magic starSummarize by Aili

Focusing on Impact: Pitfalls and Maturity Levels in Data Analytics

๐ŸŒˆ Abstract

The article discusses the common challenges faced by data and AI professionals, and provides practical recommendations to increase trust within a company. The key focus areas are: ensuring data problems are treated as business problems, focusing on relevant KPI impact and outcome rather than output, and growing the teams' maturity over time while not skipping the basics.

๐Ÿ™‹ Q&A

[01] Domain Knowledge, Business Relevance and Expectations

1. What are the common pitfalls encountered when there is a disconnect between the objectives of a data team and the overarching business goals?

  • Without a clear understanding of the business context and requirements, data initiatives often fail to deliver meaningful value.
  • This lack of alignment leads to frustration on both ends, with stakeholders feeling underwhelmed by the outputs and data teams grappling with vague directives.
  • Data products or teams often fail not due to technical reasons, but because they delivered something that did not lead to real outcomes for stakeholders.

2. How can data teams foster a culture of inquiry to uncover the real challenges and requirements faced by different business units or product groups?

  • It's crucial to dive deeper and ask "why" until the underlying issue is revealed, rather than just addressing surface-level requests.
  • Probing further with questions like "why do you need/want/can't X?" can lead to valuable insights about the real challenges and requirements.
  • This iterative process helps data teams tailor their solutions to address the specific needs of the business effectively.

3. Why is domain expertise within data teams important, and how can it be integrated into the analytics process?

  • Without a deep understanding of the industry and business dynamics, data analyses may overlook critical factors or misinterpret trends.
  • The rise of data-mesh/hub-and-spoke architectures promotes collaboration and reliability by bringing business knowledge into data products through decentralized business teams.
  • Numbers only make sense in context, so making relevant outcomes for stakeholders to solve a problem should be the top priority.

[02] Data Access & Data Quality

1. What are the challenges analysts often face regarding data reliability and accessibility?

  • Access to accurate data is vital for meaningful analysis, yet many analysts face obstacles related to data quality and accessibility.
  • The bigger the environment, the higher the risk of not knowing which data exists or who to ask for it, and the higher the risk of the data not being in good shape to analyze.
  • Unreliable raw data casts doubt on analysis results, leading to skepticism from stakeholders and reducing the chance that data teams' work leads toward meaningful outcomes.

2. How can clear data governance help address these challenges?

  • Establishing clear data governance is crucial: data teams have to know who owns the data and where they can find it.
  • Clear ownership can also help with data quality, as analysts can ask stakeholders for help in determining which data makes the most sense for their domain.
  • Trust is key, and data teams need to ensure that stakeholders see the data as reliable.

[03] Maturity & Stakeholder Buy-In

1. What are the different levels of maturity for data teams, and why is it important to focus on the foundational stages?

  • Level 1 (Accurate Reporting): Data teams need to be recognized as the 'go-to' organization for data needs, providing well-known reports that act as the source of truth.
  • Level 2 (Insights Facilitator): Once the data is reliable, analysts can act as insights facilitators to solve analytical and business problems, putting the numbers in meaningful context.
  • Level 3 (Break the Bottleneck): As data teams become popular, they need to scale capabilities, invest in self-service functionality, and consider organizational changes and better infrastructure.
  • Level 4 (Proactive Business Partner): Data teams should start generating hypotheses to challenge and support stakeholders, rather than just reacting to their requests.

2. Why is it important for data teams to start small and focus on building trust?

  • There is a smaller chance that self-service functionality will be appreciated if the underlying data is not trustworthy, or if a team is not seen as a reliable data partner.
  • Reacting to the needs of the organization and turning into a proactive data organization over time is the last step, and depends on having the basics in place.


Shared by Daniel Chen ยท
ยฉ 2024 NewMotor Inc.