Taking Action: Key Insights for Talent and Organization

In summary, talent and organizational planning is a key driver of success for leading and implementing the DSC. We strongly advocate action planning now. Avoid the fate of many firms which recognize their talent plans are not up to the task and, at the same time, fail to move the needle on making progress. Some key leadership actions to initiate now are as follows:

Develop New (Non-Traditional) DSC Talent Plans

  • • Attract and retain scarce digital talent using new creative sourcing
  • • Evaluate your organizational culture as well as your current skills
  • • Build and interact with communities - pay attention to geographies - outside sectors
  • • Develop bold new roles now to harvest benefits in the future (“data science engineer”) - SCM and problem-solving skills are still kings

Develop Action Plans and Set Goals

  • • Align with HR (research shows signs of misalignment)
  • • Pay attention to culture and build your employment brand
  • • Drive a Leadership Imperative involving digital human interactions
  • • Build communities - be realistic about training, but don’t minimize the importance
  • • Develop and launch purposeful training that helps with skills, as well as attitudes and behaviors

Drive Performance and Action

  • • Develop organizational strategies to suit “new and different” or a “part of operating model” culture
  • • Consider creating smaller, independent, cross-functional Business Units to speed transformation
  • • Promote and reward integration behaviors critical to end-to-end digital implementation
  • • Recognize and counteract emotional responses to boundary spanning requirements that are “overwhelming and difficult to manage” for practitioners.

Organizational Structures

What should your initial organizational approach to developing your digital capabilities and outcomes look like? Decisions about centralization, decentralization, outsourcing, and organizational acquisitions require a target structure to ensure strategic alignment (Figure 5.2).

One approach is illustrated below. The guiding principles for this approach are as follows:

Example Organizational Approach

Figure 5.2 Example Organizational Approach.

  • • Data science is not the same as data analysis. Data science is strategically utilized to develop non-intuitive insights and patterns in an exploratory manner. Analytics are less exploratory and predictive, and, therefore, require a skill set that is more directed to functional reporting.
  • • As an initial approach, try to develop a small “Data Exploration Center” that is centralized and whose direction is set by enterprise strategy. Rare and expensive, top data science talent should be protected from more mundane data engineering (quality and cleansing) tasks and focused instead on business-specific scientific questioning and hypothesis testing for the enterprise.
  • • Data analytics talent may be decentralized and aligned with functional performance reporting.

Key Role Definitions

Data Scientist Profile

  • • Highly skilled, highly trained individuals experienced in building sophisticated analytic models with explanatory power, as well as predictive power.
  • • Data Scientists are capable data modelers, evaluators of data quality, data miners, mathematicians, statisticians, and computer programmers often rolled into one unique person. They often define business problems and explore hypotheses utilizing data rather than creating reports.
  • • Data Scientists are comfortable working with high volume data sets, high velocity, and a wide variety of both structured and unstructured data. They can ascertain the relevancy of disparate data sources and find ways to connect them with integrity and validity in search of answers to complex problems.
  • • Data Scientists can be rare and difficult to recruit and retain and are

drawn to top digital native organizations such as Amazon and Google, who they feel may better value their unique capabilities.

Data Steward Profile

  • • Data stewards are individuals who have a key responsibility in championing, sponsoring, or ensuring data quality, integrity, alignment, and analytic availability.
  • • Data stewards must understand and influence key business processes that generate transactional data, as well as safely replicate and allow for the access of analytic data stores to the firm’s data consumers.
  • • Data stewards are responsible for the key strategies/structures that enable enterprise data dictionaries, cross-functional data definition and changes, data analytics, and warehousing processes, such as extraction and transformation, as well as data set duplication for analyst consumption.

Data Engineers Profile

Data Engineers are responsible for:

  • • Key actions/processes that enable the effective design of enterprisewide data dictionaries
  • • Cross-functional data definition and changes, data analytics and warehousing processes, such as the extraction, transformation of data formatting, and loading or alignment to data stores and lakes
  • • Data Quality and Alignment
  • • Data provisioning for analyst consumption
  • • Data lake architecture, development, and deployment
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