Thriving in a Data World: A Guide for Leaders and Managers

By Sangeeta Krishnan

This book focuses on the foundations needed to be successful in managing and engaging with data analytics initiatives, bridging the gap between creators and users of data.

Currently, every company, no matter its size, is data-driven in one way or another; using data to improve customer experience, as a new value stream, and to stay competitive. However, many business leaders, professionals, and students—such as executives, business analysts, UI/UX designers, project managers and marketing teams —are forced to interact with data and those who generate data, without being taught the general competencies needed to feel comfortable having these conversations.

This book focuses on the foundations needed to be successful in managing and engaging with data analytics initiatives, bridging the gap between creators and users of data. As a management reference guide, it discusses the different types of data strategy needed for succeeding with data, covering topics such as data team composition, types of data analytics, the importance of data storytelling, and identifying data ROI.

Framed by the author's personal story, the trove of information is made tangible through the compelling narrative with its unprecedented accessibility and readability for a non-technical audience.

If you suffer from fear of data, anxiety around conversations with technical teams, this practical approach book can help with actions you can start implementing right away.

• Language: English
• Publisher: Business Expert Press
• Release date: Dec 7, 2022
• ISBN: 9781637424179

Sangeeta Krishnan

Sangeeta Krishnan is an engaging business intelligence and analytics leader who possesses a winning blend of subject matter expertise and practical experience from a variety of industries. Most recently, she joined Bayer as senior analytics lead (director) for mass sales. She has worked with Fortune 500 organizations, not-for-profits, and everything in between helping various organizations in building their operations and monetizing data products –establishing a data analytics team from the ground up, building automated workflows to improve accuracy and seamless enterprise data handling. Sangeeta is a public speaker, content creator having articles published in industry journals and was recognized as a Finalist of the Women in IT Awards 2018 (USA) in the Data Leader of the Year category. Outside of work, she likes to travel with family and crochet.

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Introduction

Do you struggle with conversations about data?

Are you hesitant to ask data questions for fear of looking dumb?

Do you struggle with not knowing where to start with data?

Are you overwhelmed with the data terms and information available?

We hear so many buzzwords every day, like big data, machine learning, artificial intelligence, data-driven decision making, data insights, and many more. Take the word insights. Everyone uses insights these days, but without a common definition or understanding. The assumption seems to be that if we collect and use data, this magical insight will show up. Machine learning carries a similar mystique—people are liable to think of it as a very advanced field only a select few can understand. In addition, there are also so many data tools and the list keep growing, making it confusing to understand the starting point.

Can you name something that is part of your daily life that uses machine learning? Think supervised learning algorithms. Can you guess what I’m talking about? If you’ve ever viewed a notification on your smartphone like iPhone that reads, You have a new memory, you’ve seen machine learning at work. A decade ago, I never would have thought my phone would identify all my food-related photos and group them together with a title like Bon Appétit Over the Years. It is refreshing to see the chef side of me making so many mouthwatering dishes over the years, but I did nothing to create this memory. The data side of me knew the answer. My phone used on-device machine learning to analyze every photo in my library in a variety of ways, including scene classification (grouping my food pictures), people and pet identification, and audio classification.¹

Data is embedded in so many aspects of modern society, and integrated seamlessly, that we often don’t even recognize it as artificial intelligence. Our entertainment recommendations, modern cars—with drowsiness detection!—and fitness trackers are all driven by data and artificial intelligence.

Over the past decade, and especially during the COVID-19 pandemic, the data landscape has changed drastically. In 1880, it took the United States Census an estimated eight years to process census data for a population of 50 million. By 1890, the German American statistician Herman Hollerith had introduced the electric tabulating system, improving processing speeds significantly. The system was the first of its kind data processing machine to replace manual processing by hand.²

We have come a long way in terms of data processing speed, from several years in the past to the point where we now sometimes demand data available in close to real time. Today, every company, no matter its size, is data driven in one way or another. Companies are using data to improve customer experience, create a new value stream, and stay competitive. So, understanding data is a critical necessity for everyone.

I have worked with Fortune 500 organizations, not-for-profits, and everything in between. And within various organizations, I have repeatedly seen a gap between data teams on the one hand and leadership or business stakeholders on the other. These parties are not aligned in their data goals or in their understanding of the uses and capabilities of data. This book arose from my desire to close this gap and provide an easy-to-follow, technology-agnostic reference guide with relatable examples.

Many business professionals and students—such as business analysts, UI/UX designers, project managers, marketing teams, finance teams, and executives—are forced to interact with data and those who generate it. Few have been taught the general competencies needed to feel comfortable having these conversations. There are tons of information about getting into the field of data and working with data. How many articles, books, blogs, or videos have you read or watched in the past six months? This data information overload actually makes people less likely to retain knowledge. Our brains do not retain what we read unless we use and experiment with it. My aim is to enable people to want to learn more about data, to be curious about data, and who to reach the Data as a Hobby stage and wish to level up to analytical thinking. This book is arranged with the information you need to thrive in your organization—and nothing more. (No information overload!)

Working with data requires experimentation and questioning. It is a quest to discover the unknown, which are elements of a curious mindset. Unfortunately, curiosity is not considered an essential part of the recruitment process; neither is it encouraged nor promoted as a value in most organizations. But thriving with data is not about knowing a bunch of coding languages and technical tools. It is about maintaining a curious mindset, gaining a foundational data understanding, and seeking out answers to questions asked—and questions not yet asked in addition to the requested business requirements. If you develop a curious mindset around data, finding the right tool for the job will be easier (and will help you avoid learning a tool only to realize it is not the one you need most). This book focuses on developing that necessary core understanding about data.

This book is the go-to guide for any business reader who wants to understand the language of organizational data and feel comfortable conversing the language of data. What’s more, Thriving in a Data World is unprecedented in its accessibility and readability for the nontechnical reader.

This book focuses on the foundations you need to successfully manage and engage with data analytics initiatives, and to bridge the gap between the creators and users of data. As a management reference guide, it discusses the different types of data strategies needed to succeed with data, and it covers topics such as data team composition, types of data analytics, the importance of data storytelling, and identifying data ROI.

In psychology, Picture Superiority effect—or the famous saying a picture is worth a thousand words—refers to people’s propensity to remember pictures better than text. Data is no different, and it is therefore essential to gain the skill to tell stories with data. I have worked with several people who were technically skilled, but who suffered when it came to presenting the data findings. No one can act on data if no one follows the data analysis and insights. As a result, this book includes a chapter about how to persuade with data, irrespective of the tool you use for visualizations. Even if you follow and understand data, if your company lacks a data culture, it will resist making any data-driven decisions. This book discusses how to encourage a data culture and how to overcome challenges to data culture. It also takes a practical approach to Data Analytics: each chapter contains simple, straightforward actions you can take immediately to start implementing your newfound knowledge. It’s an enjoyable, engaging way to learn how to confidently interact, manage, and work with data analytics teams today.

CHAPTER 1

The Ever-Changing Data Ecosystem

We humans constantly learn and evolve over generations to build our modern society. However, at times, there are more sudden changes in the state of affairs thereby breaking the regular patterns creating revolutions. In simple terms, when we exchange one way of doing things for something altogether different, we hope a better society at scale. Industrial revolutions in the past have emerged in a quest to get to a better next progressive stage. Industrial Revolution 1.0, for example, involved coal-powered production. Industrial Revolution 2.0 entailed gas and electricity (mass production), and Industry 3.0 was electronics (automation). The boom of Internet and technology advancements led to the current revolution: we live in Industry 4.0—the digital age and the Internet of Things (IoT). This revolution is leading to the creation of a new raw material—data—and like all other raw materials, data needs to be used effectively to create something. Data is no longer seen as just something that benefits corporations by providing competitive advantage. Data is an economic driver: it accelerates the economic development of a country and creates more data in the process.

In the past decade, both data collection and data usage have gone through the roof. Everyday activities such as borrowing books from a library, banking, fitness and exercising, smart household appliances such as washing machine and microwave, driving cars, and even dating are all digital, and many are connected to the Internet. So they create a lot of usage and preferences data. This data helps industries to understand user patterns and behaviors and thus create better user experiences—which again generate data and value. A data ecosystem operates on a continuous cycle in which we provision with data to create more data and value. In this ever-changing technological landscape, something can become obsolete quickly, while something that was never a possibility can become feasible. Remember, there was a time when data was mainly used by technology companies, and other industries considered big data and analytics as tech-centered buzzwords that had nothing to do with them. Today, all companies—regardless of industry, size, or geography—need to invest in understanding their data to get ahead of the competition. The government also collects data for the wider social and economic development. A variety of services, like emergency and postal services, depend on accurate address information. Denmark, for example, released its standardized and unified address data to the public free of charge. This single Denmark Address Register (Open Data) has an annual return of economic benefit that is 70 times its maintenance cost.³

Let’s talk about another example. What do you think of when you hear the word Farming? It might be acres of land, soil, seeds, crops, or pesticides, and so on. But the word data does not immediately come to mind about conventional farming. Conventional farming practices have used pesticides and fertilizers, along with legacy knowledge and gut feeling, to increase yield. But modern farming is augmenting decision making with data. It’s a continuous cycle: -> Collect data using file sensors -> insights leading to value-driven farming -> create more data -> repeat. Climate Corp is transforming the agricultural industry by using detailed crop yield data, weather observations from one million locations in the United States, and 14 terabytes of soil-quality data—all free from the U.S. Government—to help farmers make informed decisions. A company like Climate Corp is feasible because it uses excessive open-source data.⁴

Traits of Data

Although there are several traits of data, some aspects such as quality, relevance, and completeness stand out. I like to remember it as 3 Ds of data traits—Discover, Digest, Doable. Data is discoverable, you know it exists and have a way to access it. Data is understandable, you can process and digest the data as an organization. Data is doable, meaning you can act on it in meaningful ways creating business value. If you cannot access the needed data or even know it exists, cannot understand or interpret the data, and unable to apply data to decision making, collecting high-quality data is of no use. Hence 3 Ds mentioned are critical traits to your data journey.

Data as Goods or Resource

Unlike other revolutionary industrial goods, like coal, data is nonrivalrous good. That means that even when data is used for one purpose, its quantity