iReuby Profile picture
Jan 29 10 tweets 3 min read
Hello Data Analyst,

Do you want to captivate your stakeholders and keep them glued to your reports?

Use these 4 story arcs to transform your storytelling skill. The 4th story arc is my favourite.
A story arc is the overall structure of a story while a plot diagram is the visual representation of the structure.

Story arcs can help data analysts to structure their data-driven stories in a way that is interesting and easy for their audience to understand.

Shall we?
There are different types of story arcs/plot diagrams but I will be discussing my favourite 4.

1. Rags to Riches Plot Diagram: this story structure describes the journey of a character/company from a state of hardship (rags) to a state of success (riches).
Start by stating the current challenges, then show the company achieving success with the help of insights gleaned from data and your turnaround strategies.

Use 5W 1H (What, Why, Where, Who, When, and How) to create the contents.
2. The Man in the Hole Plot Diagram: describes a character/company who finds themselves in a difficult situation, often literally being trapped in a hole, and must find a way to escape or survive.
Show how the firm was doing well but gradually got trapped due to business/market realities. Show how they can survive or escape using your insights and strategies.

3. The Icarus Plot Diagram: used to convey the theme of the dangers of ambition and overreaching...
...and to highlight the importance of humility and self-awareness.

Imagine a start-up or an upcoming artiste who got some support and became very successful that it gets to their heads & they become proud.

Use this to show how the firm has progressed and the dangers should...
...they fail to sustain the upward trajectory due to their hubris.

4.The Cinderella Plot Diagram: this story structure describes a character/company who goes through a transformation from a state of hardship to a state of success.

It often conveys...
...themes of transformation, perseverance, and the power of hope.

Using insights gleaned, acknowledge the current challenges and paint the transformation journey towards becoming a successful company. Again, use the 5W & 1H to create the contents.
Story arcs help you to logically structure your report.

Whatever style you choose, be sure to capture the key needs of the stakeholders by showing past/current realities, expected future state & how to get there (5W & 1H).

Tell more engaging stories with #data!

#Storytelling

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More from @Magnanimo_s

Oct 11, 2022
Hello Data Analyst,

Are you researching on what issues or solutions to address?

These are the top five global issues at the moment.

1/4
Use the acronym "ADAPT" to remember them.

A – Asymmetry: The world has a wide distribution of wealth. The gap between the rich and the poor is very wide.

D – Disruption: Rapid technological changes and its implications.

A – Age: Lots of demographic pressures. E.g. Japa?

2/4
P – Populism: Growing populism and rejection of status quo. (E.g. OBIdients movement?)

T – Trust: As a result of the constant erosion in confidence, wars have been sparked and relationships have suffered.

Which do you consider most pressing and should be prioritized?

3/4
Read 4 tweets
Sep 21, 2022
Hello Data Analyst,

Let’s talk about our tools.

The best tool for data analysis is your brain. Develop it.

Different firms use different tools. Having the data analysis skills is material so you can apply them to any BI tool.

The tools listed here are not endorsements.
1. Database Systems. For creating, extracting & maintaining data from databases.
E.g. Microsoft Access, MySQL, PostgreSQL, Microsoft SQL Server, IBM DB2, Oracle, Teradata etc.
2. Standard Reporting: Used to manipulate or show data in a consistent, repeated manner.
E.g. Microsoft Excel, Microsoft SQL Server, Oracle OBIEE, Cognos, MicroStrategy etc.
Read 6 tweets
Sep 19, 2022
Hello Data Analysts,

Let’s talk about data quality.

Bad data can have a big impact on a company's bottom line.

Poor-quality data is frequently blamed for operational blunders, incorrect analytics, and poorly thought-out company initiatives.

What should be reviewed?
Organizations can detect data mistakes that need to be fixed and determine whether the data in their IT systems is suitable for the intended use by measuring data quality levels.

1. Check for completeness/uniqueness. Presence of missing data? Are data entries duplicated?
2. Check for accuracy and consistency. Are the formulas correct and consistent? Are the entered Data accurate?

3. Check for conformance and validity. Do the data meet required specifications?

4. Timeliness. Is it up to date? Is it readily available?
Read 5 tweets
Sep 5, 2022
Hello Data Analyst,

Have you ever heard a BI Developer or a Software Developer discuss development (dev), testing, and production (prod) environments?

In this short thread, I will explain them to you.
Dev (development) environment

This is where new reports, ideas, and modifications are created. What is going on here has no bearing on the business process.
Testing environment

The newly created or modified reports or functionalities are tested here before being made available to the end user.

SIT (System Integration Test), UAT (User Acceptance Test), and other terms are used.
Read 7 tweets
Sep 2, 2022
Hello Data Analyst,

Have you heard of the acronym ALM?
(Application Lifecycle Management)

It is a process that enables you to design, build, test, & distribute BI reports effectively & efficiently.

Let's go over the six main stages. Use the acronym ADDTDM to remember them.
1. Analysis – Gather & analyze business requirements to define the report's goals. Clarify the problem statement & the analysis's purpose.

Struggling with this? See some useful questions to guide you in my previous post.

2. Design – Plan, prepare your data, create the data model and user interface for the report.

Don’t know what a data model is and its benefits? Check this previous post.
Read 6 tweets
Aug 23, 2022
Hello Data Analyst,

I know how difficult it can be to ask the right questions that will generate the key insights you need to solve a problem or make a data-driven decision.

Here are 20 key questions to help you understand and solve problems with data.
1. Why was I asked to review this? – Problem Statement/Purpose

2. What does the product/process do? – Domain knowledge

3. Where are we now? – Actual Performance

4. Where should we have been? – Plan/budget

5. Did we achieve what we planned to achieve? – Variance Analysis
6. Why did we not achieve what we planned to achieve? – Root cause analysis

7. Where are the gaps in our process/strategy? – Gap Analysis

8. What did we not consider before? – Gap Analysis

9. What has changed within this period? – Trend Analysis
Read 7 tweets

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