The Conversation: 3 Things to Know About the Direction of Data Analytics

During the recent 2021 Wharton Annual Analytics Conference at the Wharton School, University of Pennsylvania, leaders in business gathered virtually to discuss the latest business trends in data analytics.

The message across industries was clear: data is a powerful tool that companies large and small are using to make deeper, stronger, more effective decisions with the goal of improving everything from products and innovation, to their relationships with customers.

For analytics conference keynote speaker Jamie Moldafsky, WG’89, chief marketing and communications officer of Nielsen, a global data and analytics research company for the media industry, data has long been a guiding light. “As I look back in my career, there are common threads around the use of data,” she said. “It’s so critical to success whether you’re a general manager or a marketer — focusing on the ‘so what,’ asking the right questions, listening to the data, and always representing the truth is what makes companies strong.”

“It’s a very cool time to be in data and analytics, but the responsibility to use our skills as a force for good is a real one.” — Jamie Moldafsky, Nielsen

The strategic use of data is growing in strength and importance, supported by the machine learning and models that enable analysis. Here are a few of the most notable 21st century trends:

1️⃣ Over the next decade, every company will be transformed by Artificial Intelligence. Data has been described as the lifeblood of AI (human intelligence exhibited by machines). Systems that use artificial intelligence can analyze data from different sources and offer predictions about what works and what doesn’t.

As AI technology gets more sophisticated, so too does the process of data analytics. “Here at Google, AI has been something that we used in a few products to something we’re using in every product that’s out there,” said Rajen Sheth, vice president of Google Cloud AI and industry solutions at Google. “Everything from how you interface with Google Search to how you work with your Android phones to how you work with your Chrome browser all use AI to make the user experience better. Over the last five-to-six years, there has been dramatic transformation in a few key areas with AI…[They are]: Computer Vision and being able to interpret pictures and videos; Conversation: being able to converse with a computer and have it respond to you; Language: natural language understanding has taken big leaps over the last few years and is due for many big leaps over the next few years; and Structured Data: this is probably the most traditional way that people have used AI, taking tabular structure and being able to make predictions based on it. A lot of the new techniques are giving us better and better and better predictions as a result.”

2️⃣ Data analytics will improve diversity, equity and inclusion. A fundamental part of data analysis is figuring out the stories behind the numbers to gain more valuable insights and make better decisions. “This is especially important in the media industry,” said Moldafsky. “Audiences expect inclusion and demand stories that reflect the diverse experiences of their lives.”

She added: “The events of 2020 and 2021, up to and including the resolution of the George Floyd murder, have continued to bring into the public discourse the inequities and the injustices that exist in our society. I believe we have a moral obligation as business leaders and data analytics professionals to use our expertise as agency for positive change. It’s a very cool time to be in data and analytics, but the responsibility to use our skills as a force for good is a real one.”

This responsibility extends to understanding where biases – thinking strongly either for or against a person or idea – might influence the process of data analytics and the so-called outputs or conclusions people draw.

“2020 shook things up in a big way and private entities, corporations and other research organizations made a ton of information available to researchers, policy-makers and to the general public.” — Alex Arnon, Penn Wharton Budget Model

During an Analytics Conference roundtable discussion on “The Importance of Cultural and Social Intersections to Data Analytics,” Stephanie Creary, an identity and diversity scholar in Wharton’s management department, addressed bias in the hiring process. For a while, companies decided to rely only upon algorithms and machine learning to make the decisions about who to hire for a new job or who should get a raise in salary. But then lots of research started coming out to say that humans are deciding which data are being used to determine what is fed into the algorithm – so the biases inherent in human thinking, including racial stereotypes, are still influencing data-driven decisions.

“What needs to be done in the short-term and the long-term is for organizations and people who are part of these processes, such as analytics leaders and analytics teams, and people who work on human resources, talent, and diversity, equity and inclusion teams, to begin to work together in order to figure out how we effectively reduce biases in these processes,” said Creary. “It is very hard, we have found, for the analytics people and the people experts to get on the same page, but it is possible.”

3️⃣ We are experiencing a real-time data revolution. This is potentially exciting new for all you budding economists out there who like to follow the latest federal economic data, including monthly employment and unemployment rates and Gross Domestic Product, the summary measure of the whole economy.

During the pandemic crisis, policymakers have needed more “of-the-minute” data to guide their decisions, rather than waiting for those monthly statistics. “As we discovered in 2020, we just don’t have time to wait for the high quality, comprehensive but somewhat lagging information that we get from the headline indicators,” noted Alex Arnon, associate director of policy analysis for the Penn Wharton Budget Model, a research-based initiative that provides accurate, accessible and transparent economic analysis of public policy’s fiscal impact. “That is where real-time tracking of the economy comes in…2020 shook things up in a big way and private entities, corporations and other research organizations made a ton of information available to researchers, policy-makers and to the general public.” This information is coming from mobile devices and apps, enterprise services software that handles activities like employee scheduling at companies, payroll and earnings management systems and payment platforms that provide information about credit card transactions.

“Normally, if you wanted information on the labor market in a particular county, you might have to wait a year or two at least before the U.S. Bureau of Labor Statistics would get enough quality information,” said Arnon. “For people like me, this has been a revolution in what is available and how much we can keep an eye on what is going on in the economy from day to day.” He added that the data is less comprehensive and the quality is not as strong, so researchers are proceeding with caution in how they use this new plethora of data to guide policymaking.

Are you driven by data? Then be sure to tune into all the 2021 Annual Analytics Conference sessions and explore Analytics at Wharton, Wharton Customer Analytics and Wharton AI for Business to discover the latest research and trends in data analytics. Before long, Eric Bradlow, vice dean of analytics at Wharton and the K.P. Chao Professor and professor of marketing, statistics, education and economics, will no doubt get his wish: “Let’s make analytics an action word: Let’s analytics it!”

Conversation Starters

Nielsen's Jamie Moldafsky says, "It’s a very cool time to be in data and analytics, but the responsibility to use our skills as a force for good is a real one.” What does she mean by this?

Wharton's Stephanie Creary talks about the problem of bias in the hiring process. Imagine that you are going to become part of the solution here. After reading her insights, what role would you take in eliminating bias in the hiring process and making these decisions more effective?

This article talks about both human and machine-driven data trends. How do you define the roles for both humans and machines in the world of data analytics? Are both necessary parts of the process? Why or why not?

8 thoughts on “The Conversation: 3 Things to Know About the Direction of Data Analytics

  1. Numbers hold us accountable. This exploding reliance on data as a measurement tool is improving business and society in so many ways—here’s where we are and here’s where we need to be. And while data-driven decision making is powerful, it should also incorporate emotional intelligence. Emotions make us human and give us an advantage over even the most sophisticated machines because we can clearly see and appreciate varying perspectives that lend context and depth to those all-important numbers. Let us not lose sight of that essential balance.

    1. Diana, your assertion over incorporating emotional intelligence in machines to make them resemble humans is nonetheless true; one capable of making decisions and factoring certain emotions would make the perfect substitute for a human in the workforce. However, Stephanie Creary introduces the idea that bias has inevitably steered its way into the hiring process, and the cause in this case dealt with human input and activity towards the operation of the machine. When we think of emotion, then, is it really necessary for machines to feel and make decisions based on their emotion towards the subject? Or does that seem to introduce more bias than a robotic device?
      You mentioned that we as humans have a leverage to machines since we can delve further into certain subjects with our capability to factor in emotions and evaluate several perspectives. How would you say this is true? If our task with a large pool of numbers that happen to be salaries of Americans aged 20-29 is to evaluate which Americans will have a stable retirement life, we would say that those earning above X dollars a year is an intuitive method to determining retirement stability. Now if we factor in our emotions, we would make various statements over an individual earning less than an amount we consider low because our sympathy would affect our decision. We would ask, “Did some families consider donating supplies to this individual?” or “Is this individual studying and working at the same time? They may be extremely busy at the moment, and there is a chance this individual will understand priorities sooner than later.” On the other hand, our decision in observing an individual making 100 times the salary we consider high would vary as we may start asking questions like, “What if this individual never considered contributing to their retirement plans?” or “Couldn’t this individual have spent all their money during their working years?” These questions nevertheless spur from emotion; fear or anger. Even jealousy. A machine simply evaluating numbers like they are, numbers, would not factor in feelings or emotion to final decisions. In this case, is that not a good thing?
      Emotion adds bias to situations depending on a final decision. Especially in the hiring process, humans inputting initial data brings in even unconscious bias, making the system flawed. As much as we try to advance machines with AI and have them resemble humans as closely as possible, we must keep a distinct separation between the two. Machines, by definition, consist of several parts with a definite function that work together to perform a task. We must not stray from the purpose of these devices, for they will introduce several unknown dissections in our technologically dependent future.

    2. Diana, I agree that emotional intelligence is the main advantage humanity must preserve while becoming a more data-driven society. However, with the promise of a standard to measure performance (numbers), having a conscious mind will fall further and further out of reach. The reason this problem is so pertinent is that it’s a two-way street. Stuck in the middle, it’s impossible to go both ways at the same time. Similarly, combining two polar opposites–fact and emotion– is relatively unfeasible at first glance. Nonetheless, what if there were two people, each taking their own road? Perhaps employing two different standards, one addressing facts and another emotion, would be a possible solution to prevent this difficulty. Ultimately, it is evident that we are living in an ever-changing society, and we must strive to adapt in a healthy and beneficial way to avoid being swept away by the current.

      1. Rachna and Ethan, thank you both for your thoughtful responses to my comment about emotional intelligence. You added great insight and context to this discussion. Rachna, the motivation for my comment was not to envision a world where computers have emotional intelligence, but rather a hope (desperate?) to have a world where both machines and humans co-exist and bring their individual strengths to the decision-making process. You’re right, there are definitely cases where emotion leads to bias! This problem must be addressed. And I appreciate Professor Creary’s perspective that an important solution here is low-tech communication; getting hiring managers and HR professionals to sit around the table with analytics leaders and brainstorm, strategize, problem-solve face-to-face. I do see instances where purely data-driven analysis is necessary, if not beneficial, to sound decision-making. Great example on retirement numbers! However, I feel strongly that both heart and head are appropriate when the numbers tell only part of the story. Take, for instance, human-centered problem-solving. It may not be as cost-effective for a coffee seller to purchase and import cocoa beans from a small farmer in Nicaragua and pay prices that align with the hard labor involved, but companies with a conscience support fair trade practices for such farmers, partnering with them and ensuring they receive a fair wage.

        Ethan, I like your suggestion of leveraging both emotion and numbers in a systematic way. I feel this is where things must eventually land—otherwise, where is the humanity? Are we destined for a robotic existence? As you suggest, we must be intentional about considering the essence of what makes us human, while also embracing the tech-driven progress that is transforming society.

  2. Data analysis is a process of inspecting, washing, changing, and modeling data with the goal of discovering useful information, telling results, and supporting decision-making. Generally, I read blogs, in the blog I see the post about Content Writing Services it gives the best services and I also get the best service from there.

  3. It’s so critical to success whether you’re a general manager or a marketer — focusing on the ‘so what,’ asking the right questions, listening to the data, and always representing the truth is what makes companies strong.”
    Systems that use artificial intelligence can analyze data from different sources and offer predictions about what works and what doesn’t. This is what separates a successful venture from a one that isn’t . Data analysis is the path foward .
    1) Data analytics will improve diversity, equity and inclusion.
    2) We are experiencing a real-time data revolution.
    This is potentially exciting new for all budding economists to provide them with a place to stay updated without having major technological knowledge yet having abundant access to the data they require , including monthly employment and unemployment rates and Gross Domestic Product, the summary measure of the whole economy . This including many other fields highly benefits from this .

  4. A dominant business trend of our generation is the rise of the mass data collected, leading to the ascend of the data analytics industry. Information like what you want to wear and what type of book interests you may seem like worthless information but to companies, every bit of knowledge they control is potential profit. Once companies know what you like, targeted ads that may attract you to make purchases will turn into tangible profits for the company. Statistics and data points are inarguable and hold us accountable. Honing this information to make informed decisions is very powerful.
    With the growing abstract data companies store, many companies/governments are having trouble sorting and handling this tremendous amount of information. As an active investor in the stock market, I pay attention to new companies and industries that may be on the rise. Due to this business trend, I spotted companies like Palantir Technologies, stock symbol PLTR. After researching the company and finding that they have a huge market cap yet to be touched, I heavily invested in the company. Having tremendous success, Palantir thieves in the newly developing industry of big data analytics; despite the crashes recently in the stock market, their stock has soared over 120% in the past year. The company uses its software for data integration, quantitative analytics, and information management. The software public data sets across many companies and uses AI to distinguish obscure trends that can be used for predictive analytics.
    The development of this industry was definitely due to the covid pandemic. Palantir started working with the UK National Health Service (NHS) on supporting coronavirus efforts. By April 2020, the worst stages of the virus, several countries like Canada have used Palantir technology to track and contain the contagion. Another contributing factor to the success was Palantir’s development of Tiberius. Tiberius is a software is used to track vaccine allocation used in the United States built on the basis of data. Palantir was awarded a $44.4 million contract by the U.S. Food and Drug Administration, boosting its shares by about 21%. With this growing popularity, over 150 countries have used PLTR to help to build businesses. Even the largest industry institutions like JP Morgan, IBM, Amazon, U.S. Army, Navy, and CIA use this technology.
    Being conscious of the growing industry, I aspire to pursue a major in computer science and mathematical data science in hopes to study data structures and algorithms. I will be sure to pay attention to these companies and look where the future will hold for them. With my skill, I hope to start or be a part of these new companies that are breaking into uncharted territory.

  5. Let’s face it: We live in a world filled with information awaiting at our fingertips only a few taps away. In today’s digital age, I can not only gain valuable insights through relevant information, but I am also able to share it to many more people. Data around us is readily available, and it is up to our generation to utilize it for the better.

    The field of data analytics is wide in scope, and it has several applications in real world scenarios. Artificial Intelligence is often perceived as an intricate futuristic technology, but it is applied today with a heavy reliance on data. For example, machine learning(a part of AI) is built on the fact that systems learn better from data. I believe that Generation Z is best equipped to draw reason from data in order to make decisions promoting positive change. As Jamie Moldafsky mentions, it is important to understand and rely on data appropriately for success. Similarly, with a large range of data available, it is paramount to understand its significance in order to make well-informed decisions. This includes recognizing if any biases might be influencing the data as well as the importance of specific information over others in regards to the situation.

    Data analytics can enhance the social well-being of our world through a respect for all people. Many social problems today are complicated in nature, but data analytics has a role in identifying the roots of these problems, including biases, and using the information to locate places of potential action for making reasonable progress. I see our generation most involved in this trend, as it reveals the potential of data analytics to help tackle social issues that may plague certain communities.

    Aside from its use by individual businesses, data analytics can drive decisions at a federal level and in the public sector. With public policy, issues that are debated on are reexamined in an effort to make a resolution to the problem. The clear data in front of policymakers can be used to remove some of the biases surrounding a topic. On the other hand, if data is not properly utilized in a wholesome way, the conclusions can be fallible. For instance, if one only considers data supporting a particular standpoint and doesn’t synthesize other relevant data available in the argument, then the conclusions drawn may be misleading. The problem with this is that then the solutions proposed will not be as effective in addressing a given issue.

    On the other hand, data analysis in the private sector helps businesses make financial decisions and assess risks in ways that otherwise would not seem possible. From figuring out whether the business plan is realistic to setting adjusted prices to draw more customers, businesses can truly hone down on the management process.

    There is so much that can be done with raw data, but it would be fairly useful if decisions were complimented with empathy and compassion. Especially stemming off of social issues, there is so much to be dealt with. Whenever there are many needs to be met, it is important to demonstrate empathy and use the analysis to find a way that helps in the best possible way, without causing any harm.

    In the most simplest terms, the rise of data analytics means that there are endless options to learn about new things. From extrapolation of data to predict future growth to the ability to draw helpful findings, the possibilities are centered around information. However, at a much larger scale, it allows for people to be more engaged and focused on learning what data can do to benefit the world. Especially with Gen Z, data analytics can be used to solve social problems from the ground up. Through its use, there is no doubt that positive impact can be expanded to numerous industries and beyond.

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