How do we function with advanced data analytics, in WaysAhead Global?

List of 100 tasks that helped us to excel and scale in 5 countries.

Let’s start and focus on what’s important. You might have come across thousands of articles, which just outlines and touch topics in a vague term. We are hereby doing an attempt to talk to the point.

Focus of what’s important: Descriptive Analytics (Tasks 1-28) - Understanding and summarizing historical data:

1. Define the business problem and objectives clearly.

2. Identify and list all potential data sources.

3. Gather raw data from identified sources.

4. Conduct initial exploration to understand the available data.

5. Create a comprehensive inventory of collected data (Our secret task, most companies skip this task)

6. Clearly define the scope and goals of your data analytics project.

7. Assess the feasibility of solving the problem with data analytics.

8. Formulate hypotheses based on initial data insights.

9. Plan the methods and tools for data collection.

10. Set up data collection mechanisms and processes.

11. Collect and organize the raw data.

12. Profile the collected data to identify its characteristics.

13. Detect and document data quality issues.

14. Maintain a detailed record of data source details and provenance.

15. Plan data cleaning procedures and establish data cleaning standards.

16. Address missing values through imputation or deletion (Our secret task, most companies don’t address this)

17. Identify and treat outliers to ensure data integrity.

18. Standardize data formats and units for consistency.

19. Remove duplicates and handle data duplication issues.

20. Resolve data inconsistencies and discrepancies.

21. Tackle data encoding problems, such as character encoding (Our secret task, most companies don’t handle this)

22. Normalize numerical data for consistent scaling.

23. Eliminate irrelevant data to focus on relevant information.

24. Impute missing data using appropriate methods.

25. Validate data against predefined business rules.

26. Transform data to prepare it for analysis.

27. Aggregate data if necessary for higher-level insights.

28. Deduplicate data to ensure each record is unique.

Making sure we are on the right track: Diagnostic Analytics (Tasks 29-42) - Exploring data relationships and patterns:

29. Develop a data enrichment strategy to enhance your dataset.

30. Retrieve relevant external data sources.

31. Merge external data with your existing dataset.

32. Extract meaningful features that drive insights.

33. Create new derived variables based on domain knowledge (another trade secret, connect with us to learn more)

34. Calculate descriptive statistics to understand data distribution.

35. Generate data summaries to highlight key statistics.

36. Perform dimensionality reduction to simplify complex datasets.

37. Apply feature scaling techniques for modeling purposes.

38. Handle categorical data through encoding methods (secret task, companies usually skip this task)

39. Engineer new features for better predictive power.

40. Create time-series features for temporal analysis.

41. Augment data with external sources to enrich context.

42. Document your data enrichment procedures for transparency.

It’s time to open the Pandora’s Box: Predictive Analytics (Tasks 43-70) - Building predictive models and making forecasts:

43. Select appropriate modelling techniques based on the problem.

44. Split the dataset into training and testing subsets.

45. Preprocess data specifically for modelling.

46. Choose relevant evaluation metrics for model performance.

47. Train initial machine learning models using training data.

48. Fine-tune model hyperparameters for optimization.

49. Evaluate model performance on the testing dataset.

50. Implement cross-validation techniques for robust evaluation.

51. Address overfitting or underfitting issues in models.

52. Explore ensemble modelling methods for improved accuracy.

53. Select the most important features for model training (Shhh! don’t tell this)

54. Interpret model results to understand feature importance.

55. Validate models with domain experts and stakeholders.

56. Maintain detailed documentation of the modelling process.

57. Conduct exploratory data analysis to understand data patterns.

58. Perform hypothesis testing to confirm data relationships.

59. Analyse correlations and causations within the data.

60. Identify and visualize trends over time (very important, secret task, often companies overlook)

61. Conduct advanced statistical analyses when necessary.

62. Cluster data to discover hidden patterns or segments.

63. Apply time-series analysis techniques to temporal data.

64. Conduct sentiment analysis on textual data (if applicable).

65. Perform regression analysis to predict continuous outcomes.

66. Execute classification analysis for categorical predictions.

67. Evaluate model predictions against actual outcomes.

68. Extract actionable insights from model results.

69. Summarize key findings and their implications.

70. Validate analytical results with stakeholders.

Our entire DA team has mastered this art and science: Prescriptive Analytics (Tasks 71-100) - Providing actionable recommendations:

71. Create data visualizations to communicate insights effectively.

72. Select appropriate chart types to convey information clearly.

73. Design interactive and user-friendly dashboards.

74. Visualize trends and patterns over time.

75. Use colour coding judiciously for emphasis and clarity (can’t reveal this technique).

76. Label visualizations clearly to aid understanding.

77. Develop geographic visualizations for location-based insights.

78. Construct histograms, distributions, and density plots.

79. Design scatter plots and heatmaps for data exploration (we bet you don’t practice this secret recipe).

80. Utilize bar charts and pie charts to showcase proportions.

81. Visualize model performance metrics for assessment.

82. Incorporate storytelling elements to engage the audience.

83. Share visualizations and insights with stakeholders.

84. Craft a data-driven narrative to tell a compelling story (we take pride).

85. Structure a well-documented report with clear sections.

86. Tailor the story to the specific audience's needs.

87. Support key points with data and visual evidence.

88. Incorporate relevant visualizations to reinforce insights.

89. Highlight significant insights and emerging trends.

90. Connect findings directly to business objectives.

91. Provide actionable recommendations based on insights.

92. Craft a narrative that captivates and educates the audience.

93. Practice effective communication techniques.

94. Prepare to answer questions and engage in discussions.

95. Present findings to stakeholders in a clear and engaging manner.

96. Collect feedback from stakeholders and data consumers.

97. Iterate on your analysis and presentation based on feedback.

98. Maintain comprehensive documentation of the storytelling process.

99. Establish data governance policies to ensure data quality (our cherry on top, a secret recipe)

100. Implement robust data security and compliance measures.

This comprehensive breakdown of tasks, categorized by analytics stages, will help you navigate the advanced data analytics process effectively and produce actionable insights for informed decision-making.