Knowledge Hub

This is also a very confusing term in Data domain, a lot of people are not sure how to assure that everyone follows Ethics in their day-to-day activities while interacting with Data. Just like CDE (Critical Data Element), Data Ethics is also a principle and must be managed at organization level and followed at individual level.

Now, the big question is which data falls under the ambarella where Data Ethics must be followed. There are multiple factors involved and everyone has his/ her own level of ethics to handle based on their roles and responsibilities.

Data Ethics is also a term which if not taken care of properly, can damage a company’s image with the general public. To make sure organizations follow ethics, different regulatory bodies in different parts of the world and in different sectors give guiding principles to follow. It doesn’t stop here, if the organization doesn’t end up following all the measures asked-for by those regulatory bodies then they can be penalized with heavy amounts as penalties.

As the name says all, Data Sharing is about making data accessible to all the relevant stakeholders.

‘Data Sharing is about eliminating Dark Data.’

Data Sharing is one of the key components in Data Driven Organization where organizations aim to build data culture with Data Liberalization.

Data Sharing Channels

• Using Business Intelligence tools
• Using languages like SQL, Python etc.
• Using APIs or direction Using JDBC/ ODBC Connectors
• Using built-in Drivers
• Using Machine Learning or Using Data Mining Models
• Exporting in Excel, CSV, FF formats
• Pushing data to other databases and applications
• Letting other databases and application to Pull data.
• ETL/ ELT tools
• Audit, Compliance or Regulatory Reports etc.

Initially, I planned to write two separate topics on Data Strategy and Data Management but as both are huge areas and the aim of this book is not to train people but rather to make sure readers understand the topics, so decided to combine.

Data Strategy is about defining many elements to make sure when a data solution is implemented, it covers everything to host data, to manage data, to protect data, to maintain data, to make use of data etc.

‘Data Management is one of the Key Element of Data Strategy.’

As the image depicts itself, Data Strategy is about defining all the features and capabilities before the execution process starts. Data Strategy is about tools, processes, implementation planning, maintaining, planning etc., based on business or organization requirements. Data Strategy covers business strategy, organizational roles, data architecture and data management. Data Strategy goals are to innovate, cater to business requirements and address risk and regulatory challenges.

Data Literacy Assessment involves evaluating an individual's ability to understand, interpret, and work with data effectively. It measures how well someone can read, analyze, and draw insights from data.

Let's elaborate on each aspect of data literacy assessment:

• Data Understanding: This aspect assesses an individual's ability to comprehend and make sense of the data presented. It involves understanding the data structure, variables, and context. For example, imagine you have a table with information about the number of apples sold each day at a fruit stand. A data literacy assessment might ask you to look at the table and answer questions like, "On which day were the most apples sold?" This tests your ability to understand the data presented.
• Data Interpretation: It evaluates how well someone can analyze and draw meaningful insights from the data. It includes recognizing patterns, trends, and relationships within the dataset. For example, you are given a chart showing the temperature variations over a week. A data literacy assessment could ask, "What can you conclude about the weather during this week?" This evaluates your capacity to interpret data trends and draw meaningful conclusions.
• Data Presentation: It focuses on the skill of effectively communicating data findings. It involves selecting appropriate visualizations or formats to convey information clearly to a specific audience. For example, suppose you are asked to create a simple bar chart using data about the number of books read by different students. This assesses your ability to choose an appropriate way to present data visually.
• Data Analysis: It evaluates an individual's capability to process and manipulate data to uncover patterns, correlations, and trends. It entails making use of statistical and computational strategies to derive insights. For example, you are provided with a data set on students' test scores and attendance. A data literacy assessment might request you to identify if there's a correlation between attendance and academic performance. This tests your ability to analyze data for patterns and relationships.
• Problem-Solving with Data: This aspect assesses the ability to use data to solve real-world problems. It involves applying data-driven approaches to make informed decisions and address challenges. For example, given data on the growth of plants under different conditions, a data literacy assessment could ask, "Which condition is most favorable for plant growth, and why?" This evaluates your capability to apply information to resolve real-international problems.
• Data Ethics: It examines an individual's understanding of ethical considerations related to data. It involves recognizing and addressing issues such as privacy, consent, and fairness in the handling of data. For example, you are presented with a scenario where a company is collecting customer data. A data literacy assessment might ask, "What ethical considerations should the company take into account when handling this data?" This assesses your understanding of ethical implications related to data.
• Data Communication: It assesses how well someone can convey data insights to others. It entails growing reports, visualizations, or narratives that efficiently talk findings to each technical and non-technical audience. For example, you are given a dataset on pollution levels in a city. A data literacy assessment could ask you to prepare a short report for a non-technical audience, explaining the findings and potential implications. This tests your ability to communicate data insights effectively.
• Critical Thinking with Data: Critical thinking with data evaluates the ability to approach data with a questioning mindset. It involves considering alternative perspectives, asking relevant questions, and identifying potential biases in the data. For example, you receive data about the popularity of different sports in a school. A data literacy assessment might ask, "What other information would you need to determine the factors influencing students' sports preferences?" This assesses your analytical reasoning abilities in consuming data.

In the data world, "Digitalization" and "Digitization" are related concepts, but they have distinct meanings and implications.

Let's decode the difference between the two.

Digitization: It refers to the process of converting analog information into digital format. It involves taking physical or analog data, such as text, images, or sound, and transforming it into digital form, typically represented as binary code (0s and 1s) that computers can understand. For example, scanning a paper document and converting it into a digital PDF file is an example of digitization. The original document exists in analog (paper) form, and digitization converts it into a digital representation.

Digitalization: It is a broader concept that involves the use of digital technologies to transform business processes, activities, or models. It goes beyond converting analog data to digital format and encompasses the integration of digital technologies into various aspects of an organization or system to enhance efficiency, functionality, or value.