We want to understand the specifics of the company, the current operating methods, how data analyses are implemented now, and the technologies that the client uses.

In this way, we can incorporate the client’s unique characteristics into the Data Literacy program to further engage participants in the program.

This part is necessary only once, even though the program will be implemented for multiple groups of participants within the company.

1. Data Literacy: Presentation and Importance in Companies

Data Literacy is a program aimed at enhancing skills in data reading, working with data, data analysis, and processes to acquire data.

In this Part I we will cover:

2. Data Awareness involves realizing the importance of data at the organizational level.

Topics:

• What do we rely on when making a decision?
• What are the concrete steps in the decision-making process?
• Why are some decisions favorable while others are not?
• How do we prevent unfavorable decisions?

The implementation for this part will be achieved through several concrete case studies.

In order to create data awareness we need to start from personal life.

Examples:

• • From personal life when we make decisions (in our personal life we buy things all the time. What are the most important aspects when we decide to buy or not something that is very important, something that is nice to have – an open discussion that will help us build a list of all the factors considered when making a decision), which will later be mapped onto the necessary steps for decision-making in business.
  • From the workplace:
    • When deciding on a promotion across all stores, how do we ensure its success? We study historical sales data, organize a pilot program for 10% of the stores, and then expand to all stores—at each stage, there will be a reporting phase that we will implement practically.

This part can be assumed as coaching short program in order to define together, trainer & participants why data is the most important asset today in business.

All the case studies will be defined after the Part I when we know better the company.

3. Data Ethics includes also aspects of GDPR, but we will focus on key points in delivering reports and data analyses:

• Accuracy of data source
• Accuracy of reports and identification of verification keys

By the end of Stage 1, we will have:

• A list of concrete steps that we need to take in making a decision, steps which we will define together and later map onto a standard list of steps.
• Several methods for verifying the accuracy of the data/reports.

Part II is dedicated to the importance of data in the area of Data Analysis & Analytics. We will cover several important sections, and the implementation process will always start from the results (what we want, what the final result looks like, and where the final result is used) to the technical implementation.

a)     Data Analysis Techniques / Reporting / Visualization:

We will cover all types of data analysis:

• Data Sources for Business: files, databases, BI tools
• Cumulative and selective analysis
• Comparative and trend analysis
• Past versus Predictive analysis
• Graphical representations – best practices
• Results interpretation

To optimize reporting techniques, we will additionally analyze:

• Optimization of data sources: what is the optimal data period to construct the four types of analyses using the same dataset
• What are the most requested reports and how we can optimize both data sources and reports
• How we use the same dataset for both summarizing data and obtaining details

Practical Exercises:

1. Create four types of reports in Excel using the same dataset.
2. Automate reports using Power Query in Excel even if the participants didn’t work with Power Query in Excel.
3. Build a dashboard either in Excel or Power BI for graphical representation.
4. Collaboratively create a list of questions for a data analyst.

Individual or Team Exercise:

Participants will have two business cases to analyze, including Part I. To make a decision on store expansion, three reports of different types will be requested. Through this exercise, we will reinforce the concepts learned earlier.

b) Data Terms – Glossary:

We will define and go through practical terms in the field of Data such as:

• A: Analysis, analytics, AZURE, algorithm
• B: BR (Business Request), BA (Business Analyst)
• C: Chart, combo, cluster, catalog
• D: Dataset, database, data lake, dashboard, data mart, data tribe, data lineage
• E: EXCEL, environment
• F: Funnel, FABRIC
• G: Graphic, Gantt
• H: Histogram
• K: Key
• M: Model
• N: Neuronal
• O: One lake
• P: PYTHON, POWER BI, production, POWER Query, POWER Pivot, parameter
• Q: Quarter
• R: R Studio, report
• S: SQL
• T: Tableau, test
• U: UAT
• V: Variable
• W: Warehouse

The list will be defined together with the beneficiary company based on what they currently use and what they wish to learn further.

c) Business Data Roles:

We will identify key business roles that involve working with data:

• Data Analyst
• Business Analyst
• Data Owner
• Product Owner

The list will be defined together with the beneficiary company based on their current roles and additional knowledge they wish to acquire.

Practical Exercise: A quiz with 20 questions covering both the glossary and the areas of data analysis and roles.

At the end of Part III participants will be able to implement and recognize:

1. –       All the types of data analysis
2. –       Data Terminology
3. –       Data roles in Business and the connection between them