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We explore the ways in which people drive value from data so that you can start to identify the opportunities within your own organisation. We also introduce the course workbook that highlights the various dimensions you need to consider to increase your likelihood of success. We explore a couple of case studies so that you can learn from practitioners and apply their experiences to your emerging data strategy and data opportunity list. We also introduce the data analytics maturity model, allowing you to map where you are today and where you need to get to to release the value of your opportunities identified so far. We build upon the data analytics assessment and introduce the full data maturity matrix that looks at what, beyond the data, is needed for success. We bring this to life by learning from the experience of recognised industry leaders so that your actions are realistic, pragmatic and informed by their experiences. In line with the main goal of this course to give you the confidence to start adding value through better use of data, this final week will focus on execution. 3 themes run through this section: Value - overcoming challenges by focusing on the business value; Leadership - overcome inertia by appointing an accountable person to lead the change; Foundations - overcome that lack of assets and skill by taking small steps with partners. Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This is a course for leaders, for those of you setting up data capabilities for the 1st time, or those of you taking steps to the next level of data maturity. This is for you if you are the one leading the change to make your organisation more data savvy. The target learners are: The main goal of this course is to give you the confidence to start adding value through better use of data. You can use the hashtag #FLValueFromData to talk about this course on social media.

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We explore the ways in which people drive value from data so that you can start to identify the opportunities within your own organisation. We also introduce the course workbook that highlights the various dimensions you need to consider to increase your likelihood of success. We explore a couple of case studies so that you can learn from practitioners and apply their experiences to your emerging data strategy and data opportunity list. We also introduce the data analytics maturity model, allowing you to map where you are today and where you need to get to to release the value of your opportunities identified so far. We build upon the data analytics assessment and introduce the full data maturity matrix that looks at what, beyond the data, is needed for success. We bring this to life by learning from the experience of recognised industry leaders so that your actions are realistic, pragmatic and informed by their experiences. In line with the main goal of this course to give you the confidence to start adding value through better use of data, this final week will focus on execution. 3 themes run through this section: Value - overcoming challenges by focusing on the business value; Leadership - overcome inertia by appointing an accountable person to lead the change; Foundations - overcome that lack of assets and skill by taking small steps with partners. Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This is a course for leaders, for those of you setting up data capabilities for the 1st time, or those of you taking steps to the next level of data maturity. This is for you if you are the one leading the change to make your organisation more data savvy. The target learners are: The main goal of this course is to give you the confidence to start adding value through better use of data. You can use the hashtag #FLValueFromData to talk about this course on social media.

Topics Covered

  • Week 1 - Driving value from data
  • Week 2 - Case studies and data analytics maturity assessment
  • Week 3 - Fully data maturity assessment
  • Week 4. Execution
  • Individuals who are directly or indirectly accountable for driving value in their organisations.
  • Small to medium size business owners who want to drive more value from the information they already have.
  • Data professionals who are struggling to get business buy-in to driving value from data.

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Reviews summary

Data strategy for business impact

According to learners, "Driving Value from Data" is a highly effective course for business leaders, managers, and small business owners aiming to leverage data strategically. Students praise its clear focus on business value, practical insights, and the valuable case studies that offer real-world application. Many highlight the utility of the data maturity models and the actionable advice provided in the execution-focused final week, which truly builds confidence. However, some data professionals note that the course is high-level and lacks technical depth or hands-on exercises, making it less suitable for those seeking advanced analytics skills.
Uses insightful case studies and maturity models.
"The case studies were particularly insightful, offering practical examples."
"I found the concept of mapping our data maturity invaluable."
"I appreciated the real-world examples shared by industry leaders."
"The maturity model was a key takeaway for me."
Provides practical insights for immediate application.
"The final week on execution provided me with actionable insights."
"Fantastic! This course shifted my perspective... The framework for execution is exactly what I needed to get buy-in from my team."
"As a small business owner, this course demystified how to leverage the data I already have."
"It gave me the confidence to start small, focusing on immediate business value."
Empowers leaders to drive data value strategically.
"This course was a fantastic eye-opener for understanding how to integrate data strategy into business."
"I found it excellent for anyone in a leadership role looking to understand the strategic side of data."
"The focus on 'driving value' instead of just 'data analysis' was refreshing; it truly empowered me."
"I gained a very useful understanding of the big picture of data in an organization."
High-level overview, not for hands-on application.
"Some parts felt a bit high-level, lacking deep dives into implementation details."
"I expected more practical tools and less theory."
"I found myself wishing for slightly more hands-on exercises or templates."
"I missed practical exercises."
Ideal for leaders, less for technical data professionals.
"It's a great starting point for those new to data strategy, but not for technical data professionals looking for advanced techniques."
"I struggled to see how to apply the content directly in my day-to-day as a data analyst; it's probably better suited for managers."
"As a data professional, I already knew most of what was covered. There wasn't enough depth or new information for my role."
"This course is definitely for a strategic audience, not for those looking for technical skills."

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Career center

Learners who complete Driving Value from Data will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.
Provides a practical guide to big data analytics. It covers the challenges of big data, as well as the techniques and tools that can be used to analyze big data. It valuable resource for anyone who wants to learn more about big data analytics.
Provides a comprehensive overview of data mining. It covers the basics of data mining, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data mining.
Provides a practical introduction to statistical methods for data analytics. It covers the basics of statistics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using statistics to analyze data.
Provides a guided tour of predictive analytics. It covers the basics of predictive analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using predictive analytics to make better decisions.
Provides a friendly introduction to data analytics for people who are new to the field. It covers the basics of data analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data analytics without getting bogged down in technical details.
Provides a comprehensive introduction to data analytics with Python. It covers the basics of Python, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use Python for data analytics.
Provides a broad, introductory overview of data analytics concepts, making it ideal for beginners across various disciplines. It covers key data concepts and includes real-world examples and case studies to solidify understanding. Many universities use this book as a textbook for introductory data analytics courses. It serves as excellent background reading for anyone new to the field.
Introduces the fundamental principles of data science and data-analytic thinking from a business perspective. It helps readers understand how to extract valuable knowledge and business value from data, covering various data mining techniques without getting overly technical. Based on an MBA course, it uses real-world business problems to illustrate concepts, making it highly relevant for business-oriented individuals and professionals.
Focusing on the crucial aspect of communicating insights, this book teaches the fundamentals of data visualization and how to tell compelling stories with data. It provides practical guidance and real-world examples to help readers create effective visualizations and presentations. is highly recommended for anyone who needs to present data-driven findings clearly and persuasively, regardless of their technical background.
Offers an accessible and engaging introduction to the fundamentals of statistics, a critical component of data analytics. It explains key statistical concepts using real-world examples and relatable anecdotes, making it an excellent resource for those without a strong mathematical background. It helps build a solid foundation in statistical thinking necessary for data analysis.
Written by the creator of the pandas library, this book practical, hands-on guide to data manipulation, cleaning, processing, and analysis using Python. It is an essential resource for anyone looking to use Python for data analytics, covering key libraries like pandas, NumPy, and Jupyter. It includes numerous real-world case studies and is widely used by students and professionals.
Provides a comprehensive introduction to data science using the R programming language and the tidyverse package collection. It guides readers through the entire data analysis workflow, from importing and cleaning data to visualization and modeling. It's a widely recommended resource for those who prefer to use R for data analytics and is suitable for students and professionals.
Offers a less technical introduction to statistical learning compared to its counterpart, 'The Elements of Statistical Learning.' It covers essential concepts and methods for statistical modeling and prediction, with practical applications in R. It is widely used as a textbook in universities and is suitable for those with a background in statistics or quantitative fields looking to deepen their understanding of the statistical foundations of data analytics.
Considered a classic in the field, this book provides a comprehensive and rigorous treatment of statistical learning methods. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and a variety of algorithms. While mathematically more demanding, it is an invaluable reference for graduate students and researchers seeking a deep understanding of the theoretical underpinnings of many data analytics techniques.
This practical guide focuses on machine learning concepts and techniques using popular Python libraries. It provides a hands-on approach with code examples, making it excellent for those who want to implement machine learning models as part of their data analytics workflow. It is suitable for individuals with some programming experience and valuable resource for deepening technical skills.
Offers a practical and engaging approach to data science and analytics, focusing on using readily available tools like Excel to perform powerful analysis. It's a great resource for business professionals who want to leverage data without necessarily diving deep into programming. It provides a solid understanding of analytical techniques through relatable examples.
This influential book explores how organizations can gain a competitive advantage by effectively using data and analytics for decision-making. It highlights the importance of building an analytical capability within a company and provides examples of successful analytical competitors. This must-read for business leaders and professionals interested in the strategic implications of data analytics.
Makes a compelling case for the importance of big data in today's business landscape. It explores the opportunities and challenges presented by large datasets and how organizations can leverage them for insights and innovation. It's a valuable read for business professionals and leaders looking to understand the strategic value of big data analytics.
This comprehensive textbook covering the fundamental concepts and techniques of data mining. It delves into various data mining methodologies, algorithms, and applications. It widely used resource in academic settings for both undergraduate and graduate students seeking a detailed understanding of data mining as a core component of data analytics.
Provides a comprehensive introduction to data analytics with R. It covers the basics of R, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use R for data analytics.

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