Data Story Telling

Stephen Mboroto

(0)

Contact for pricing

About this service

Summary

Data: The foundation of any data story is the data itself, which can be derived from various sources. It should be accurate, relevant, and reliable. It's the raw material that the story is built from.

Narrative: The narrative is the storyline that connects the data points. It provides context and makes the data more relatable to the audience. It could be a story of improvement, decline, correlation, causation, etc.

Visuals: Visuals like charts, graphs, and infographics help to represent the data in a more understandable and digestible format. They help to illustrate trends, comparisons, and relationships in the data.

Descriptive Analytics: This is the most basic form of data analytics. It involves understanding and summarizing data to find historical insights about a company's performance. Examples include monthly revenue, average customer spend, etc.

Diagnostic Analytics: This goes a step further to interpret the data for deeper understanding. It involves more diverse data inputs and a bit of hypothesis-making to understand why something happened.

Predictive Analytics: This form of analytics uses statistical models and forecasting techniques to understand the future. It uses the data from descriptive and diagnostic analytics to detect tendencies based on which predictions about future trends can be made.

Prescriptive Analytics: This is a more advanced form of data analytics, which examines data or content to determine what decisions should be made and which steps taken to achieve an intended goal. It's all about providing advice.

Real-time Analytics: As the name suggests, real-time analytics involves analyzing data as soon as it enters the system to provide real-time insights and help in immediate decision-making.

Workflow Automation: It involves streamlining processes for tasks to move more efficiently from one step to the next. It's about eliminating manual, time-consuming tasks like data entry and retrieval, improving efficiency, and reducing errors.

Artificial Intelligence (AI) and Machine Learning (ML): AI and ML can be used to predict future trends, automate decision-making processes, and improve the quality of outputs. They can also be used to learn from past actions and optimize processes over time.

Robotic Process Automation (RPA): RPA uses software bots to automate highly repetitive, routine tasks normally performed by knowledge workers.

Business Rule Engines: These are systems that execute one or more business rules in a runtime production environment. The rules might be quite complex, like if a customer orders more than $100, offer a 10% discount, or simpler ones like all orders must include sales tax.

Integrations: Process automation often involves integrating various systems to ensure smooth data flow and task automation. This might involve connecting an organization's CRM, ERP, and HR systems, for example

What's included

Data Story Telling
Data storytelling is the process of using data and analytics, combined with narrative and visual elements, to effectively communicate insights and findings. It’s a crucial skill in the world of data science and analytics, as it helps bridge the gap between raw data and actionable insights, making data accessible and meaningful to a wider audience.
Data Analytics
Data analytics is the process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves a range of techniques and methodologies to analyze data from various sources in different sizes and formats, to uncover hidden patterns, correlations, trends, and insights.

Example projects

Championing a Data-Driven Culture: Unleashing Power of Analytics

Unleashing Automation and Machine Learning

Skills and tools

Data Scientist

Automation Engineer

Data Analyst