Data Visualization and Interpretation.pptx

Data Visualization and Interpretation.

1. TITLE: DATA VISUALIZATION AND INTERPRETATION PRESENTED BY: MR. DARWIN MONG’ARE DATE: 18 TH SEPT. 2025

2. DATA VISUALIZATION AND INTERPRETATION

3. We are learning to understand the purpose and benefits of data visualisation create clear and accurate data visualisations choose the correct chart type for various data relationships. We can explain why data visualisation is essential for uncovering trends and communicating insights design visualisations to ensure clarity that focus on key data implement appropriate chart types to communicate data relationships critically assess visualisations for clarity, accuracy and potential bias.

4. Data Visualization The art and science of transforming raw data into visual formats like charts, graphs, and maps. It makes complex data accessible and understandable . Data Interpretation The analytical process of examining these visuals to extract meaningful insights, patterns, and trends. It turns understanding into actionable intelligence.

5. Why visualise data ? Data is only as good as our ability to understand and communicate it Data visualisation helps: ​ uncover trends and patterns visualisations help spot trends and patterns in data that might be missed in spreadsheets or raw numbers. ​ make data accessible charts and graphs translate complex data sets into a format that's easier to understand for a wider audience. ​ Cont ,

6. C ommunicate insights clearly V isuals can present findings in a compelling way, making it easier to share data stories and recommendations . S park action and decision making B y highlighting key trends, visualisations can guide better decision-making based on clear data insights.​ M ake data memorable Information presented visually is more likely to be remembered and recalled.

7. General design tips Make sure you do the following Ensure focus on the data and tone-down or remove the non-data elements such as grid lines, axis lines, chart borders and colour that does not have a purpose . Label it up and always include clear and concise legends and data labels to provide context and ensure viewers understand what they're looking at. ​ Cont ,

8. Make sure you do the following Take a step back and squint at your visualisation. If the overall message is still clear without reading any labels, you're on the right track.​ Get feedback. Don't be afraid to ask for fresh eyes! Sharing your visualisation with others that can help you identify areas for improvement and ensure your message is clear. General design tips

9. Make sure you do the following Avoid clutter by adding too much information to a single chart as this eliminates the advantages of processing data visually​ . Avoid 3D visualisations as they can be visually distracting and make it difficult to compare data points accurately. Stick to simpler 2D charts for better clarity. ​ General design tips

10. Make sure you do the following Use ‘bad’ colour combinations. Always try and avoid harsh colour combinations such as red/green or blue/yellow. Don't make users perform visual or mental calculations to interpret your visualisation. If the chart is complex, break it down into separate visuals and reduce the viewer's cognitive load. General design tips

11. PRINCIPLES OF EFFECTIVE DATA VISUALIZATION GUIDELINES TO ENSURE YOUR VISUALS ARE CLEAR, ACCURATE, AND IMPACTFUL.

12. Principle Description Why It Matters Clarity Avoid clutter; make the core message obvious. Prevents confusion and ensures the audience grasps the key takeaway immediately. Simplicity Use the simplest chart that effectively conveys the point. Reduces cognitive load. Don't use a 3D pie chart when a bar chart will do. Accuracy Scale visuals appropriately; don't distort the data. Maintains integrity and trust. Misleading visuals lead to flawed decisions.

13. Principle Description Why It Matters Consistency Use standard colors, fonts, and labeling across all visuals. Creates a professional look and allows the audience to focus on the data, not the design. Relevance Only include information that supports your message. Eliminates noise and directs attention to what is truly important.

14. DATA CHARTS

15. A data chart is a graphical or visual representation of data. It translates complex numerical information and relationships into a visual format, making patterns, trends, and outliers easier to see and understand. In simple terms: It’s a picture of your data. Why Use Charts? From Data to Insight Charts serve four primary purposes: Simplify: Break down complex datasets into digestible visuals. Compare: Show differences and similarities between values. Reveal Trends: Illustrate how data changes over time. Show Relationships: Demonstrate how variables interact with each other. Goal: To facilitate faster and more accurate decision-making.

16. T YPES OF CHARTS

17. Bar Graphs These are one of the most commonly used types of graphs for data visualization. They represent data using rectangular bars where the length of each bar corresponds to the value it represents.

18. Line Graphs These are used to display data over time or continuous intervals . They consist of points connected by lines, with each point representing a specific value at a particular time or interval. Line graphs are useful for showing trends and patterns in data.

19. Different Types Of Charts For Data Visualization Pie Charts These are circular graphs divided into sectors, where each sector represents a proportion of the whole. Pie charts are effective for showing the composition of a whole and comparing different categories as parts of a whole.

20. Scatter Plots These are used to visualize the relationship between two variables. Each data point in a scatter plot represents a value for both variables, and the position of the point on the graph indicates the values of the variables. Scatter plots are useful for identifying patterns and relationships between variables, such as correlation or trends.

21. Area Charts They are used to represent cumulative totals or stacked data over time. Area charts are effective for showing changes in composition over time and comparing the contributions of different categories to the total.

22. Radar Charts A lso known as a spider chart or a web chart, is a graphical method of displaying multivariate data in the form of a two-dimensional chart. It is particularly useful for visualizing the relative values of multiple quantitative variables across several categories.

23. Pareto Charts This is a specific type of chart that combines both bar and line graphs. It's named after Vilfredo Pareto, an Italian economist who first noted the 80/20 principle, which states that roughly 80% of effects come from 20% of causes . Pareto charts are used to highlight the most significant factors among a set of many factors.

24. Histograms These are similar to bar graphs but are used specifically to represent the distribution of continuous data. In histograms, the data is divided into intervals, or bins, and the height of each bar represents the frequency or count of data points within that interval.

25. TOOLS FOR DATA VISUALIZATION & DASHBOARDS

26. 1: Spreadsheets (The Foundation ) Purpose: Versatile and accessible tools for quick analysis, basic charts, and simple dashboards. Best for: Quick , simple charts; data cleaning and basic analysis; universal accessibility .

27. Tool Key Features Best For Microsoft Excel Incredibly ubiquitous, vast chart types, PivotTables/ PivotCharts , Power Query. Everyone. Quick ad-hoc analysis, reporting, and widely shared files. Google Sheets Real-time collaboration, cloud-native, easy sharing, built-in exploration tools. Collaborative projects, simple shared dashboards, and cloud-first teams.

28. 2: Business Intelligence (BI) & Dashboarding Best for: Purpose: To connect to various data sources, model data, and create interactive dashboards for sharing across an organization. Connecting to live data sources; building interactive, enterprise-grade dashboards; self-service analytics for business users .

29. Tool Key Features Best For Tableau Powerful drag-and-drop interface, superior visual design, high interactivity. Enterprises, analysts focused on deep exploration and beautiful visuals. Microsoft Power BI Deep integration with Microsoft ecosystem, strong self-service capabilities, cost-effective. Organizations using Azure & Microsoft 365; strong overall value. Qlik Sense Associative data model, great for discovering hidden trends, strong governance. Users who need to explore data freely without predefined queries. Looker (Google Cloud) Uses a custom modeling language ( LookML ), embedded analytics, cloud-native. Tech-savvy teams that need customized, embedded analytics solutions.

30. 3: Programming Languages (Maximum Flexibility ) Purpose: To build custom, reproducible, and highly specific visualizations directly with code. Offers maximum flexibility. Best for: Statistical analysis; automated reporting; custom and complex visualizations; reproducibility.

31. Tool / Library Key Features Best For Python ( Matplotlib , Seaborn , Plotly ) Matplotlib is highly customizable. Seaborn for statistical plots. Plotly for interactivity. Data scientists, developers building custom applications and advanced analytics. R (ggplot2) Based on "Grammar of Graphics," creates elegant, publication-quality static visuals. Academic research, statistical analysis, and fields like bioinformatics. JavaScript (D3.js, Chart.js) D3.js offers unparalleled control for web-based visuals. Chart.js for simpler web charts. Web developers creating interactive, web-native data visualizations.

32. How to Choose the Right Tool ? Ask these questions: Who is the audience? (Technical managers vs. C-suite executives) What is the data source? (Static Excel file vs. Live SQL database vs. Cloud platform) What is the goal? (A one-time report vs. an ongoing monitoring dashboard) What is the technical skill of the creator? What is the budget? (Free vs. enterprise licensing)

33. IDENTIFYING TRENDS & PATTERNS IN DATA

34. The "Story" in the Data Raw data is a collection of facts. Trends and patterns are the meaningful stories we extract from that data. They describe relationships, changes, and structures that allow us to understand the past and anticipate the future.

35. In essence, they answer three key questions : What is happening? (Description ) Why is it happening? (Analysis ) What is likely to happen next? (Prediction)

36. What is a Trend? A trend indicates a consistent, long-term upward or downward movement in data over a significant period. It shows the overall direction or tendency . Key Characteristics: Direction: Upward (Increasing), Downward (Decreasing), or Horizontal (Stagnant). Duration: Long-term (e.g., years, quarters). Significance: Represents a fundamental shift, not just short-term noise.

37. Example: "Our company's annual revenue has shown a steady upward trend over the past five years." "The number of physical store visits has been on a consistent downward trend since 2020." Goal of Identification: To understand the fundamental trajectory of a metric and make long-term strategic decisions (e.g., investing in a growing product line, phasing out a declining service).

38. What is a Pattern ? A pattern is any repetitive, recognizable structure or relationship in the data. Patterns are often cyclical and can be short or long-term . Key Characteristics: Repetition: Occurs at regular or irregular intervals. Form: Can be seasonal, cyclical, or based on relationships between variables. Focus: Describes how data behaves, not just its overall direction. How to Identify It: Patterns are identified through visual analysis (e.g., repeating peaks/troughs on a chart, clusters on a scatter plot) and statistical methods.

39. Common Types & Examples: Seasonality: Short-term, regular patterns tied to time (e.g., "Ice cream sales peak every summer."). Cyclical Patterns: Longer-term fluctuations linked to economic cycles (e.g., "Housing sales rise and fall with the GDP."). Correlation: A relationship between two variables (e.g., "As marketing spend increases, website traffic also increases."). Clustering: Groups of similar data points (e.g., "Customer data shows three distinct patterns of purchasing behavior."). Goal of Identification: To predict short-term changes, optimize operations, understand customer behavior, and segment audiences.

40. Feature Trend Pattern Timeframe Long-term Any timeframe (short or long) Nature Overall direction Repetitive structure or relationship Focus "Where is it going?" "How does it behave?" Example Revenue increasing over 5 years Sales spiking every December Key Differences at a Glance

41. COMMUNICATING INSIGHTS THROUGH VISUAL STORYTELLING

42. Visual storytelling is the practice of combining data visuals with a narrative to present insights in a clear, engaging, and persuasive way . Goal : Not just to show data, but to explain what it means, why it matters, and what actions to take.

43. What is Data-Driven Storytelling? It's the art of weaving data, visuals, and narrative into a compelling story that inspires action. It moves beyond simply showing data to explaining what it means and why it matters. Without Storytelling: "Here's a dashboard of last quarter's sales." (Audience Thinks): "So what? What am I supposed to do with this?" With Storytelling: "Last quarter, we gained significant market share in the Midwest. Our story today is about how a localized marketing strategy drove that success and how we can apply it nationally to hit our annual target." (Audience Thinks): "I see! Tell me more."

44. The Anatomy of a Data Story Every effective data story has three core components : 1. Data (The Evidence) The raw material. This is your cleaned, analyzed data and the visualizations you build from it (charts, graphs, maps ). 2. Visuals (The Stage) The presentation of the evidence. This is the thoughtful design of those visuals using principles of clarity, simplicity, and emphasis to guide the audience's eye .

45. 3 . Narrative (The Script) The structure and language that frame the evidence. It provides context, explains conflict, and builds toward a resolution. It answers: What is happening? (The initial situation) Why does it matter? (The conflict or opportunity) What should we do about it? (The resolution & call to action)

46. The Storytelling Framework: A Practical Guide Follow this structure to build your narrative : Hook (The Beginning): Start with a relatable question, a surprising fact, or the core insight to grab attention. Example : " Did you know we're leaving $5M in revenue on the table ?“ 2. Conflict/Quest (The Middle): Present the problem, opportunity, or key finding. Use data visuals as evidence to build your case. Example : " While our overall sales are flat, this chart reveals a hidden gem: a 300% growth in a specific customer segment we've been ignoring ."

47. 3 . Resolution (The Ending): Reveal the solution or answer derived from the data. This is your main insight. Example: " The data shows this growth is directly tied to our recent content marketing efforts .“ 4. Call to Action (The Next Chapter): Clearly state what you want the audience to do, decide, or believe based on the story. Example: " I recommend we allocate an additional 20% of our Q4 budget to content marketing to leverage this proven strategy."

48. CASE STUDY: SAFARICOM PLC

49. Enhancing Commercial Decision-Making with a Real-Time Sales Performance Dashboard Company: Safaricom PLC Industry: Telecommunications , Mobile Money (M-PESA ) Challenge: Managing and analyzing sales data across vast retail channels to drive growth and agent network effectiveness.

50. Executive Summary The Challenge: Safaricom's extensive sales operations—spanning direct sales, dealer networks, M-PESA agents, and retail outlets—generated massive, siloed data. Regional managers lacked timely insights, leading to delayed decisions on agent support, stock allocation, and promotional campaigns. Performance reporting was a manual, weekly process, hindering proactive management .

51. The Outcome: 20% reduction in time spent on manual reporting. 15% increase in active M-PESA agent performance within three months due to targeted interventions. Improved stock allocation, reducing out-of-stock scenarios by 30% in key regions. Empowered regional managers with real-time insights for faster decision-making. The Solution: A centralized, interactive Sales Performance Dashboard built in Microsoft Power BI, integrating data from multipe sources (SAP, CRM, M-PESA transaction logs) into a single source of truth.

52. The Business Problem & Objectives Background: As Kenya's leading telecom operator, Safaricom's commercial success relies on a complex distribution network. Understanding regional, dealer, and agent-level performance in near real-time is critical for maintaining a competitive edge . Key Business Problems : Data Silos: Sales , agent, and airtime credit data resided in separate systems, making consolidated analysis difficult and time-consuming .

53. Delayed Reporting: Manual Excel-based reports were outdated by the time they were distributed, causing reactive instead of proactive management. 3. Ineffective Targeting: Inability to quickly identify underperforming agents or regions to deploy support teams and resources effectively . Project Objectives: Automate Reporting: Create a single source of truth that updates daily. Enable Drill-Down Analysis: Provide insights from a national level down to an individual agent level. Identify Trends & Patterns: Track Key Performance Indicators (KPIs) over time and across regions. Improve Actionability : Equip regional managers with tools to make data-driven decisions swiftly.

54. KPI Category Specific Metrics Purpose Overall Performance Total Revenue, Sales Volume vs. Target, YoY Growth % Track overall health of sales operations. Regional Analysis Revenue by County, Top 5 Performing Regions, Bottom 5 Performing Regions Identify geographic strengths and weaknesses. Agent/Dealer Performance M-PESA Transaction Value per Agent, Airtime Sales by Dealer, Activation Rates Rank partners to target support or rewards. Temporal Trends Sales by Week/Month, Seasonal Patterns, Moving Averages Forecast demand and prepare for peaks (e.g., holidays). Product Performance Revenue by Product (Voice, Data, M-PESA, Fuliza ) Inform product strategy and marketing focus. Key Performance Indicators (KPIs) Visualized: