Who is responsible for what in your analytics efforts?
It is ironic that human skills have led to such great technological advances and that now technology has the ability to provide us with increasingly great opportunities to truly understand customer behaviour and act in order to optimize business outcomes, but at the same time often what holds us back from optimization are inherent human flaws. These flaws take many forms and may for example range from anything such as unwillingness to work in teams, to the inability to communicate effectively, and often impact on the decision-making process and therefore impede us from taking full advantage of all of the insights data offers.
Decision-making is crucial in the analytics process of taking insights found in data and making real, performance-enhancing changes. This post, to be the first of a few that will focus on decision-making, will describe 3 models of decision-making: Digital Insight Management, the Lean Analytics Cycle and RAPID. Following posts will discuss the true implications of each model and their weaknesses.
Decision-making model 1: RAPID ®
Developed by: By Paul Rogers and Marcia Blenko of Bain Company
RAPID is a model created to help organizations define the decision-making process for the highest-quality decision making, strongest performance and ability to learn from previously made decisions.
It focuses on:
- The need to clearly define responsibilities and roles
- Strong strategic planning and prioritization of decisions based on the value they can bring to the organization
- An appropriate balance between control and creative freedom
- Action, speed, and adaptability
Process: In Decide & Deliver, Rogers, Blenko and Michael C. Mankins propose a 5 step process for implementing RAPID:
- Score your organization
- Focus on key decisions
- Make decisions work
- Build an organization
- Embed decision capabilities
Decision-making model 2: Digital Insight Management
Developed by: Eric T. Peterson & Sweetspot
DIM revolves around leveraging your existing investment in Digital Analytics and Optimization to overcome organizational obstacles and effectively capture and communicate insights.
It focuses on:
- Clarity in ownership of data and decisions
- Effective team collaboration
- Analytical understanding
- Developing, clarifying and clearly setting out requirements from the start
- Accountability for decision-making and action-taking
Process: Eric T. Peterson has outlined 10 steps for successfully undertaking DIM
- Clearly define ownership for analytics
- Plan to add analytical expertise
- Establish clear expectations for the use of data and information
- Learn from past experiences
- Define a ‘Hub and Spoke’ model for analytical support
- Create a consolidated view of your data
- Agree on Key Measures of Success
- Define & support analytical workflows
- Quantify and share your analytics-driven success
- Develop your own DIM plans
Decision-making model 3: The Lean Analytics Cycle
Developed by: Alistair Croll and Benjamin Yoskovitz
The Lean Analytics Cycle was developed with the aims of helping start-ups, and other companies, ACT quickly, continually LEARN and make constant IMPROVEMENT in a fast-paced market.
It focuses on:
- Allowing companies to focus on their critical business decisions
- Create the best measurement framework (choose the best metrics)
- Emphasizing the value of testing
- Analysing results so that decisions can be made quickly and effectively
- Continually re-evaluating the top business priorities
Process: Avinash Kaushik, in his post on the Cycle, has outlined 4 steps for carrying out the Lean Analytics Cycle
- Find out what to improve
- Form a hypothesis
- Create your experiment
- Measure your results & take action
Does your organization use any of these decision-making models?
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