Data Rich, Decision Poor: Why Good Data Still Doesn’t Change Decisions with Lucy Nemes

Ket Takeaways

• Human-Centric Data Challenges: Approximately 80% of data leaders attribute ineffective data use to organisational culture, people, and processes rather than technology or data quality.
• Interconnected Failure Patterns: Data initiatives often fail due to four main issues: Organisational Misalignment, Strategic Ambiguity, Operational Gaps, and Cultural Lack, which hinder effective decision-making.
• Organisational Misalignment: Data teams are often not involved early enough in the decision-making process, leading to their role being limited to validation rather than influencing key decisions.
• Strategic Ambiguity: Many teams prioritise building dashboards without a clear understanding of the specific decisions they need to support, resulting in misaligned efforts.
• Accountability Gaps: While metrics are tracked, there is often no accountability assigned to an individual for driving these metrics, creating a lack of ownership and results.
• Cultural Issues: Employees may lack the skills to interpret data or may not feel confident enough to voice insights from the data, reducing overall data engagement within the organisation.
• REAL Framework Implementation: By focusing on Relationships, Goals, Accountability, and Literacy, organisations can address these challenges through collaborative tools, decision-centric approaches, ownership assignments, and tailored skill assessments, leading to a stronger data culture.

Webinar Details

Title: Data Rich, Decision Poor: Why Good Data Still Doesn’t Change Decisions with Lucy Nemes
Date: 2026-10-27
Presenter: Lucy Nemes
Meetup Group: Book Launch with Technics Pub x MWS
Write-up Author: Howard Diesel

Why Do Enterprise Data Initiatives Often Stall?

Lucy Nemes is an independent data strategist and author of Beyond Dashboards, specialising in helping European enterprises turn existing data into actionable business decisions.

Data initiatives frequently stall because organisations focus on building dashboards rather than improving executive decision-making. Strategic alignment requires closing the communication gap between technical data teams and business leaders.

The presentation structure mirrors Nemes’s book across three core phases:

• Part 1: Analysing why enterprise data initiatives stall.
• Part 2: Introducing the REAL data strategy framework.
• Part 3: Applying practical field-guide tools.

Key Takeaways

• Core Mission: Help companies use existing data assets rather than building excess dashboards.
• Target Audience: Serves both technical data leaders and business executives seeking a common language.
• Pragmatic Application: Emphasises adaptable, real-world tools over rigid theoretical models.

Frequently Asked Questions

• What is the primary purpose of Beyond Dashboards? It provides a practical framework to resolve the cultural and operational barriers that stop companies from using data in decision-making.

What Factors Hinder Enterprise Data Success the Most?

Enterprise data failures stem from people, culture, and process bottlenecks rather than technological or data quality defects.

A senior product lead admitted that despite having clean, accurate dashboards built by her analytics team, she felt unsafe admitting she still did not know what decision to make.

Industry surveys show that roughly 80% of data leaders identify human and cultural factors as their primary barrier to success. Organisations spend millions on software tools, yet human behaviour remains the true bottleneck.

• Technology & Infrastructure (20%): Analytics platforms, data quality, and BI software tools.
• People, Culture & Process (80%): Psychological safety, clear roles, aligned goals, and operational habits.

Key Takeaways

• Human-Centric Failure: Clean data and visual dashboards do not guarantee operational decision clarity.
• Psychological Safety: Stakeholders often hesitate to express confusion regarding dashboard metrics in public settings.
• Investment Misalignment: Over-investing in technology fails when organisational culture is ignored.

Frequently Asked Questions

• Why do clean dashboards fail to drive executive actions? They fail when organisations treat decision-making as a software problem rather than addressing psychological safety, role clarity, and workflow processes.

What Are Common Failures in Data Initiatives?

Data initiatives consistently stall across industries due to four recurring organizational failure patterns.

Organisations repeatedly suffer from four structural breakdowns that prevent data from influencing business strategy:

• Organisational Misalignment: Data teams are involved late, merely to validate decisions already made.
• Strategic Ambiguity: Teams build outputs and dashboards without identifying the underlying decision.
• Operational Gaps: Companies report on metrics without assigning individual accountability to move them.
• Cultural Lack: Staff lack data reading skills or fear speaking up about what data reveals.

Buying new software applications treats these systemic symptoms as a quick fix without addressing underlying workflow flaws.

Key Takeaways

• Interconnected Friction: Failure patterns reinforce each other, eroding trust between business and technical units.
• False Silver Bullets: Software purchases cannot replace clear organisational governance and communication habits.
• Upstream Involvement: Data teams must participate when decisions are being shaped, not after.

Frequently Asked Questions

• Can purchasing new analytics software fix stalled data initiatives? No, software tools cannot resolve broken team alignment, ambiguous goals, or unassigned metric accountability.

How Does the REAL Framework Address Data Bottlenecks?

The REAL framework resolves organizational data bottlenecks by transforming transactional help-desk requests into collaborative business partnerships.

The REAL framework stands for Relationships, Goals, Accountability, and Literacy. Pillar 1 addresses misalignment by replacing transactional ticket-taking with shared metric definitions.

In one retail case study, four separate teams created four distinct “conversion” dashboards. While teams argued over numbers, customer complaints rose 18% and Net Promoter Score (NPS) dropped 7 points.

Implementing a Clarity Workshop establishes a shared Goal/KPI Tree, defining single metric owners and precise cross-departmental definitions.

Key Takeaways

• Framework Structure: REAL targets Relationships (misalignment), Goals (ambiguity), Accountability (operational gaps), and Literacy (cultural lack).
• Clarity Workshop: Uses five alignment questions to build consensus before technical construction begins.
• Goal/KPI Tree: Maps individual departmental contributions to a single primary organizational objective.

Frequently Asked Questions

• How does a Clarity Workshop prevent metric disputes? It brings all stakeholders together before building tools to agree on shared goals, metric definitions, and data trust.

What Is the Target Decision Before Data Analysis?

A decision-backwards approach re-engineers data projects by defining the target decision before performing analysis or data modelling.

Instead of starting with available datasets, data teams must uncover the executive question driving the request.

A telecom company initially requested a customer churn model, generating 40,000 at-risk subscribers that the retention team could not process. Re-framing the inquiry to “Who is at risk that we can realistically save?” produced a prioritisation tool that increased retention by 7%, saving €4,000,000 in six months.

The Decision Backwards Brief formalises this by documenting decision timing, decision makers, existing insights, and required output formats.

Key Takeaways

• Goal Alignment: Shift focus from descriptive tracking (“who will churn”) to actionable interventions (“who can we save”).
• Decision Backward Brief: A structured intake tool forcing stakeholders to clarify target actions.
• Root-Cause Discovery: Asking how decisions change based on findings prevents building unused tools.

Frequently Asked Questions

• What is a Decision Backwards Brief? It is an intake framework that captures the decision maker, deadline, existing knowledge, and precise action triggered by data findings.

Who Is Accountable for Changes In Key Metrics?

Operational data accountability requires assigning a single named individual, not a department, to act when a key metric moves.

Reporting metrics during meetings is useless unless specific individuals are responsible for taking corrective action.

A global CPG firm across 40 markets experienced declining complaint resolution rates. Five departments blamed each other, resulting in zero operational accountability despite having numerous data stewards.

Resolving this requires an Ownership Map, which establishes three parameters:

• Named Outcome Owner: A single person accountable for metric movements.
• Required Action: Pre-defined operational responses to metric changes.
• Significant Thresholds: Clear limits defining when escalation is required.

Key Takeaways

• Single-Person Ownership: Metric accountability must belong to one named individual, never a committee.
• Ownership Map: Defines who acts, what action is taken, and what threshold triggers response.
• Incremental Scope: Begin governance by assigning owners to eight key business metrics.

Frequently Asked Questions

• What is the difference between a data owner and a metric owner? A data owner manages data quality and infrastructure, whereas a metric owner is responsible for operational outcomes when figures change.

What Is True Data Literacy and Its Importance?

True data literacy is the psychological confidence to state data insights publicly and drive change, not mere technical tool operation.

Traditional corporate data training focuses on software certifications and completion rates, failing to change daily decision behaviour.

A B2B client conducted a Fluency Assessment before designing training, uncovering three distinct workforce cohorts:

• High Skill / Advocates: Technical champions who lead internal peer communities.
• Skill Gap Learners: Employees requiring structured foundational training.
• Invisible Group: Employees with high technical skills who lack confidence to speak up.

Tailoring learning paths to these cohorts increases peer-led scaling and decision confidence.

Key Takeaways

• Beyond Tool Mastery: Literacy requires confidence to challenge decisions using data, not just operating software.
• Fluency Assessment: Maps workforce technical skills against usage confidence to design targeted learning.
• Cohort Customisation: Differentiates advanced advocates, foundational learners, and unconfident skilled workers.

Frequently Asked Questions

• Why do mandatory software training programs fail to improve data culture? They measure completion rates rather than assessing whether employees feel confident using data to drive business decisions.

How Can We Target Operational Friction Points Effectively?

Sustained data culture transformation relies on starting small at acute friction points and compounding trust through iterative questioning.

Cultural integration requires acknowledging that all four REAL pillars are interconnected. Rather than launching massive enterprise transformation programs, organisations should target immediate problems.

Key implementation principles include:

• Targeted Interventions: Begin where operational friction is highest, and control is achievable.
• Differentiated Education: Adapt frameworks like CDMP to address specific cohort confidence levels.
• Compounding Inquiry: Analysts build stakeholder trust by asking concise, clarifying brief questions that refine decision focus.

Key Takeaways

• Iterative Entry: Start small with manageable metric friction rather than attempting total enterprise overhaul.
• Connected Cultural Levers: Relationships, goals, accountability, and literacy reinforce each other over time.
• Trusted Advisory: Asking insightful questions moves data analysts from order-takers to strategic advisers.

Frequently Asked Questions

• Where should an organisation begin when deploying the REAL framework? Start immediately with the single business area experiencing the highest operational pain where immediate influence is possible.