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

by Diana Drake

During the recent 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 news 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 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!”

Related Links

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?

5 comments 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.

    • 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.

    • 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.

      • 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.

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