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The People & Organization Components of Data Maturity

August 2022
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As a foundational element of the IPTOP framework, investing in people’s capabilities and ability to work with data in their day-to-day is paramount for ensuring all other investments in becoming data-driven have a return on investment.

Throughout the session, the third one in our Data Maturity webinar series, we outline how to accelerate data culture and literacy across the maturity spectrum, examples of upskilling programs that work for data skills, how to drive excitement around upskilling, how to organize data teams across maturity stages, and more.

Key takeaways:

  • A detailed overview of how People, are the most important component of the data maturity framework

  • Discussions on how to accelerate data literacy and culture within an organization

  • Examples of upskilling programs that work for data skills


Please make sure to watch the other sessions in our Data Maturity webinar series:

(1) The Five Dimensions of Data Maturity

(2) The Infrastructure Component of Data Maturity

(3) The Tools and Processes Components of Data Maturity

Summary

In our discussion on the people and organization components of data maturity, our focus was on the significance of creating a data-driven culture within organizations. The rapid increase in data generation necessitates that organizations become data-driven, yet many face difficulties due to challenges in culture and skills. The IPTOP framework for data maturity was mentioned, emphasizing the need for a comprehensive approach that includes infrastructure, people, tools, organization, and processes. The progression to data maturity involves transitioning from being data reactive to data literate, requiring investments in data talent, upskilling, and establishing a culture that values data-driven decision-making. The discussion highlighted the importance of aligning data strategies with business goals and the need for executive support in creating a data-centric environment. Challenges such as the pitfall of shiny toys and the need for a cultural shift were discussed, highlighting the complexity of becoming a data-driven organization.

Key Takeaways:

  • Data maturity requires a comprehensive approach involving infrastructure, people, tools, organization, and processes.
  • Organizations must invest in data talent and upskill existing employees to create a data-driven culture.
  • Executive support is important for creating a data-centric environment and ensuring the success of data initiatives.
  • Aligning data strategies with business goals is essential for measuring the impact and success of data initiatives.
  • The transition from data reactive to data literate involves overcoming cultural and skill-based challenges.

Deep Dives

Understanding Data Maturity and the IPTOP Framework

The IPTOP framework ...
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for data maturity provides a comprehensive approach to evaluating an organization's progress towards becoming data-driven. As outlined by Adele Nemi, the five pillars include Infrastructure, People, Tools, Organization, and Processes. Infrastructure involves enabling data access and ensuring data is trusted, findable, and actionable. The People component focuses on transforming talent and creating a data-driven culture. Tools refer to the modern data tools used for analysis, while Organization involves structuring data teams to treat data as a strategic asset. Processes emphasize the efficiency of data teams and the inclusivity of data processes for both technical and non-technical stakeholders. The framework exists on a spectrum, with organizations striving to move from being data reactive, where data is not a priority, to data literate, where data-driven decision-making is widespread. This progression requires aligning investments with the organization's strategic goals and a commitment to continuous improvement.

Cultural and Skill-Based Challenges in Data Maturity

One of the biggest barriers to achieving data maturity is overcoming cultural and skill-based challenges. Despite significant investments in data science and AI, many organizations still struggle to become data-driven. According to a survey by New Vantage Partners, only 26% of organizations claim to be data-driven, with many attributing this to cultural hurdles. A significant portion of employees still prefer gut instinct over data-driven decision-making, highlighting the need for a cultural shift. Adele Nemi emphasized that data transformation is not solely a technology investment but requires a focus on skills, culture, and mindset. Organizations need to integrate data-driven decision-making into their culture by promoting continuous learning and upskilling. The discussion highlighted the importance of executive support in driving this cultural change and ensuring that data strategies align with business objectives.

Strategies for Upskilling and Building Data Talent

Upskilling and building data talent are important steps in advancing along the data maturity spectrum. Organizations must invest in hiring data talent and creating a comprehensive upskilling strategy that empowers employees at all levels to work with data effectively. Adele Nemi highlighted the importance of understanding data personas within an organization and designing learning programs to meet their specific needs. A successful upskilling strategy involves setting transformational goals that align learning objectives with business outcomes, such as reducing supply chain costs through advanced analytics. The discussion also discussed the importance of rewarding change agents within the organization who champion data-driven initiatives and create a culture of learning and innovation. By focusing on upskilling and building a data-centric culture, organizations can overcome skill gaps and drive data maturity.

Organizational Models for Data Teams

The structure of data teams within an organization plays a significant role in advancing data maturity. Organizations can choose between centralized, decentralized, or hybrid models, each with its advantages and challenges. A centralized model treats data science as a center of excellence, promoting collaboration and standardization but may limit cross-functional alignment. Conversely, a decentralized model embeds data scientists within individual teams, creating alignment with business goals but potentially hindering resource sharing. The hybrid model combines elements of both, allowing for strategic projects at a centralized level while enabling data scientists to work closely with business units. Adele Nemi emphasized that choosing the right organizational model requires considering the organization's maturity level, strategic goals, and available resources. The discussion highlighted the importance of aligning the data team's structure with the broader organizational strategy to maximize impact and drive data-driven decision-making.


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