Data Analytics 2/4

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.