iReuby Profile picture
Aug 23 7 tweets 2 min read
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
10. Where do we want to be by XYZ time? – Vision/Predictions

11. What do we need to do to get there? – Prescriptions

12. What are the quick wins (easy to implement at low cost)? – Low hanging fruit

13. What if thing(s) don’t go as planned? – Sensitivity & Scenario Analysis
14. What are the specific success factors? – Detail-Oriented Thinking

15. Does the cost of implementing these ideas match the benefits? – Cost-Benefit Analysis

16. Who should be accountable for implementing this? – Responsibility Assignment
17. Who is drawing this product/process backwards? – Stakeholder Analysis

18. What will happen for me to know the business objectives were met? – Acceptance criteria

19. How do we know that the full benefits for our efforts were achieved? – Benefits Realization Tracking
20. How does this product/process/solution fit our overall ambition/strategy? – The big picture

Is there a question you believe should be added to this list? Please share.

Go solve problems leveraging insights from data!

🥂

#Data #ProblemSolving #KeyQuestions #Analytics

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More from @Magnanimo_s

Aug 22
Hello Data Analyst,

Let's discuss data models & benefits of having solid data models.

One of a data analyst's key responsibilities is building a strong data model.

The term "Data Model" refers to the process of arranging data into tables based on relationships & groups...
...to minimize duplication and maximize efficiency.

By performing this task properly, you contribute to making it simpler for people to comprehend your data, which will make it simpler for both you and them to create useful reports and dashboards.
It is challenging to provide a set of guidelines for what constitutes a good data model because every piece of data is unique and is used in different ways.

A smaller data model is preferable because it will operate more quickly and be easier to use.
Read 5 tweets
Aug 18
Hello Data Analyst,

Let’s talk about #dashboards.

They assist you to share insights effectively to guide decision makers.

If done with care, they are visually appealing & tell insightful stories about the data.

10 things you should bear in mind when designing a dashboard...
1. Design for a target. Who will use the dashboard? What are their needs? Do the insights in the dashboard address the needs?

2.Pick the right charts.

See my previous tweet on visualization.
3. Keep it simple. How easy is it to access key insights? Leave out the noise.

4. Keep everything at a glance. Don’t overcrowd the dashboard. Enhance accessibility.

5. Show important context – Filters, tooltips, chart titles, descriptions etc.
Read 6 tweets
Aug 16
Hello Data/Business Analyst,

You know how frustrating change of requirements from stakeholders can be.

Besides following a clear change management process, developing mental agility will boost your efficiency and overall productivity.

What is mental agility?
Mental agility is the ability to think and apply insights quickly from one context to another.

In simpler terms, it is how well your mind can quickly adjust to new conditions/ideas.

Thankfully, your mental agility can be improved.

Use these 5 simple ways to get started.
1. Be curious. Ask “Why” & “What If”.
2. Read, observe & listen. Read widely & learn to listen to understand not to reply.
3. Be less defensive. Have an open mind.
4. Schedule time to meditate & think.
5. Gain domain knowledge. This helps to understand other possible use cases.
Read 4 tweets
Aug 15
Hello Data Analyst,

A soft skill you should develop is problem-solving.

How do one solve a problem if one can’t identify the root cause?

There are many root cause analysis tools. In this thread, I have shared my 3 favourite ways of identifying the root cause of a problem.
1. 5 WHYs: Asking “Why” multiple times to drilldown to the root cause of the problem. Recall diagnostic analytics.

E.g. Manchester United lost again last week 😭. Why? The players were uncoordinated, and defense was weak. Why?... Why?...
2. By asking the question “When does it happen and when does it not happen?”

E.g. I can’t make a call with my phone.
Can I make a call when I insert my SIM in another device? If yes, that means the fault is from my phone and not the network provider.
Read 5 tweets
Aug 13
Hello Data Analyst,

Let’s talk about visualization.

Your visuals should convey messages to users in an effective & efficient way.

Try to strike a balance btw creating a beautiful visual & having an informative visual.

Thread contains recommendations 4 some use cases.

1/17
1. Changes over time. E.g. Last month, you entered a supermarket and saw someone screaming “God abeg” and you ask your data analyst friend… “What was Nigeria’s headline inflation from Jan – Jun 2022?”

Need: To display changing TREND of measures (prices).

2/17 Nigeria's Headline Inflation Between Jan - Jun 2022
Changes over time (contd.)

Recommendations: Line chart (my favourite), Area chart, Sparkline by OKViz, Card with States by OKViz, you could also try a combination of column chart and line chart if you have different sets of values.

3/17
Read 17 tweets
Aug 12
Hello Data Analyst,

Are you aware of the 5 types of analytics?

In the order of increasing difficulty & value:

1. Descriptive Analytics: Answers questions about what has happened. E.g. Man Utd lost to Brighton last weekend. The match ended 1-2 in favour of Brighton.

1/5
2. Diagnostic Analytics: Helps answer questions about why things happened. E.g. Man Utd lost the game because the players made mistakes on the ball & organisation mistakes in defending.

2/5
3. Predictive analytics: Helps answer questions about what will happen in the future. E.g. Despite the first loss, current issues in the dressing room & with the quality of players and coaching, Man Utd will finish top 4 in this season’s EPL.

3/5
Read 5 tweets

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