting

  • Creation of a roadmap of the analytical ideas based on overall feasibility and impact.
  • Selection of the top idea for a pilot.

Outcomes

The workshop will result in a final presentation, containing

  • A summary of the companies business goals and analytical aspirations.
  • The educational material around analytics as presented in the workshop.
  • A road map with resulting ideas from the brainstorm session, placed in a feasibility perspective:
    • Data availability,
    • Model integration complexity,
    • Analytical model complexity,
    • Model impact on the business.
  • Selection of the top analytical initiative
  • An estimate on time/price/resources for a pilot for the top initiative.

After the workshop, the company will

  • Have a good understanding on how analytics can be used to improve business processes.
  • Have realistic expectation on what models can and can’t do.
  • Have an understanding of the data requirements for the various analytical models in combination with the actual data availability.
  • Have an roadmap of analytical initiatives
  • Have an estimate for the execution of a pilot in the top initiative.


Attendees and timing

IBM

  • 1 senior data scientist
  • 1 business analyst
  • 1 project manager

The company

  • 1-2 persons from (higher) management
  • 3-4 persons from the relevant business departments
  • 1-2 persons with IT/data knowledge
  • 1-4 persons from companies own analytics department (optional)

The workshop is split in the following parts.

  • The initial session will take one full day.
    • Business understanding
    • Analytical model education
    • Analytical idea brainstorm
  • The data session will take a half day
    • Deep dive with IT on data availability
  • The closing workshop
    • Presentation of the findings
    • Selection of the top idea