Moving from complexity to simplicity and rapid iteration, we have adopted a  method to ensure your organization is ripe for data monetization. Corporate innovation can be oxymoronic, but focused on the core principles and delivered through a facilitated process with coaching advisors, our approach is established.

Step 1 Reinvent with Pre-flection

Wise practitioners observe that innovation rarely comes from within an industry. As a foundational principle, we start with ‘pre-flection’. The goal of step 1, the initial hypothesis, is to figure out the solution to the problem before starting. It may seem counterintuitive, yet it happens when imagining what comes next  - it's called "Pre-flection".

In a facilitated, reflective session, we work through a journey of each persona in a potential service experience. We search for the maximum disruptive process that the data could fuel (raw or enriched). It's not that we will think ‘out-of-the-box’; on the contrary, we will assume there is no box!

The result of this step 1 is an issue tree. In other words, we start with an initial hypothesis and branch out at each issue. This will provide an opportunity map for the enterprise. That’s the easy part. The challenges come in step 2 and step 3 when we dig deep to prove or disprove the initial hypothesis.

Step 2 Iterate: Assess Potential & Justification

In order to assess the potential 0f the data, we focus to business invention, needs, and justification – but not yet the data and tech itself. Starting with a cross-functional focal team, which works hand in hand with the target and conceived customer, we work through the opportunity map until we have determined each service opportunity that could be a made into a minimal viable opportunity to delight an under-met or under-serviced business need.

For each identified need, we will focus on what data would be required and how the service need would be met in a variety of ways. From this, a list of KPIs and service indicators are identified. Approximated value is assessed as well as some clear business justification is required.

While we intentionally make assumptions (some requiring a leap of faith) the result of step 2 is an iterated and prioritized potential list. As we work through each scenario, we build a model of what would be needed in terms of execution and availability of internal, external or yet-to-be-determined where the data with an associated scale of difficulty to execute.

Step 3 From Mock-up to Make-Up: Proof Points & Learning

Digging deeper and striving for swift decision and execution capabilities, step 3 is about modern test and learn.  The focal team is further empowered to go from mock-up to make-up. Several actions occur in parallel to prove or disprove each prioritized potential.

Critical path tasks in this step ensure that customer measurement and tracking is in place for likes, dislikes and rejection of ideas.  Proof points around benefits to customer and scale-up repeatability is essential.  Pertinent points to address include the identification of “instant” insight and the possible means to action these insights as they relate to new business process. This leads to a move from reactionary business practice to predictive efforts to differentiate outside of tradition value chain.

There is a focus on communication across multiple channels (social, web, crowdsourcing etc.), there is an equally required internal assessment for how and where the organization will adapt. The pivot component of this stage is the continual consideration of how and where the options lie with ultimate resolution focused on the business value with each alternative.

The outcome of step 3 is a minimally viable product(s) that has been vetted and initially blessed by the target customer, meeting the minimum delightful experience standard; simultaneously the internal organizational design is ripe for its next phase.  At this stage, there is a product service definition with prioritization.

Step 4 Express & Experiment: Business Plan Options

Forging a path forward, organizations begin a dedicated transformation toward data and analytical sophistication, they need to consider the best approach for them. Popular opinion suggests that either data or tech is the shortcoming that restrains organizational insight.  Based on our studies, only about one out of five respondents cited concern with data quality or ineffective data governance as a primary obstacle. The adoption barriers organizations face most are related to management and culture; the leading obstacle to widespread analytics adoption is lack of understanding of how to use analytics to improve the business.

Core questions to address the best way to express the next phase is linked to business strategy.  Pertinent questions to address include data ownership and curation, customer integration into business leadership and decision making.  Establishing thresholds and decision criteria are foundation to expedited experiment and fail fast methodology. Likely, each organization will need to acquire new talent and skills to ensure adoption.

The result of step 4 is a spectrum of short, interim and longer terms approaches to data monetization.  Funding, sponsorship and technology options will help sort through the chaff to get to the planting. Decisions resulting from analytic insights bear fruit only when the entire organization gets behind them and makes changes required for breakaway.

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