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iReuby

@Magnanimo_s

Sep 21

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

iReuby

@Magnanimo_s

Sep 19

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

iReuby

@Magnanimo_s

Sep 5

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

iReuby

@Magnanimo_s

Sep 2

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.

https://twitter.com/Magnanimo_s/status/1561991938055749632?s=20&t=28dktPmHm0qiSUXev6Qq0g

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.

https://twitter.com/Magnanimo_s/status/1561707153848762371?s=20&t=28dktPmHm0qiSUXev6Qq0g

Read 6 tweets

iReuby

@Magnanimo_s

Aug 23

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

iReuby

@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

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