Data Analytics 2/4

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Questions and Answers

What is the main focus of the Deming Management Method?

Process improvement rather than blaming individuals for problems.

What does HIPPO stand for?

High Impact Perspective of Peers

Highest Paid Professional's Outlook

Highest Paid Person's Opinion (correct)

Highly Important Person's Opinion

List one trait of an organization powered by data.

Everyone embraces data, starting from the executive suite.

According to McKinsey, what is crucial for creating a data culture?

Commitment from the CIO and board Signup and view all the answers

Focusing solely on data rather than business objectives can lead to successful data-driven transformation.

False Signup and view all the answers

What is the first step in a data-driven decision-making (DDDM) approach?

Identify business needs Signup and view all the answers

What do the FAIR principles stand for?

Findable, Accessible, Interoperable, and Reusable. Signup and view all the answers

What does DaaP stand for?

Data as a Product Signup and view all the answers

Data culture must be developed and ______.

reinforced. Signup and view all the answers

Match the following consulting firms with their focus on data culture:

McKinsey = Commitment from the board Harvard Business School = Universal basis for decision-making Forbes = Data culture test MIT Sloan School = Connect data to organizational culture Signup and view all the answers

In order to implement robotic process automation, a company needs to identify internal processes.

True Signup and view all the answers

What is one reason companies fail to become data-driven?

They view transformation as a departmental issue Signup and view all the answers

What are the expected outputs of the DDDM process?

KPIs, reports, graphs Signup and view all the answers

In process mining, __ is primarily used to improve processes.

data Signup and view all the answers

What is the role of C-level executives in business needs?

C-level executives define business needs. Signup and view all the answers

What should be included in the homework exercise?

Define five business needs that the DDDM process is expected to answer. Signup and view all the answers

Who is responsible for the first hour of the next lecture?

Students will show their results. Signup and view all the answers

What does 'process mining' utilize to produce results?

Logs of the software applications Signup and view all the answers

Study Notes

Data-Driven Strategy

Data-driven strategy: using and sharing data to achieve goals consistent with business strategy.
Data culture: a culture that values data and uses it to make decisions
Importance of cultural shift: Data-driven strategy requires more than just technological changes; it involves a cultural shift towards embracing data and using it for decision-making.

Traits of a Data-Driven Organization

Data accessibility: Data is accessible to all employees with appropriate privacy, security, and protection.
Data utilization: Data is used to uncover opportunities for improvement and test new ideas.
Data skills development: Employees are trained and equipped with the necessary data skills.
Data as a business asset: Data is recognized as a valuable asset that can deliver competitive advantages.
Ethics and privacy: Data is used ethically and with respect for privacy.

Building a Data Culture

Leadership commitment: Change starts from the top, with commitment from the CIO and board.
Data democratization: Making data accessible across the organization, ensuring privacy, security, and protection.
Ecosystem approach: Focusing on creating a data ecosystem rather than a single platform.
Eliminating silos: Breaking down silos in both technology and social aspects.
Change agents: Utilizing experienced professionals who understand the business to drive change.
Integration of talent: Combining the expertise of experienced managers with the skills of young people.
Sharing information: Encouraging open sharing of data and insights.

Preventing Data Manipulation

Collaboration with data scientists: Working closely with data scientists, avoiding isolation.
Respecting objective data: Accepting data as it is, even if it doesn’t align with expectations.
Contextual understanding: Recognizing that data can be interpreted differently based on individual contexts.

Creating a Data Culture Framework

McKinsey: Emphasizes building decision systems, commitment from leadership, data democratization, ecosystem-based approach, and risk management.
Harvard Business School: Emphasizes data as the universal basis for decision-making, leadership commitment, and avoiding data manipulation.
MIT Sloan School: Advocates for connecting data to organizational culture, ensuring data professionals understand business language, leveraging domain expertise, and highlighting real-world problem-solving with data.
Forbes: Advocates for a people-centric approach with a shift in mindset, developing data-related skills, streamlining data tools, and focusing on the quality of data used.

Data Maturity Levels

Data-Aware: Recognizing the value of data but not utilizing it effectively.
Data-Proficient: Competent in data handling and integrating data analytics into operations.
Data-Driven: Data is deeply embedded in the culture, with strategic and operational decisions based on data insights.

Common Reasons for Data-Driven Failure

Departmental focus: Viewing data-driven transformation as solely a departmental responsibility rather than company-wide.
Data-centric focus: Emphasizing data itself rather than its connection to strategic and business goals.
Lack of cultural shift: Failing to embrace the necessary cultural changes for a data-driven approach.

Data-Driven Organization

CDO role: Chief Data Officer plays a vital role in managing and developing data as a strategic business asset.
Data scientist team: Building a strong data scientist team to analyze data and provide insights.
OSEMN framework: A framework for data science processes (Obtain, Scrub, Explore, Model, and Interpret) that helps manage the project lifecycle.
Process mining: Utilizing data science and BPM to optimize organizational processes.

Data-Driven Decision-Making (DDDM) Approach

Step 1: Identifying business needs.
Step 2: Acquiring data.
Step 3: Analyzing data.
Step 4: Making decisions.

FAIR Principles

Findable: Data is easily discoverable.
Accessible: Data is readily available and usable.
Interoperable: Data can be seamlessly integrated with other datasets.
Reusable: Data can be reused for different purposes.

Data-Driven Decision Making (DDDM) Process

The DDDM process involves 4 key steps: identifying business needs, acquiring data, extracting valuable information, and making decisions.

Homework Exercise

The homework is designed to be an exercise, not an assignment, and there is no evaluation or scoring.
The purpose of the homework is to understand the DDDM process and apply it to a hypothetical scenario.
The homework is focused on applying the DDDM process to a retail company with 30 stores across Europe and an annual revenue of €100 million.
Students can work individually or in groups.
The homework involves:
- Defining at least 5 business needs that the DDDM process is expected to answer.
- Defining the main steps to set up and implement an effective DDDM process.
- Identifying at least 3 outputs (KPIs, reports, graphs) related to the business needs.
The homework should be completed and presented on Monday, November 4th.
The discussion on Monday is focused on the approach taken to address the exercise, and not on providing scores or evaluation.

Data Considerations

The exercise focuses on the process and not specific dataset analysis.
Data improvement or acquisition strategies are relevant to the DDDM process.
Methods to gather data include:
- Implementing loyalty programs to gather customer information.
- Purchasing data through companies like Talkwalker, which specializes in social media data.

C-Level Perspective

Business needs should originate from the C-level or top-level management of the organization.
"C" in C-level stands for Chief, referring to senior managerial roles within the company.

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