Organization models for commercializing Big Data

Organization structures for Big Data

Mobile operators must define the right organization model for managing large Big Data projects. The model needs to effectively align the demands of the business with the technology requirements needed to support those demands. Such initiatives necessarily involve an element of cultural change within the operator, as they require collaboration across conventional functional and business unit silos.

The importance of organization structure

Commercializing Big Data products requires an operator to integrate a strong IT analytics capability with externally-facing marketing resources. Deciding how this integration is organized and led is a critical part of successful Big Data initiatives.

In our Organizing for Big Data page we look at the overall issues of organization structure. This post looks at the different organization models.

There is no ‘cookie cutter’ approach to organizing Big Data teams. Operators begin at different points along the evolution path, with different existing organizational structures and levels of maturity, and varying sets of capabilities.

Arriving at the appropriate cross-functional, cooperative model for leveraging data assets involves some degree of compromise and trade-off between the various stakeholder groups.

 What are the preferred organization models?

There are three discrete organization models that are currently being implemented across those mobile operators who are actively pursuing Big Data initiatives:

  • IT systems-led (including a dedicated data analytics group)
  • Business function-led (with Marketing and/or Finance as the lead functions)
  • Matrix organization (a hybrid IT-Marketing-Finance group)

Post - big data monetisation chart 2

Given the clear potential for creating new and sustainable revenue streams from Big Data initiatives, it is likely that the majority of operators will elect to form matrix organizations over time as a critical mass of Big Data resources is established.

The importance of senior leadership

This structure implies a senior executive leader who understands the overall operator business and has a clear line of sight into C-suite priorities.

In addition, this leader must have considerable authority to make key decisions about investment priorities with regard to both IT and business resources, a high degree of respect among both business and IT stakeholders, and the ability to balance short-term business needs with longer-term goals for building out the necessary data capabilities.

As such identifying and attracting such talent (if unavailable internally) is a potential drag on progress in gearing up to attack the Big Data opportunity.

The war for talent is not confined to the fierce competition for data scientists!