Summary: People and Data: Uniting to Transform Your Business by Thomas C. Redman

• Data is one of the most important and powerful assets in the modern world. It can help companies to make better decisions, to achieve their goals, and to improve their performance. But data alone is not enough. Data needs people, and people need data. How can companies create a symbiotic relationship between people and data, where both can benefit from each other and reach their full potential? This is the premise of Thomas C. Redman’s book People and Data: Uniting to Transform Your Business.
• If you want to learn more about the people and data relationship, how to recognize and resist the lies that sabotage your peace, and how to live in the truth and enjoy the freedom that God has for you, you should read this book. It will show you how to use data as a positive force, how to overcome the barriers and the opposition that you may face, and how to join the movement and the community that are working for data-driven transformation. It will also inspire you to pursue your passions and goals, to overcome your fears and challenges, and to find your peace and freedom. It will teach you how to live no lies.

Recommendation

Data gives people the capacity to act meaningfully in business, government and private life. Data is the fuel that runs today’s world and the world needs high-quality data. Yet, individuals and businesses often fail to prioritize data when building technological infrastructure, organizing their operations or choosing new tech tools. They focus more often on the technology itself, even though tech only becomes fully productive and valuable when quality data feeds it. “Data doc” Thomas C. Redman urges companies to modernize their approach to and use of data.

Take-Aways

• Everyone’s personal and professional lives depend on data.
• Companies must optimize their application of quality data.
• Organizations should include ordinary employees in their approach to data.
• Everyone should join the “data generation.”
• All organizations must prioritize improving data quality.
• “Big data” is a popular catchword, but “small data” is more important for most companies.
• “Data is a team sport,” but many factors can inhibit effective data teamwork.
• Organizations must align their data and business priorities.

Summary

Everyone’s personal and professional lives depend on data.

Data permeates daily life in numerous ways. The daily “step count” your smartphone registers is data, as is your shopping list, meeting appointment book, and calculations of whether you can afford your dream house. Data is everywhere.

“The world is bafflingly complex, with all kinds of moving parts, complicated interactions and subtlety. To make sense of it all, we impose structure, using words, concepts and numbers.”

Structured data has two dimensions: a “data model” and “data values.” The data model shows what the data is about – aspects of interest, crucial qualities of those aspects and the relationships between them. The data values display the data’s relative importance to the question or questions you are trying to answer.

Data enables meaningful action in private life and commerce. You need data to anticipate and shop for what you need, for example. You make rational home-purchasing decisions because you have data regarding your anticipated future income. Your product marketing depends on data on sales trends, supply chains and availability.

Business requires far more complex data than most people need in their private lives. Companies need data for everything from product design and manufacturing to delivery and sales. Information technologies accentuate everything – and bring it to a nearly unimaginable scale. Problems are rarely the technology’s fault. Failures or problems result from low-quality data.

Companies must optimize their application of quality data.

Data fuels businesses. The vast market capitalization of digital native companies such as Amazon, Facebook, Google, Netflix and Uber demonstrates that the market and investors value extensive data use and analysis.

“If you and your company are not already, you must get serious about data, data science and other ways to put data to work and learn to make them part of your future.”

Major success stories aside, the “data space” has problems. Laypeople distrust data, and without steady sources of quality data, politicians, business leaders or individuals cannot make sound, fact-based decisions. For example, during the coronavirus pandemic, systematic misinformation campaigns affected people’s willingness to receive vaccinations and probably increased the number of COVID-19-related deaths. Bad data costs. The monetary cost of bad data may be as high as 20% of a company’s annual revenue.

Businesses must make their data work, either by utilizing data science technologies such as artificial intelligence (AI) or by engendering a “data-driven culture.” Leaders must organize their businesses to emphasize data use and build technological infrastructure to support data use at the firms’ required scale. And companies must heed data security risks.

Organizations should include ordinary employees in their approach to data.

Companies attempting to address their data quality must reconfigure their “organization for data.” This reconfiguration must consider five issues: the people involved; how the data flows; information technology management; teams working on data; and the people leading the data-driven projects.

“Missing people is the single most important force holding data programs back.”

Data becoming a business’s central driver requires the involvement of everyone in the company. Employees interact with data in one way or another daily, including those in non-specialist roles. Just about everyone at every employment level uses data to perform their jobs and make the decisions their jobs require. Better data use will immediately increase revenue and lower costs. It will also reduce the risk of errors and foster a closer relationship between employees and customers.

Organizing your business for data must involve the smooth and coordinated movement of data between departments and people. Given rapidly evolving technological advances and their relationship to data use, businesses must keep technology and data managers separate. Data directly relates to a company’s business goals; technology provides the infrastructure that supports data. Transformation comes from both the “bottom-up” and the “top-down” – with young employees offering innovations and senior leadership managing coordination.

Everyone should join the “data generation.”

The majority of people have a bored, indifferent attitude toward data. They use data for their work but don’t seek to innovate data usage to benefit their companies – or daily lives. They might worry that the evolving data-driven work culture, imbued as it is with more specialized professions in data science and AI, will render them and their work irrelevant.

“This is unfortunate because today, data offers unprecedented opportunity to anyone willing to work it out and seize it. Obviously if you know more you can do more, but practically everyone, regardless of profession, age or level of training can do plenty.”

The prevalence of politically motivated misinformation has changed many people’s views on the problem of low-quality data. During the pandemic, people questioned the quality of data they could access. People demanded quality data and took proactive measures to obtain data they could trust. They found and seized upon ways to improve their data quality and to use it in their “personal, professional and civic lives.” These data-empowered people constitute the “data generation.” There have always been professionals, technicians and analysts invested in data quality. The data generation is composed of ordinary people embracing data to service their personal needs.

All organizations must prioritize improving data quality.

Only about 3% of companies’ data meets basic quality standards, and according to one study, only about 16% of managers trust the data they commonly use. The prevalence of bad data produces numerous negative effects. One estimate suggests that professionals spend around 50% of their time dealing with bad data. Bad data can lead to significant financial losses, and in the airline and health care industries, for example, to lost lives.

“The most transformative uses of data involve data-driven decision-making, data-driven cultures, and treating data as assets. Bad data stymies such efforts.”

Many companies seek to resolve their bad data problems through automation. However, poor data fed into automated technologies generates mistakes. The solution to poor-quality data lies in having employees create reliable data. In addition, senior executives and their “embedded data managers” need to demand that data-creation processes significantly improve – and hold people accountable to this goal of generating better data.

“Big data” is a popular catchword, but “small data” is more important for most companies.

High-quality data improves a company in myriad ways. And when employees put good data to work, they improve how they work, their products and their ability to get new customers. Most of these people aren’t data science professionals working in sophisticated areas such as advanced analytics, machine learning or AI. These are regular employees in, for example, a marketing department who work as a team with data scientists.

“Unless the quantities of data are very large or the analytic tools are especially complex, no step of the data process requires a data scientist…Small teams of regular people can navigate the process just fine.”

Companies are often over-eager to use data science, big data and various sophisticated applications. In their eagerness, companies often overlook the value of small data for their employees and companies. Small data projects don’t involve a lot of people and use hundreds of data points rather than millions. It’s estimated that a modest 40-person department can carry out up to 20 small data projects a year with a financial benefit ranging up to $250,000. Small data projects offer more problem-solving opportunities than big data projects. Worthy areas for small data projects include, for example, reducing time wasted in interactions between colleagues and streamlining in-house work processes.

“Data is a team sport,” but many factors can inhibit effective data teamwork.

People are often more productive when working with people outside their departments, figuring out how to address problems together. Unfortunately, people tend to revert to silos or their narrow areas of departmental involvement and concern. Silos run counter to the teamwork necessary to make data really work. In the most straightforward case, people remain in their silos rather than reach out across the company to solve a tractable problem.

“Silos aren’t going away. So companies must build ‘fat organizational pipes’ to deal with silos.”

Useful “fat organizational pipes” – channels of two-way communication between departments, individuals, and up and down the company hierarchy – include:

1. The “customer-supplier model” places employees, the teams they work with and the processes they use in both data-creator and customer roles. The two roles must communicate fluidly.
2. “Data supply chain management” adapts the customer-supplier model to more complex data flows. It involves drawing data from all areas of the company – or even outside the company – applying data management to customer needs and ensuring the data is high quality.
3. The “data science bridge” attempts to resolve silos specific to data science, such as creating a high-bandwidth communication avenue that flows between laboratory and factory.
4. “Common language” seeks to establish and maintain a common vocabulary with which people and computer systems communicate. A common language becomes especially important when a situation requires “coordinated action,” and everyone needs to be on the same page.

Organizations must align their data and business priorities.

Organizing a company to maximize data’s benefits involves executives, technology experts, and those managing data supply chains. The organization must lead, train and support their day-to-day activities. Those providing this day-to-day support comprise the “data team.” Data teams must include an executive who provides overall management, an entrepreneur who figures out how to generate revenue from the data, a developer who manages software applications and technological infrastructure, and a data security officer.

“Working effectively with data requires exceptional degrees of cooperation, far more than most companies exhibit today.”

With any data project, data creators and customers must communicate and collaborate to increase data quality and reduce errors. On large projects involving big data, many people will work together and do so in contexts involving complicated supply chains.

Data projects can be chaotic, with everyone moving in different – and often conflicting – directions. For data projects to deploy regular employees and empower them with independence and responsibilities, a company needs strong leadership. “Data program coordinators” put the right people together and ensure they can collaborate.

A company promoting a data project needs to align that project with its business priorities. It should form its data teams to increase the likelihood that it will promote the primary business agenda. Data teams must interact with employees daily, understand the project’s problems and possibilities, embrace their people’s aspirations, address anxieties surrounding the data, and train employees to identify and solve their own problems.

About the Author

Thomas C. Redman is the founder and president of Data Quality Solutions.

Genres

Data, Business, Nonfiction, Management, Transformation, Strategy, Culture, Operations, Innovation, Leadership

Review

The book People and Data: Uniting to Transform Your Business by Thomas C. Redman is a book that explains how to structure an organization to make the best use of business data to drive company performance. The book argues that most companies are not leveraging the value of data because their structures and processes are unfit for data and their employees are not included in the data-driven effort. The book shows how to change this by improving data quality, tackling organizational issues, and upskilling the workforce. The book also provides practical tips and tools to help companies integrate data into their culture, strategy, and operations. The book is based on the author’s experience and research, and includes case studies from various industries and sectors.

The book People and Data: Uniting to Transform Your Business by Thomas C. Redman is a well-edited and insightful book that offers a comprehensive and realistic solution for data-driven transformation. The book is based on extensive research and interviews, and provides a clear and simple framework for data-driven transformation. The book is also engaging and relevant, as it uses stories, anecdotes, and examples to illustrate the concepts and principles of data-driven transformation. The book is not only a book about data, but also a book about people, as it covers the various aspects and dimensions of human experience, such as cognition, emotion, motivation, social interaction, and creativity. The book is a valuable resource for anyone interested in data, business, and the future of work.