What is *Data Strategy* by Bernard Marr about?

*Data Strategy* by Bernard Marr explains how businesses can leverage data as a strategic asset to drive decision-making, optimize operations, and create revenue streams. The book outlines frameworks for building a data strategy aligned with organizational goals, emphasizing data collection, governance, and monetization. It includes real-world examples and practical steps for transforming raw data into actionable insights.

Who should read *Data Strategy* by Bernard Marr?

This book is ideal for business leaders, executives, and managers seeking to integrate data into their organizational strategy. It’s also valuable for data professionals aiming to align technical efforts with broader business objectives. Startups and enterprises alike will benefit from its actionable guidance on turning data into competitive advantage.

Is *Data Strategy* by Bernard Marr worth reading?

Yes, the book provides a clear, non-technical roadmap for harnessing data’s potential, making it essential for decision-makers navigating digital transformation. Marr’s focus on real-world applications, governance, and monetization offers tangible value, while case studies and updated 2025 insights ensure relevance in evolving markets.

What are the key concepts in *Data Strategy* by Bernard Marr?

Marr identifies three pillars: **data-driven decision-making**, **operational optimization**, and **data monetization**. He stresses aligning data initiatives with business goals, categorizing data types (structured, unstructured, semi-structured), and establishing governance frameworks. The book also highlights cultural shifts needed to foster data literacy.

How does Bernard Marr suggest monetizing data?

Marr outlines three approaches: **selling insights** (e.g., market trends), **enhancing products/services** (e.g., personalized offerings), and **strategic partnerships** (e.g., joint data ventures). He emphasizes ethical practices and balancing revenue generation with customer trust.

What are the three core areas where data impacts business according to the book?

1. **Decision-making**: Using data for market intelligence and customer insights. 2. **Operations**: Streamlining processes like supply chains or customer service. 3. **Monetization**: Creating revenue through data products or partnerships.

What data types does Bernard Marr outline in *Data Strategy*?

1. **Structured**: Organized data (e.g., databases). 2. **Unstructured**: Text, social media, or multimedia. 3. **Semi-structured**: Formats like JSON or XML with partial organization.

How does *Data Strategy* address data governance?

Marr underscores the importance of **data ownership**, **privacy compliance**, and **security protocols**. He advocates for clear policies on data access, quality control, and ethical use to mitigate risks while maximizing value.

数据战略

如何从大数据、分析和物联网的世界中获利

伯纳德·马尔

3.78(410 Reviews)

《数据战略》概述

在《数据战略》（*Data Strategy*）一书中，全球排名前五的商业影响力人物伯纳德·马尔（Bernard Marr）改变了领导者利用数据力量的方式。这本屡获殊荣的指南近期根据人工智能的进展进行了更新，已成为亚马逊（Amazon）、谷歌（Google）和北约（NATO）高管在数据驱动的世界中寻求竞争优势的秘密武器。

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第 1 章

Transforming Business with Data: The New Competitive Advantage

In a world where every swipe, click, and transaction leaves a digital footprint, data has become the most valuable resource for businesses seeking competitive advantage. Bernard Marr's "Data Strategy" arrives at a pivotal moment when organizations are drowning in information yet starving for insights. Marr, ranked among the top five business influencers globally by LinkedIn with 2 million social media followers, has crafted what Forbes calls "the essential playbook for the modern enterprise." The book has become required reading at business schools worldwide, with executives from Amazon to Microsoft implementing its frameworks. What makes this work particularly compelling is how it transforms the often intimidating world of big data, analytics and artificial intelligence into practical strategies any organization can implement. As we stand at the threshold of the Fourth Industrial Revolution, this guide offers both a compass and roadmap for navigating a landscape where data literacy has become as fundamental to business success as financial literacy.

第 2 章

The Dawn of the Data-Driven Enterprise

We now create as much data every two days as we did from the beginning of time until 2003. This astonishing growth is transforming business at an unprecedented pace, revolutionizing how companies operate and becoming increasingly critical to organizational survival. Those viewing data as a strategic asset will thrive in this new landscape, while others risk obsolescence. Industry leaders like Amazon, Google, and Microsoft have built their empires on the foundation of data analytics, setting new standards for how businesses must operate in the digital age. Almost every action we take leaves a digital trail-from browsing online to shopping with credit cards, sending emails, taking photographs, or simply walking down streets with our mobile phones. Even mundane activities like adjusting your home thermostat or using a fitness tracker contribute to this vast data ecosystem. While data itself isn't new (we've used paper records and databases for years), our ability to capture and analyze unstructured data-pictures, videos, maps, text, speech recordings-represents a revolutionary shift. This raw information contains far more valuable insights than traditional structured business data, if we can extract it effectively. Modern analytics platforms can now process millions of data points in real-time, identifying patterns and trends that would be impossible for humans to detect. Your digital footprint reveals more than you might imagine. Google knows your age and gender even if you never told them, based on your search patterns and browsing behavior. Facebook can predict relationship outcomes and intelligence levels based on your likes, shares, and interaction patterns. Police track your driving through networked cameras and license plate recognition systems, while retailers like Target can predict pregnancies through purchasing patterns before families know themselves. Elections are increasingly analytics-driven-Obama's team ran 66,000 simulations daily in 2012, while Trump's campaign increased daily donations tenfold through targeted advertising. Netflix saves $1 billion annually through data-driven content recommendations, while Uber optimizes millions of rides using real-time analytics. We're witnessing the emergence of the Fourth Industrial Revolution, connecting computers and automation in revolutionary ways. Robotics controlled by machine learning algorithms require minimal human input, enabling "smart factories" where cyber-physical systems monitor production processes and make autonomous decisions. Companies like Tesla and BMW operate facilities where AI systems adjust production parameters in real-time based on quality metrics and demand forecasts. The "digital twin" process has emerged-simulated versions of real-world systems built using sensor data that allow businesses to predict outcomes when changing variables, leading to less waste, higher productivity and increased profits. GE uses digital twins to optimize wind turbine performance, while Siemens employs them to improve manufacturing efficiency by up to 25%. Beyond data, AI and IoT, several other technologies are proving crucial in this era. Genomics applies technology to understanding the genome and gene editing, with methods like CRISPR-Cas9 showing tremendous potential for treating hereditary conditions and revolutionizing agriculture. Blockchain technology creates tamper-proof transaction ledgers, with predictions that 10% of GDP will be stored on blockchains by 2025. Major banks are already implementing blockchain for cross-border payments, reducing transaction times from days to seconds. Extended reality (XR) encompasses virtual and augmented reality, allowing us to enter digital worlds or overlay digital elements onto our physical world, with applications ranging from surgical training to remote maintenance. Data has become a key business asset central to success in every industry. Companies leveraging data saw $430 billion in productivity benefits over non-data-using competitors between 2017-2020. However, up to 85% of data projects fail without proper strategy. The key is focusing first on "why" rather than "what" or "how"-understanding the business purpose before implementing technology. Successful organizations typically start with clear business objectives, invest in data literacy training, and build cross-functional teams that combine technical expertise with domain knowledge. Companies like Walmart use data analytics to optimize inventory across 11,000 stores, while Progressive Insurance uses real-time driving data to offer personalized insurance rates.

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第 3 章

Six Fundamental Ways to Create Value from Data

The most critical factor in data strategy is selecting use cases that align with business objectives-whether growing customer base, increasing revenue, targeting marketing campaigns more accurately, reducing faulty returns, or minimizing manufacturing waste. These strategic objectives fall under six key categories that represent the fundamental ways businesses can create value from data. First, data transforms business decisions from gut feelings to evidence-based choices. Analytics helps develop deep understanding of potential outcomes by modeling and simulating options. While rare business leaders like Steve Jobs or Elon Musk might seem to succeed on intuition alone, even their companies rely heavily on data-driven decisions. Beyond informing human decisions, automated decision-making allows smart machines to make choices independently, from algorithmic loan approvals to automated recruitment. Second, data enables unprecedented customer and market intelligence. Businesses can now gain accurate insights into customer wants, usage patterns, purchasing behaviors, and opinions. This information improves decisions across product design, sales, marketing, and aftercare. Data also provides crucial market intelligence about competitors, potential customers, and how new offerings might fit alongside existing choices. Third, data creates smarter products. From TVs and phones to kitchen appliances, cars and even cities, products are becoming more intelligent through connectivity. These IoT "things" do more than their non-smart predecessors-smart footballs track kick power and velocity, smart scales monitor weight progress, and smart toilets screen for health issues. Using data to create products that outperform competitors gives businesses an innovative edge in the marketplace. Fourth, data enables better services. The "everything-as-a-service" trend allows ultra-personalized offerings tailored to specific customer moments, like Netflix recommending the perfect show for a particular time of day. Products traditionally sold as one-off purchases-movies, software, appliances, clothing, even cars-are shifting to subscription models, allowing companies to develop closer customer relationships and gather more usage data. Fifth, data streamlines internal operations across marketing, customer service, recruitment, manufacturing, logistics, HR, and R&D. Cloud-based, AI-augmented tools reduce time spent on repetitive administrative tasks better handled by machines. The Internet of Things offers opportunities to monitor, track and improve business processes from top to bottom. Finally, data itself can become a valuable commercial asset. Companies committed to data-driven operations often find their collected information has monetary value-whether as consumer insights (like fitness band data) or business intelligence (like marketing data sold by Google and Facebook). Even niche industries generate data valuable to someone, creating potential new revenue streams. Telemedicine demonstrates how data strategies can span all six use cases. Teladoc, the largest US telemedicine provider, leverages AI across its operations: chatbots perform initial patient triage (improving decision-making); telemedicine consultations capture comprehensive patient data (understanding customers better); compatible smart healthcare devices enable remote care (creating better products); collected patient data helps optimize healthcare resource planning (improving internal processes); and this data can be monetized by selling insights to patients and healthcare providers.

第 4 章

Making Better Decisions with Data

The foundation of data-driven decision making begins with identifying your key business questions (KBQs)-those unanswered questions directly related to your strategic goals. Without clear questions, you can't identify what data you need. Your KBQs should focus on what you need to know to achieve strategic objectives, helping you hone in on essential data rather than drowning in information. Like in Douglas Adams' Hitchhiker's Guide where a supercomputer calculated "42" as the answer to life without knowing the question, starting with the right questions makes data more manageable and useful. I once helped a small fashion retailer who wanted to increase sales by developing specific questions about customer behavior-from how many people passed their shops to conversion rates. By installing sensors to track foot traffic and combining this with sales data, they not only improved displays but also made the strategic decision to close an underperforming location with insufficient foot traffic. Once we have data that might answer our questions, we must understand what it's telling us and communicate this to decision-makers. We create summaries and visualizations that illustrate core datapoints-a practice businesses have used for centuries, though our tools have become increasingly sophisticated. Curated dashboards are like fine dining experiences-experts carefully select, prepare and present information to decision-makers. They're an ideal starting point for many organizations' data strategies, as they provide clear answers to key questions with all noise removed. However, their strengths can become weaknesses-they create reliance on specialized teams, potentially causing analytics bottlenecks and siloing data skills rather than building organization-wide data literacy. While curated dashboards are like fine dining, self-service data exploration dashboards are comparable to a Swiss raclette experience-communal and participatory. These dashboards put all necessary data ingredients within reach of users, fostering data literacy throughout the organization. However, like at a raclette table, guidance on best practices is essential to avoid messiness. As businesses increasingly democratize data access, the role of data translators becomes crucial. These individuals bridge the gap between data scientists and decision-makers, possessing both data understanding and business acumen. Like servers in a raclette restaurant who demonstrate cheese-scraping techniques and suggest ingredient combinations, data translators provide hands-on help across the workforce. Simply piling data into storage with a dashboard on top isn't enough-the real challenge lies in transforming datapoints into actionable insights. Data storytelling addresses this by leveraging humanity's receptiveness to narratives. Effective data stories have a beginning (the business question), middle (analytical processes), and end (insights gained and actions required). Good data storytelling requires conciseness; as Mark Twain noted, "I didn't have time to write a short letter, so I wrote a long one." Cutting through noise to include only vital information takes effort but saves decision-makers time.

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第 5 章

Understanding Customers Through Data

Understanding customers through data is fundamental to business success. Like modern tuna fishing that evolved from guesswork to GPS and tracking beacons, businesses today must leverage comprehensive customer analytics to remain competitive. With internet, social media and connected devices, companies can build 360-degree customer views that reveal not just who customers are, but where to find them and how to engage them effectively. Customer data falls into three main categories: personal data (information directly related to specific individuals, often protected by regulations), behavioral data (purchase history, website interactions, product usage patterns), and attitudinal data (customer satisfaction surveys, brand awareness metrics, and social media sentiment analysis). Internal customer data captures proprietary information about your own customers' activities, while external data provides insights about the broader market. Internal data collection builds customer connections but only shows part of the picture-your existing customers. External data from services like Google Trends and Facebook's Custom Audience helps understand potential customers and market trends. The ideal approach combines both internal and external sources across personal, behavioral, and attitudinal data types to create a true 360-degree customer view. Modern customer analytics originated with direct marketing pioneers like Acxiom, who developed the first online mailing list generator for Citibank in the 1980s, segmenting customers by demographics for targeted promotions. The internet revolutionized customer relationships, with tech giants leading the way. Amazon built its empire on customer understanding through collaborative filtering recommendations that drive one-third of sales. Facebook monetized its platform by connecting billions of users with businesses seeking customer data. Netflix exemplifies data-driven business success with its 200+ million subscribers. It analyzes viewing patterns to create compelling content and recommendations-like identifying "scariest movies" based on what viewers couldn't finish watching. Its celebrated early use case was "House of Cards," commissioned without a pilot after data showed the combination of director David Fincher, actor Kevin Spacey, and a dramatic real-world setting would likely succeed. Netflix's formula creates shows with an 80% success rate (versus the industry's 30-40%), while its recommendation technology drives 80% of viewing activity. Speed is essential in customer analytics-today's data is more valuable than yesterday's for capturing immediate opportunities. Walmart's analytics hub processes petabytes of store data daily but only considers information from the last few weeks relevant. Modern analytics aims to deliver real-time insights to capture "micro-moments"-fleeting selling opportunities unique to individual customers. In 2013 Disney introduced RFID-powered Magic Bands that function as admission passes, hotel keys and payment systems for park visitors. Beyond guest convenience, these wristbands provide Disney with real-time data streams showing where guests are, what they're doing, and how they're using attractions. This allows Disney to maximize value by identifying what visitors want to buy next and ensuring it's conveniently available at the right time. Customer data now drives product and service design decisions. Companies like Autodesk, which switched to cloud-based subscription models, can analyze exactly how customers use their software rather than relying on limited feedback forms. This reveals which features are popular (and should be refined) versus which are ignored (and should be removed or improved).

第 6 章

Creating Intelligent Services with Data

The modern economy is increasingly service-based, with data enabling smarter offerings that solve problems and reduce friction. As predicted by the World Economic Forum, we're moving toward a rental economy where "you will own nothing, and you will be happy"-replacing ownership with subscription services for everything from entertainment to transportation to software. Tech giants pioneered the service-based economy by starting with one exceptional service, then using gathered data to monetize insights and create increasingly smarter offerings. Amazon evolved from a bookseller to an everything-store with automated shops and drone delivery. Facebook transformed from a college social network to a provider of advertising, analytics, communications and VR services. Google expanded from search to navigation, predicting traffic with 97% accuracy. Netflix disrupted entertainment by making content on-demand and personalized, while Spotify creates custom playlists and explores AI-generated music. Even traditional businesses are transforming with data-driven services. Stitch Fix uses algorithms to curate personalized clothing packages based on customer style and fit preferences, generating $1.5 billion in annual revenue. Uber revolutionized transportation by matching riders with nearby drivers, then expanded into food delivery and implemented AI-driven customer service that reduced support ticket handling time by 15%. China's Didi Chuxing, with half a billion customers and 3,500 data scientists, evolved from ride-sharing to traffic management systems. Financial institutions are leveraging their extensive customer data to create personalized services that recreate the intimate bank manager relationship of the past. Royal Bank of Canada's Nomi Budgets analyzes spending patterns to provide customized budgeting advice, helping customers collectively save $83 million in 2019. Digital-only banks like Monzo and Revolut offer AI-powered features including facial recognition and 24/7 support without traditional infrastructure costs. Healthcare is rapidly adopting data analytics to create smart services. Babylon Health offers subscription-based virtual GP consultations with AI-powered triage chatbots that assess symptoms in natural language. Their platform combines a medical knowledge base, anonymized patient data, and probabilistic algorithms to identify likely causes of symptoms. Virtual hospitals like RPA Virtual Hospital in Sydney use IoT devices to monitor patients remotely, while Doccla partners with UK hospitals to provide remote care for chronic conditions. Robotics is becoming available through subscription models, lowering barriers to entry for businesses. InVia Robotics offers warehouse robots as a service with monthly payments covering equipment, maintenance and expertise. Their robots continuously learn and improve across all customer deployments, with one customer reporting quadrupled productivity. AI-as-a-service platforms democratize access to advanced technology that would otherwise require prohibitive infrastructure investments. Major tech companies like Amazon, Google, Microsoft, IBM, Baidu and Alibaba offer cloud-based AI through subscription models where users can leverage pre-trained models or use their own proprietary data. These platforms provide computer vision, natural language processing, and recommendation engines as "microservices."

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第 7 章

Building Intelligent Products for the Connected World

The smart product revolution began with smartphones and has expanded to encompass virtually everything in our daily lives. While early "smart" products were simply internet-connected, today's truly intelligent products incorporate AI that learns from usage patterns to deliver personalized experiences. The Internet of Things (IoT) has enabled billions of connected devices worldwide to capture and share data, allowing manufacturers to develop features aligned with user preferences. Smart products like Amazon Alexa speakers are designed as both physical products and service delivery platforms that improve through iterative updates based on usage data. This creates opportunities for additional revenue streams through subscription models, as seen with home security systems that offer basic functionality out-of-box but charge for advanced features. Industrial manufacturers like John Deere and Rolls-Royce now build data capture capabilities into their products and sell operational insights back to their customers. While self-driving cars attract the most attention for their potential to transform daily life, other autonomous vehicles may achieve full autonomy first. Maritime autonomy is advancing, with Rolls-Royce and Finferries completing the first autonomous passenger ferry journey in 2018, successfully navigating obstacles without crew input. The IBM and ProMare joint venture Mayflower autonomous ship exemplifies scientific research vessels that eliminate crew facilities, making them smaller, lighter and more energy efficient. Smart home technology has evolved from merely connected devices to truly intelligent ones that learn and adapt. Voice assistants have become central hubs, with 38.5% of US households using AI-enabled assistants by 2020. While basic smart products like Philips Hue lighting offer remote control, truly intelligent devices like Google's Nest thermostat use machine learning to understand household patterns and optimize energy use. Samsung's Family Hub refrigerators use computer vision to track contents and suggest recipes, while LG's smart fridges feature voice-activated doors and transparent displays. Healthcare costs could be dramatically reduced through intelligent devices that detect problems before they require expensive interventions. Smart devices monitor bodily functions and compare symptoms against vast datasets to identify early warning signs. The Apple Watch includes ECG monitoring for heart irregularities, while smart contact lenses in development can track glucose levels for diabetics. Bisu offers smart urine tests that detect infections without doctor visits, and smart pacemakers transmit cardiological data directly to caregivers via Bluetooth. Industrial tools have evolved beyond mere heavy lifting to become smart decision-makers. The Internet of Things enables predictive maintenance-fixing equipment before it breaks by using sensors to detect wear and tear, dramatically reducing costly downtime and allowing strategic positioning of maintenance resources. Modern manufacturing robots have transcended repetitive tasks; they now navigate warehouses, identify faulty parts through computer vision, and make complex decisions. Fanuc has partnered with Cisco and Rockwell Automation to create robots that can train themselves through reinforcement learning.

第 8 章

Implementing Your Data Strategy Successfully

Developing a successful data strategy requires focusing on the right use cases, building appropriate infrastructure, and creating a data-driven culture throughout your organization. The journey begins with identifying specific opportunities to put data to work across all six use case areas: better decision-making, understanding customers, creating smarter products or services, improving business processes, and monetizing data directly. When identifying data use cases, start with a brainstorming session involving key team members whose work will be affected. Every idea should connect to one of these areas and align with strategic business goals. Without this alignment, data projects become costly with little achievement. It's easy to get dazzled by what data "can" do rather than focusing on what it "should" do. After brainstorming potential applications, select a focused shortlist of viable use cases that align closely with strategic goals. The most successful strategies typically start small but focused rather than trying to tackle too many initiatives simultaneously. I recommend identifying between one to five "major" use cases and one to three "quick wins"-the former to demonstrate long-term value, the latter to build trust quickly. With your selected use cases, you can build a unified data strategy. Rather than treating each use case in isolation, look for synergies and efficiencies across all initiatives. This involves revisiting data requirements, governance, technology, skills, and implementation-but now considering how they affect your strategy as a whole. Data governance means taking ownership of legal and moral responsibilities when working with powerful information. It requires ensuring legal compliance, proper permissions, security protocols, and clear accountability for data quality and accuracy. This culture of responsible data stewardship should permeate the entire organization as a core value. Executing a data strategy is where theory transforms into action-the most rewarding phase that turns data into tangible business improvements. Success begins with proper planning and senior leadership buy-in to create a top-down ripple effect establishing data as a core organizational asset. Creating a data culture requires buy-in at every organizational level, recognizing data as a key business asset and adopting a "data-first" attitude where the initial response to any challenge is "what data do I need to solve this problem?" Like any good strategy, your data approach requires regular review and revision, considering both technological advancements and evolving business needs. For most companies using data to improve operations or decision-making, an annual full revision as part of regular planning cycles works well. As business goals shift and new opportunities arise, your data strategy must evolve accordingly. What begins as addressing specific business questions may expand to new areas once infrastructure is established. The true value of AI and data lies in solving humanity's biggest challenges. The United Nations' 17 Sustainable Development Goals provide a framework for understanding AI's potential global impact. From reducing poverty and hunger to improving healthcare, education, gender equality, and environmental sustainability, data technologies offer tremendous opportunities to create a better world. While individual AI projects may seem small compared to Tesla or Facebook, everyone implementing AI has a role in determining its future direction. Thoughtful, ethical application of AI and data strategy delivers tangible benefits for customers, workforce and business, while disregarding privacy, trust and ethics could lead to undesirable outcomes.

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《数据战略》核心主题

strategic asset management
data-driven decision making
digital transformation framework
competitive advantage analytics
fourth industrial revolution

《数据战略》经典语录

Those viewing data as a strategic asset will thrive.
Your digital footprint reveals more than you might imagine.
Up to 85% of data projects fail without proper strategy.
Data transforms business decisions from gut feelings.
Data enables unprecedented customer and market intelligence.

《数据战略》主要人物

Bernard MarrAuthor and business influencer

关于作者

Bernard Marr 是畅销书《数据战略》（Data Strategy）的作者，也是全球公认的未来学家。他结合自己在业务分析和数字化转型方面的专业知识，致力于在数据驱动时代为企业赋能。该书以利用数据获取竞争优势为核心，反映了 Marr 二十年来为微软（Microsoft）、沃尔玛（Walmart）和壳牌（Shell）等财富 500 强公司提供咨询的职业生涯。

作为剑桥大学校友，他撰写了 19 本具有影响力的著作，包括《未来技能》（Future Skills）和《生成式人工智能实践》（Generative AI in Practice），这些著作深入探讨了新兴技术与战略创新。

Marr 的见解通过其《福布斯》专栏和 LinkedIn 平台得到广泛传播。他在 LinkedIn 全球商业影响力人物中名列前五，通过可操作的战略吸引了超过 150 万专业人士。他的理论框架被整合到企业培训计划和学术课程中，巩固了他作为商业技术领域权威发声者的地位。

Marr 的作品累计销量达数百万册，被翻译成 25 多种语言，至今仍是领导者应对数字化变革的必读书籍。

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@Matt, YC alum

108

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@Erin, Investment Banking Associate , NYC

254

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@djmikemoore

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@Pitiful

4.5K

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@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

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@Cashflowbubu

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@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

查看更多网络上关于 BeFreed 的讨论

开启你的学习之旅，就是现在

第 2 章

数据战略

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一本书，多种聆听方式。

Transforming Business with Data: The New Competitive Advantage

The Dawn of the Data-Driven Enterprise

边听边看，每个词都清晰呈现

Six Fundamental Ways to Create Value from Data

Making Better Decisions with Data

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Understanding Customers Through Data

Creating Intelligent Services with Data

边听边看，每个词都清晰呈现

Building Intelligent Products for the Connected World

Implementing Your Data Strategy Successfully

你已读完 《数据战略》

《数据战略》核心主题

《数据战略》经典语录

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关于作者

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发现更多

1,000,000+ curious minds.

1,000,000+ curious minds.

一本书，多种聆听方式。

Transforming Business with Data: The New Competitive Advantage

The Dawn of the Data-Driven Enterprise

边听边看，每个词都清晰呈现

Six Fundamental Ways to Create Value from Data

Making Better Decisions with Data

从 《数据战略》中收获更多

Understanding Customers Through Data

Creating Intelligent Services with Data

边听边看，每个词都清晰呈现

Building Intelligent Products for the Connected World

Implementing Your Data Strategy Successfully

你已读完 《数据战略》

用户还喜欢

发现更多

《数据战略》概述

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你已读完《数据战略》