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Data Strategy
Connect data collection, analysis and reporting with the decisions the organisation actually needs to make.
Data Analytics & Decision Intelligence
Turn data into information, insight and better decisions.
Organisations often have more data than ever, but having data is not the same as understanding what it means or knowing what action to take.
Denise works across data analytics, statistics, reporting, visualisation and decision intelligence to help businesses and professionals extract useful meaning from information.
Analytics Services
Understand what the data is actually telling you.
Data work can range from understanding an individual dataset to improving the way an organisation measures, reports and makes decisions.
02
Business Analytics
Examine operational, commercial or organisational information to identify useful patterns and insights.
03
Statistical Analysis
Apply appropriate statistical approaches to understand relationships, patterns, differences and evidence within data.
04
Exploratory Data Analysis
Investigate datasets systematically before assumptions are made about what the information means.
05
KPI Development
Identify measures that genuinely reflect performance, progress or business objectives.
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Dashboard Strategy
Determine what should be monitored, how it should be presented and who needs the information.
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Reporting Improvement
Improve reports so that important information is clearer, more relevant and easier to act upon.
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Data Visualisation
Communicate complex information using visual structures that support understanding rather than simply adding more charts.
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Decision Intelligence
Connect evidence, analysis, business context and human judgement to support stronger decisions.
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Customer Analytics
Explore customer-related information to support understanding of behaviour, experience or business needs.
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Operational Analytics
Analyse operational information to understand activity, efficiency, bottlenecks and opportunities.
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Market & Business Analysis
Combine quantitative evidence with business context to support strategic understanding.
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Data Interpretation
Translate analytical findings into language that decision-makers and stakeholders can understand.
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Data Quality Review
Examine whether the available information is suitable, complete and meaningful enough for the intended analysis.
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Analytical Problem Solving
Structure unclear business questions into measurable analytical problems.
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Data Literacy
Help professionals and teams understand, question and use data with greater confidence.
Business Questions
Analytics should answer a useful question.
Good data analysis starts by understanding what needs to be known or decided.
What is actually happening?
Establish meaningful measures and understand performance rather than relying on assumptions.
Is something improving or deteriorating?
Examine patterns and trends over time to identify meaningful movement.
Where are the important differences?
Compare groups, periods, products, locations or activities where appropriate.
What may be influencing the result?
Explore relationships and potential explanatory factors within the available evidence.
What are customers telling us?
Use customer data and feedback to improve understanding of behaviour and experience.
What should we do next?
Translate analytical findings into evidence that can inform practical business decisions.
Analytical Approach
Question first. Analysis second.
01
Define
Clarify the business question, objective and decision that the analysis needs to support.
02
Understand
Examine the available data, its quality, limitations and relevance.
03
Analyse
Apply suitable analytical or statistical methods rather than analysis for its own sake.
04
Interpret
Connect the results with business context, implications and possible action.
Data + AI
AI does not eliminate the need for good data thinking.
Artificial intelligence can increase the speed and accessibility of analysis, but organisations still need to understand their data, ask appropriate questions and interpret results critically.
Denise's work across both AI and data analytics enables the two areas to be considered together where appropriate.
Strong data thinking asks:
- Where did the data come from?
- Is the data suitable for this question?
- What does the measure actually represent?
- Are we confusing correlation with causation?
- What information may be missing?
- How should uncertainty be interpreted?
- What decision does this analysis support?
Data Training
Build confidence working with data and analytics.
Personalised data learning.
Private programmes can be adapted to the learner's existing knowledge, role and practical objectives.
Introduction to Data Analytics
Business Data Analysis
Statistics
Data Visualisation
Excel for Analytics
Python for Data Analysis
Analytics for Managers
Data-Driven Decision Making
Custom Data Programme
Improve data capability across teams.
Corporate programmes can focus on data literacy, analytics, KPIs, visualisation and using evidence more effectively in decision-making.
Organisational Data Literacy
Analytics for Managers
KPI Workshops
Data Visualisation
Data-Driven Decisions
Understanding Statistics
Executive Data Briefings
Bespoke Team Training
Who This Is For
Data support for organisations, teams and professionals.
Business Leaders
Improve the evidence supporting strategic decisions.
Management Teams
Understand KPIs, reports and operational performance.
SMEs
Use available business information more effectively.
Startups
Build measurement and analytical thinking into growth.
Universities
Data, statistics and analytical education.
Researchers
Analytical reasoning, interpretation and data support.
Professionals
Develop practical analytics and data literacy skills.
Innovation Teams
Combine data, AI and business questions effectively.
Discuss Data
What decision are you trying to make?
Start with the question, challenge or information you currently have. The analytical route can then be defined around the actual requirement.
Data Questions
Common starting points.
We have a lot of data but do not know what to do with it. Can Denise help?
Yes. The first step is usually to identify the business questions that the data could help answer, rather than analysing everything simply because it exists.
Can Denise help us define useful KPIs?
Yes. KPI work can focus on identifying measures that genuinely reflect the objectives and decisions relevant to the organisation.
Does data analytics include statistics?
Yes. Statistical analysis may be appropriate where the requirement involves comparisons, relationships, patterns or interpretation of quantitative evidence.
Can Denise help improve dashboards and reporting?
Yes. The focus is not simply on creating more visualisations, but on determining what information should be shown, why it matters and who needs it.
Can data analytics be combined with AI consulting?
Yes. Many AI requirements depend on data, measurement and analytical reasoning, so the two areas can be considered together where appropriate.
Is private one-to-one data training available?
Yes. Private learning can cover selected analytics, statistics, visualisation and data-related subjects.
Can businesses request data training for employees?
Yes. Corporate programmes can be designed around data literacy, analytics, KPIs, visualisation and decision-making.
What should we include in a data enquiry?
Explain the business question, what information currently exists, what decisions need to be made and any difficulties you are experiencing with analysis or reporting.