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Summary to Becoming Data Literate by David Reed

Becoming Data Literate aims to provide readers with a foundational understanding of data concepts. Reed introduces core principles like types of data, use of data modeling and analysis, extracting value and insights from data. The book touches upon popular tools and techniques without focusing on technical specifics.

Reed emphasizes developing analytical thinking and asking questions of data to gain knowledge. He stresses acquiring literacy skills that empower better decision making using real-world examples. Becoming Data Literate covers topics such as structuring data, types of analysis and communicating findings effectively.

Summary to Becoming Data Literate by David Reed

While some sections seem basic, the book serves as an excellent primer on data principles. Reed presents an engaging, conversational style to demystify data science concepts. Overall, it provides a solid introduction to data literacy without prior expertise that benefits a wide audience.

In summary, Becoming Data Literate aims to impart foundational data understanding. Call to action: Continue reading to learn the core principles of data literacy and tools for extracting insights to make better decisions.

Genres

Data, analytics, business, technology, science, management, skills, productivity, decision making, communication, education, career, self-help, reference

Recommendation

Though data is invaluable, many organizations fail to appreciate it or harness its full potential, says data and analytics expert David Reed. According to Reed, data is the “operational DNA” that enables your organization to realize its vision. He explains how to embrace a data literacy framework and how to embark upon your organization’s data transformation. Reed shares meaningful insights that will empower you to navigate ambiguity and uncertainty, while embracing the change needed to thrive in a data-centric future.

Take-Aways

  • To harness the power of your organizational data, start with a vision.
  • Your organization can become data literate within three years.
  • A business moves through five stages as it matures to become a “data native.”
  • Inspire stakeholders, executives and teams by demonstrating the value of data transformation.
  • Build a culture whereby teams share data and a common language for talking about it.
  • Data leaders need to learn new approaches for managing data practitioners.
  • Data is an intangible asset for which standardized valuation metrics don’t yet exist.
  • Leaders have a duty to establish and implement a clear data ethics code.

Summary

To harness the power of your organizational data, start with a vision.

Data plays a crucial role in guiding decision-making, and many leaders see the need for dramatic transformation to unleash the potential of data at their organizations. Neglecting data can lead to major headaches – and expenses. Poor-quality data can cost a business a sizable portion of annual revenue – an average of 8.8% in 2020 – while putting the organization at risk of fines for violating regulations, such as the EU’s General Data Protection Regulation (GDPR). Addressing these problems – and going beyond, to realize the potential rewards of data literacy – requires more than just investing in tech tools: Leaders must craft a new corporate vision. Don’t confuse the notion of being data driven with having vision. Your vision should reflect the power of data to serve a strategic purpose.

“Crucially, data will be part of the organization’s DNA, indistinguishable and inseparable from its operating model.”

The DataIQ Way provides a framework to guide leaders in creating data-driven organizations. This framework, which emerged from deep research and extensive practical experience, incorporates the following five dimensions:

  1. Vision” – Data should play a central role in an organization’s vision and should not be considered an external factor. Your vision clarifies your organizational purpose (for example, delivering innovative services), which every goal and decision should serve.
  2. Business strategy” – Your business strategy typically encompasses the next three to five years and describes the steps required to achieve your vision, which should align with your data strategy.
  3. Value creation” – Stakeholders must be able to connect data and analytics to value creation, so embed formal value recognition into your organization’s processes.
  4. Culture” – Aspire to close gaps between your company culture and data culture, aligning with shared objectives, rewards and metrics.
  5. Data foundations” – Invest in data foundations that you integrate into every aspect of your business functions. These could include foundational data assets (such as data platforms), AI and automation solutions.

The DataIQ Way framework approaches each dimension of creating a data-driven organization in terms of strategy, leadership, skills and enablers, such as business partners or solutions.

Your organization can become data literate within three years.

By adhering to the DataIQ Way, practically any company can become fully data literate within three years. Data literacy means the organization has the capacity to understand the value of data, while employees have the data they need, use and trust the data they have, and share a culture of data across the organization. To gauge your organization’s current level of data literacy, consider the question from both “outside in” and “inside out” perspectives; that is, assess both the degree to which consumers of data can understand the data and use it to inform their own decision-making, as well as the extent to which the data department and producers of data align their activities with business goals.

“Data literacy means understanding how to create a great business with data at its heart, rather than trying to become a data business.”

In 2016, Aviva, a UK insurer, set out to apply data science to the development of engaging customer experiences. It took three years for leaders to understand that data science could not remain siloed and instead needed to facilitate the use of customer data across the organization. This meant bringing together more than 70 different databases containing some 15 billion data points. However, Aviva’s data leaders note that the pursuit of customer centricity depended on enterprisewide collaboration and not on the data itself.

A business moves through five stages as it matures to become a “data native.”

As an organization becomes more data literate, it progresses through five maturity levels:

  1. Data user” – You mainly use data in an ad hoc manner, rarely repeating processes or aligning behavior to strategic goals.
  2. Data driven” – You build processes with data and gain a competitive advantage by initiating formal structures.
  3. Data literate” – Data becomes accessible throughout your organization, and teams begin to make more evidence-driven decisions.
  4. Data cultured” – You’ve integrated data throughout your organization and across departments, and data informs your strategic development.
  5. Data native” – Any distinction between your business and its data strategies has disappeared, as they’re completely integrated. Your organization’s vision is fully supported and informed by data.

Inspire stakeholders, executives and teams by demonstrating the value of data transformation.

As humans naturally view change as threatening, you’ll likely face opposition if you’re spearheading a data transformation at your organization. To ensure the adoption of data and analytics throughout your organizations, identify use cases that demonstrate ways in which data clearly benefits teams, users and leaders. For example, to demonstrate the need for investment in technology, pinpoint incremental gains and business returns you could earn if updating a technology platform or automating a process. Speak a language your stakeholders understand by assigning a specific monetary value to benefits and identifying simple solutions. Secure buy-in from C-suite executives, your board and your stakeholders before pitching your organization on new data and technology investments.

“The business is not waiting to find out how data can help it – it needs to be told. Neither is it always wanting to become data literate – it needs to be shown why this makes the business strategy more achievable.”

The analytics team should engage with stakeholders and deliver on expectations by embracing the following habits:

  • Collaborating” – For instance, partner data practitioners with business executives.
  • Communicating” – Embrace data visualization and storytelling.
  • Championing” – Promote the services the data department can provide for the organization while being transparent about limitations.
  • Challenging” – Confront the status quo when data reveals a need for change.

When advocating for behavior change, leaders can help workers see and feel a connection between their actions and the organizational vision by providing a context for tasks, connecting actions to desired outcomes, creating clearly defined activity sequences that contribute toward long-term goals, and offering idealistic or symbolic messages. Leaders at NASA, for example, encouraged scientists to keep the potential of setting foot on Mars in the back of their minds while they were still working on landing on the moon.

Build a culture whereby teams share data and a common language for talking about it.

Nurture a data culture by treating data as an asset shared across departments, as opposed to keeping data in silos. Individual team leaders who guard data from others stand in the way of innovation and enterprisewide process improvement and value creation. Embrace data democratization wherever it offers benefits, and work toward clear data governance, which mitigates the potential for chaos and the misinterpretation of data.

“Progress toward being data native needs to be supported by an evolving data culture, the next step on from achieving data literacy.”

Focus on developing a common language for communicating about data, and consider introducing data literacy programs. All team members, both technical and nontechnical, should have a shared understanding of key data terms and definitions. Data practitioners should develop a range of soft skills, such as communication, as data teams must be able to help diverse audiences understand data-driven insights by using tools such as data storytelling. Align your data strategy with your brand values at every consumer touchpoint.

Data leaders need to learn new approaches for managing data practitioners.

Data leaders must remember that knowledge workers, such as data practitioners, aren’t subordinates; rather, they’re associates, who often know more about their job than their boss. Research from the Center for Evidence-Based Management suggests that companies can increase knowledge workers’ productivity by prioritizing five factors: “social cohesion, perceived supervisory support, information sharing, goal and vision clarity, and trust.”

“To be successful, the data leader also needs to demonstrate that they are continually improving the performance of the data department through building its skill set, productivity and engagement.”

Data leadership depends on the ability to build practitioners’ motivation, to represent the data department externally, to support the department by creating a conducive environment for their work, and to help other leaders and executives understand data in all its complexity and ambiguity. Building social capital and taking a positive approach to organizational politics increases data leaders’ influence.

Data is an intangible asset for which standardized valuation metrics don’t yet exist.

Opportunities abound in the data economy, which was valued at more than €400 billion (around $430 billion) in the EU and UK alone in 2020. According to PwC research, data-driven organizations tend to have higher stock valuations than their industry peers. However, data is considered an intangible asset – a category that also includes goodwill and brand valuation – and a standard, universally accepted data valuation framework has yet to emerge. As a result, data leaders themselves must discover or develop metrics to demonstrate the value of data.

“While data is all about numbers, metrics can prove harder to establish.”

According to a DataIQ study, 29.1% of organizations are unable to estimate the degree of revenue uplift the application of data science would bring. While it may be possible for you to isolate the specific contribution data makes toward certain customer actions (for instance, connecting cross-product purchasing to personalized marketing), most of the time, doing so requires sophisticated econometric modeling, which few organizations have developed. Data practitioners often attempt to identify the benefits they deliver to the organization by developing carefully constructed metrics that shed light on their work’s direct impact.

Leaders have a duty to establish and implement a clear data ethics code.

Many consumers have become cautious about sharing their data with corporations, as events such as the Cambridge Analytica scandal have demonstrated the potential harm organizations can cause when leveraging big data without ethical or regulatory considerations. Leaders should embrace transparency around issues pertaining to data governance, if they wish consumers to trust them with their data. Research from DataIQ shows that many consumers – just over 20% – will recklessly share their data without properly reading privacy notices. This is troubling, as it means that leaders have a responsibility to examine whether they’re actually obtaining users’ informed consent when harvesting data. Work to create an ethical framework – the Open Data Institute’s Data Ethics Canvas is a good place to start. Ensure that it’s adopted by everyone within your organization, underpins decisions and informs actions.

“Embedding values into the use of data is a core element of data literacy – everybody needs to have a mind-set to do the right thing.”

Data leaders can focus on four principles when opening discussions about organizational ethics:

  1. Autonomy” – Do users or consumers have the ability to make personal decisions without fear of negative consequences or coercion? When engaging in data capture or processing, are you infringing on citizens’ abilities to exercise any of their rights (for example, the Right to Withdraw Consent or the Right to be Forgotten in the EU)?
  2. Beneficence” – Are you working in the best interest of the individual?
  3. Nonmaleficence” – Just as medical practitioners promise to do no harm, so must technologists. Algorithms are not neutral; they contain human bias and thus have the potential to cause harm if you don’t take precautions.
  4. Fairness” – Are your data-driven decisions demonstrably fair?

Unlike other professionals, such as medical practitioners and military personnel, data practitioners lack a system of sanctions that would penalize them for operating outside a professional code of ethics. Thus, leaders themselves must create mechanisms to respond to ethical breaches (for instance, removing perpetrators from their roles).

About the Author

David Reed is the chief knowledge officer and evangelist at DataIQ, where he works to help brands embrace data literacy and derive value from data assets, processes and people.

Nina Norman is a certified book reviewer and editor with over 10 years of experience in the publishing industry. She has reviewed hundreds of books for reputable magazines and websites, such as The New York Times, The Guardian, and Goodreads. Nina has a master’s degree in comparative literature from Harvard University and a PhD in literary criticism from Oxford University. She is also the author of several acclaimed books on literary theory and analysis, such as The Art of Reading and How to Write a Book Review. Nina lives in London, England with her husband and two children. You can contact her at [email protected] or follow her on Website | Twitter | Facebook

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