Danger Ahead for C Level in Digital Commerce and Job Role Ambiguity

Danger Ahead for C Level in Digital Commerce and Job Role Ambiguity

By: Alan Royal, Head of Technology Innovation and Business Transformation, Strategy CIO

Alan Royal, Head of Technology Innovation and Business Transformation, Strategy CIO

IT project failure rates today still are in excess of 40 percent. This statistic becomes the referential backdrop from which organizations now seek to enable organizational specific, competitive digital commerce. The time from innovation to realization in this emergent and highly competitive digital world is likely to be the single most important basis for organizations to remain competitively relevant. Accepting the past, as a predictor of the future can no longer be tolerated; the time to realization is tied to organizations redesigning and reconfiguring their internal staffing models as well as streamlining solution delivery processes. More direct senior management over site is also required to facilitate rapid innovative solution delivery into the public domain.

"Organizations, in growing numbers, are finding themselves with amazing potential software solution narratives, without the baseline referential data intelligence to enable"

Over recent months, C level executives are being on-boarded with a diverse set of titles while largely having a common objective to be the organizational executive enabler of innovative competitive digital commerce. Titles have varied from the granular Chief Data Officer to broader titles such as Chief Innovation Officer. This variation in job title reflects organizational ambiguity as to precisely what is required from organizations to deliver end-to-end solutions into the emergent world of digital commerce.

Eliminating ambiguity as to what is required to deliver requires uncovering and leveraging internal current state data intelligence. Through recognizing and leveraging each organization’s own unique and current state data intelligence capabilities, only then can solution vendors, with their own varying degrees of reliance on organizational current state data intelligence capability, are appropriately selected. Some solution vendors require little to no existing data intelligence while others place total reliance on baseline capabilities. Vendors, which place no reliance, reflect a “greenfield” exploratory solution narrative. Vendors which place total reliance, in contrast, provide a solution narrative that provides the opportunity for organizations to opportunistically “jump ahead” of others.

Only through organizational internal self-assessment of current state data intelligence capability, can the optimal vendor solution be selected, which leverages current state intelligence capability as a basis to deliver into the rapidly evolving digital commerce space and the most immediate and competitively differentiated solution possible.

Uncovering Current State Data Intelligence Capability

Digital behavioral and predictive delivery capability is constrained by current state organizational data. Internal self-assessment is required to uncover often unknown and current state data intelligence capability.

The initial baseline data intelligence current state self declares itself through following these steps:

• Hiring or consulting with a team of well qualified data scientists

• Surveying current legacy systems for data extraction and injection into an initial analytical exploratory database

• The data scientist team applies various analytical modeling tools to interrogate the exploratory database, to uncover potential segmented consumer behavior profiles and associated predictive data elements, which can be uncovered for future state solution exploitation. This endeavor should be undertaken with resources, who are vendor neutral. Only through an unbiased mindset will the fullness of the various potential analytical constructs emerge.

• Additionally, consideration should be made by the data scientists for potential third party data sources, which could supplement current state data to generate better behavior models as well as the associated predicative analytics

While this endeavor is simplistic, it is often never undertaken as existing IT organizations are often absent of data scientists as well as the data extraction process from legacy systems is often complex and problematic.

For organizational CIOs, who do not have referential experience in data analytics, appropriate value attribution to this endeavor is easily missed. Additionally, the allure of what is viewed as amazing and the new technology solution vendors, who are making great capability declaratives, somehow often fail to mention their baseline assumptions as to current state customer data intelligence assumptions. Organizations, in growing numbers, are finding themselves with amazing potential software solution narratives without the baseline referential data intelligence to enable.


If this endeavor seems subjective, it is. Each organization’s current state analytical database footprint is as unique as ones fingerprint itself. Also, if this endeavor seems like somewhat of an art form, it is. Apply four different teams of data scientists and there will almost assuredly be four different outcomes. This variance in outcome is expected, as each team of data scientists brings unique historic experiential mindsets, which drive their methods of data interrogation.

Metaphorically, if one has a complex disease, and goes to three different specialists, it is quite likely there will be three different treatment recommendations emerge. This is why doctors are often referred to as Medical Practitioners practicing the Art of Medicine.

Once the findings from the current state assessment endeavors emerge, senior management can act upon these findings to direct future solution alternative assessments, having confidence, that optimal use of existing data is being leveraged for organizational value optimization.

Rationale for Executive Attention

Over the preceding year, the need for organizations to engage in competitively relevant and hopefully differentiated digital commerce has been increasing in velocity as few could have forecasted. As reported in the news this week, even Nintendo was caught off guard to the point where they took their Pokémon Go gaming experience to the market without due consideration being applied to how it should be monetized for optimal earnings generation. As a result, investors were so excited about the rapid adoption of this new gaming experience that Nintendo asset value soared by 90 Billion within days. However, when subsequent due diligence was applied to the underlying economic earnings model, the assessment outcome reflected a net sum zero profit margin. An embarrassing earnings warning had to be issued. Nintendo executives now find themselves mortified as to how they are going to explain to their BOD, how such a blaringly obvious component was missed as part of the solution building narrative.

It is hoped that via this publication readers have better insight into the core fundamentals, which underlie the enablement of behavioral based predictive consumer experience. With this insight in hand, it is hoped that the potential for yet another “Pokémon Go” event can be bypassed.



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