Applied Machine Learning certificate prepares professionals for data science careers

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Effective data analysis can make all the difference for a company’s efficiency. Advances in machine learning (ML) have helped data scientists harness the power of artificial intelligence (AI) and take their analysis to the next level. Brian D’Alessandro, head of data science for Instagram’s Well-Being and Integrity teams and author of Cornell’s Applied Machine Learning and AI certificate program, has 20 years of experience in ML model construction. D’Alessandro spoke to the eCornell team about the certificate, ML instructional approaches and career paths. 

What can students expect in the program?

“Even when students have learned how to build a model, I would argue that they haven’t learned to build a good model yet. There’s an empirical process required in tuning model parameters such that you get the prediction quality that you’re looking for. So, in this certificate,  we ensure students learn how to build a strong, basic model . . . how to tune it and validate it, and then we start to introduce more complex algorithms that are more often used in industry settings. We cover K-nearest neighbors, decision trees and linear models – the full spectrum. Throughout the program, we emphasize the consistency of the API across different machine learning algorithms.”

Are any important aspects of working with ML frequently overlooked?

“I really like to emphasize digging into data first before learning algorithms. The cliche at this point is “garbage in, garbage out,” and that’s very true for machine learning. So what I want to do here is build an appreciation for what will ultimately be 80 to 90 percent of the workload, which is data preparation. We emphasize the importance of it. [We] don’t dismiss it . . . this is the one area of machine learning that is often not spoken about or taught in textbooks.”

What about this program will help students upskill or pursue a career change?

“The program is oriented toward developing the core machine learning skills for most data science jobs or machine learning engineering. If you are a working professional and you want to get into either one of the fields, this certificate is designed to be one of the best starting points, presuming you’re co-learning, or have already learned, Python.”

Take the first step toward building successful machine learning algorithms in Cornell’s Applied Machine Learning and AI certificate program. Learn more and enroll now.

This story was drafted by eCornell marketing intern Justin Heitzman.

Supply chain management meets modern analytics in Cornell certificate program

While supply chain disruptions caused by the COVID-19 pandemic have mostly eased, predicting customer demand and other elements of supply chain management continue to present companies with complex existential challenges.

The crisis laid bare the vulnerabilities in traditional forecasting models – where a single miscalculation could lead to product shortages and severe revenue losses – and revealed the need for a more advanced data-driven approach.

The eight-week Supply Chain Analytics online certificate program from the Cornell SC Johnson Graduate School of Management equips specialists from various sectors with strategies to master key elements of supply chain management. Li Chen, professor of operations, technology and information management, leads the program.

“We know that the future is unpredictable, right? But we still want to make predictions. The idea is to look at historical sales and demand data, and based on that, utilize formulas to make good demand forecasts,” Chen said. “I teach this data-driven approach with a focus on measuring forecast performance as well. Every time you forecast, you should compare it to the actuals to gauge the accuracy of your predictions, so you can refine your methods over time.”

Diving into topics like demand variability and inventory management, the program provides a robust set of tools for actionable insights. It digs into inevitable trade-offs between reducing inventory and increasing transportation costs, offering a nuanced perspective to students interested in supply chain consulting and analyst roles or anyone who seeks to better understand the strategic aspect of operations management and logistics.

The instruction provided on industry standard operations and inventory management systems also highlights underutilized functionalities of software designed to improve the efficiency of supply chain configurations.

“This content will truly help people, especially those using systems like SAP or Oracle, to appreciate what’s underneath the hood,” Chen said. “My hope is that through this content, we can bring more attention and awareness to the potential end users.”

The program is directed toward entry-level supply chain analysts and those in management roles who run backend operations.

With a finely-tuned curriculum that balances theoretical underpinnings with practical insights, the Supply Chain Analytics online certificate program prepares professionals for disruption and how the unpredictable can become the expected. Learn more and enroll today.

Cornell AI Strategy certificate prepares leaders to leverage new tech

In the era of artificial intelligence (AI), professionals across sectors are racing to strategize ethical and sustainable applications of the technology. Many organizations are actively pursuing AI knowledge not only to harness its potential but also to ensure responsible implementation.

Cornell’s new AI Strategy certificate program – authored by Soumitra Dutta, professor of operations, technology and information management in the Cornell SC Johnson College of Business – offers a nuanced curriculum for leaders who are ready to leverage the power of AI in various business contexts.

“Today virtually every single employee in an organization needs to understand something about AI. It doesn’t matter if it’s the senior executive in the boardroom, office worker or factory floor worker,” Dutta said.

The program, which is available through eCornell, includes six courses. Students begin with an introduction to AI then explore knowledge-based technologies, machine learning and data-based approaches to the technology. Later courses cover AI implementations across sectors, societal effects and the tech’s future prospects. Each module is designed to be applicable to the real-world concerns of any professional aiming to comprehend how AI integrates with business and society.

Upon completion of the program, students will understand how to:

  • Assess applications of AI in their organizations
  • Apply knowledge-based AI technologies to their organizations’ standard tasks
  • Address challenges by applying machine learning
  • Design strategies to implement AI systems across an organization
  • Examine the societal implications of AI in areas such as labor, privacy and ethics
  • Envision the development of strategies to preserve human dignity and agency while embracing the benefits of the technology

In light of the rapid evolution of AI, the program maintains a dynamic curriculum, emphasizing core principles and skills for comprehending the fast-changing discourse surrounding AI.

“It’s like an AI boot camp, ” said Dutta. “The program is sufficiently light on the technology side to give you enough background but sufficiently deep on the context and the strategy side. It gives you the technical background while hitting on all kinds of things happening in our world right now,” Dutta said.

AI is more than a tool; it’s a strategic necessity. Cornell’s AI Strategy certificate program prepares professionals to navigate the exciting yet complex future of the technology. Learn more and enroll today.

Crunching Numbers: Understanding the Power of Statistics

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Imagine being able to transform raw data into actionable insights, shaping the direction of your business and your daily life. This power lies in understanding and applying statistics – the foundation of informed decision-making, the catalyst for impactful change and the key to unraveling the complexities of our world.

Cindy van Es, professor of practice at Cornell’s Dyson School of Applied Economics and Management and author of the Business Statistics certificate program, is expanding our comprehension of the study of statistics and its practical application in diverse fields. From agriculture to digital analytics, her work equips us with tools to navigate the complexity of both the corporate realm and our everyday lives, with statistics as our guide. Van Es shared her insights in the Keynote webcast “Statistics: What Everyone Should Know.”

How has statistics changed over the years?

“There are so many things after teaching it all these years, but . . . it’s present in every field these days. Even when I was going through education, it was very much the scientists, but it’s moved into so many fields now. The explosion I’ve seen over my career, from the very quantitative fields, to now: Every field has a metric. So it’s good to have a little idea of what goes behind some of these things.”

What are some surprising ways statistical information is used?

“When I think about the kinds of jobs my former students have now, they work for Airbnb, or Expedia, or Hilton or in finance. Even in marketing, now: A lot of stores will track your eyes . . . to see how long you look at a product, and they can correlate that data with the scanner data to see whether you bought it or not, and did the red label make you buy it more than the blue label . . . . There are experiments going on all around you, even when you’re not aware of it. Maybe you work in a nonprofit and you’re doing an amazing job, and it’s a very meaningful project, but in order to get funding, you may have to quantify why it’s amazing: What are the outcomes, and what are the metrics? There’s so much now: It’s kind of ubiquitous.” 

Which type of statistics is the most challenging to learn?

“Statistics has two branches: Descriptive and inferential. Descriptive is when you take a sample, you describe what you have and you ask the questions: Do I want to make a graph of this? Or do I want to make a table? Or calculate what we call ‘summary statistics?’ Most people are pretty good at that. Inferential is where you want to make an inference about a broader group, about a population. If you see a poll in the news, you’ll see a little plus-or-minus margin of error. That’s because they’re doing inferential statistics. When you see ‘this percent of people in the country think this,’ it’s based on a sample – so what you’re doing is making an inference. That part of statistics is a little harder for students and people in general, I think, because first of all, the language of inference is probability . . . understanding risk, understanding probabilities, the human mind really doesn’t think that way. So inferential is usually more challenging.”

Is artificial intelligence being used in statistical processes and interpretation of data?

“Each new technology – computers, and then supercomputers, and then desktops – influenced how people teach statistics and use techniques . . . . Now the merger is more with computer science and info science, as opposed to just being applied to agriculture, or medicine, or biology. Now the whole discipline is merging. Statistics hasn’t caught up with how to use [artificial intelligence] yet . . . statisticians are just starting to look at it.”

Harness the power of data interpretation in Cornell’s Business Statistics online certificate program. You’ll develop a dynamic set of skills that can heighten your confidence, fortify your decision-making, and catalyze meaningful change.

Drafted by eCornell writing intern Milan Lengyeltoti, with first round edits from marketing intern Justin Heitzman.

New Cornell certificate helps create the ethical data science workplace of the future

We increasingly place our trust in algorithms, whether applying for a mortgage, a new job; or making personal health decisions. But what about the security system that uses facial recognition and locks out a 55-year-old office custodian from her night shift? Or the groups of people automatically cropped out of photos on social media? These are the unintended, and often unfair, consequences of data science tools amplified across millions of users. They’re also highly preventable.

This is the lesson that lawyer and epidemiologist M. Elizabeth Karns embeds in every data science and statistics course she teaches in the Department of Statistics and Data Science. Her students will be deciding how to use data in the future, and while bad decision-making in business isn’t new, Karns says it’s the accelerated and aggregated effect of today’s data science applications that’s so dangerous: individual, team or even a whole company’s worth of decisions, can instantly affect the lives of millions of people. Moreover, the torrent of new technologies is moving faster than our regulatory systems, leaving a gap in accountability. Even data scientists themselves often don’t know exactly what’s happening inside their algorithms.

Read the full story on the Cornell Chronicle website.

Johnson offers a new degree via eCornell: the Master of Science in Business Analytics

The Samuel Curtis Johnson Graduate School of Management, in collaboration with eCornell, is launching a new, online, STEM-certified degree program, the Master of Science in Business Analytics (MSBA).

Johnson’s MSBA is designed for working professionals who seek to build a career in analytics with the skills employers say they desire most: a comprehensive understanding of the language and concepts of business, strong communication and teamwork skills, and the ability to apply the tools of data science to real problems and real data. In this hands-on program, students will collaborate in diverse teams and build a portfolio of data analytics projects as they learn to use essential analytics tools like Excel, Python, R, SQL, and Tableau to collect, visualize, and analyze data to optimize business decisions and evaluate results.

“Success in business analytics requires technical skills, creativity, and critical thinking,” says MSBA program director Vishal Gaur, Emerson Professor of Manufacturing Management, professor of operations, technology and information management. “Our first goal for students in the MSBA program is to excel at these skills by learning from their Cornell University professors who have deep expertise across a wide range of fields. Working with data also poses new challenges and requires awareness of its limitations with regard to bias, fairness, privacy, and ethics. So our second goal for our students is to become careful about the implications of analytics for society and to be aware of the incredibly transformative power of data, so that they can be responsible and effective problem solvers.”

The MSBA degree program offers students a flexible curriculum that allows them to specialize in a concentration aligned with their professional goals. The four concentrations include:

● Marketing Analytics—designed for students seeking careers in fields such as digital marketing, marketing analytics for consumer and business-to-business products, and product marketing.

● Finance Analytics—designed for students seeking careers in fields such as corporate finance, fintech, lending and credit analytics, banking and investment analytics, and fraud detection.

● Operations and Supply Chain Analytics—designed for students seeking careers in fields such as retailing operations, supply chain analytics, revenue management, manufacturing, and digital businesses including marketplaces and gig economy firms.

● Business Analytics—designed for students seeking careers in fields such as healthcare, human resources, and education.

“Data-driven problem solving is at the heart of the Cornell MSBA,” says Mark Nelson, Anne and Elmer Lindseth Dean of Johnson. “Our program features an extensive business core, a choice of specialized concentrations, an emphasis on communications and collaboration, and an applied, hands-on curriculum to instill creative and resourceful problem solving. We designed the program this way because our research with practicing business-analytics professionals and employers indicated a pressing need for talent that combines those capabilities.”

All courses will be taught by Johnson faculty and faculty in the broader Cornell SC Johnson College of Business. Faculty and industry practitioners will also serve as mentors for the capstone projects.

Instruction for the first intake of the 16-month MSBA program will begin online on May 16, 2022, and run through August 2023. The degree program includes two one-week, intensive summer residency sessions, the first in Ithaca, New York in summer 2022, and the second in New York City in summer 2023. It also entails a substantial capstone project which will unfold over the duration of the program and culminate in industry-ready final deliverables.

Applications for the program are now open.

Learn more about Cornell’s MSBA

This story first appeared on the Cornell SC Johnson College of Business news site, BusinessFeed.

Cornell Johnson Launches STEM Master In Business Analytics

Cornell Johnson College of Business is the latest US business school to launch a Master in Business Analytics (MSBA) program.

Cornell follows other top US schools like MIT Sloan School of Management and UCLA Anderson School of Business, who offer top-ranked MSBAs.

The STEM-designated program will be taught part-time over 16-months and will be predominantly online.

Cornell Johnson MSBA: The curriculum
Demand for data analytics skills is growing. According to the GMAC Corporate Recruiters Survey, 37% of recruiters hired MSBA grads in 2018, compared with a projected figure of 62% in 2021.

This increase means MSBA grads are likely to be able to land roles across a wide range of industries, with the vast majority of companies implementing data into their business models.

“The need for data analytics in organizations and society is increasing rapidly,” says Vishal Gaur (pictured), program director for the new MSBA. “We have been working towards this program for the last two years, meeting with alumni and recruiters to assess the requirements from business analytics professionals.”

Cornell’s new MSBA looks to capitalize on this trend, offering students the chance to develop their analytics skills and knowledge of analytical tools.

The program is scheduled to begin in May 2022, and will run until August 2023. It will be delivered through Cornell’s award-winning online learning platform eCornell.

Teaching will be delivered asynchronously, giving students the flexibility to view online sessions at a time that suits them. But there will be some in-person requirements; Cornell’s MSBA curriculum includes two week-long residency sessions in the summers of 2022 and 2023. These will be held in Ithaca and New York City.

The program is also designed to encourage students to work together, Vishal says.

“It will be run like a residential Johnson program with small cohorts, extensive interaction with faculty and fellow students, and co-curricular activities that allow students to expand their knowledge and network outside of the classroom,” he explains.

The curriculum covers core business elements along with soft skills such as communication and teamwork. This is combined with a focus on analytics skills; students will learn to use tools like Python, SQL, and Tableau to gather, analyze, and visualize data.

Industry-focused specializations
Along with the flexible teaching delivery, the curriculum also features six elective modules covering topics like Machine Learning for Investment and Customer Analytics and Strategy.

During a specialization period, students can choose from one of four different concentrations. These include Marketing Analytics, Finance Analytics, Operations and Supply Chain Analytics, and Business Analytics.

Each specialization is designed for students aiming to launch careers in their chosen field, and so covers a range of analytics functions within that industry. The Finance Analytics specialization, for example, includes teaching on subjects including corporate finance, fintech, and lending and credit analytics.

The program also requires students to work together in teams on a variety of projects. During the program each student will work on a Capstone Project where they will seek to solve a real-world issue using a large data set and analytics tools. They’ll present their findings to MSBA faculty members.

Applications for the Cornell MSBA are now open. If you’d like to enroll in this cutting-edge program, Cornell estimates tuition will amount to around $78,000.

Master data science programming in R with new certificate program

In a world run by data, the demand for this skill has never been higher. Data analytics is essential to almost every facet of decision-making across any organization. Glassdoor recently named it the #1 job in America, and in the top 3 must-have skills. Cornell’s new certificate program, Data Analytics in R, is designed to help take a fundamental understanding of analytics to a mastery of programming in R.

Ideal for any professional looking to scale their skills and knowledge, this program will teach techniques for understanding, modeling and visualizing data using R, including predictive and prescriptive analytics, machine learning, the Monte Carlo simulation and optimization methods for making both small and large scale decisions.

“The world has really progressed when it comes to data analytics. Today it is being used across all organizations and verticals, be it financial services or consumer goods or travel, to enable informed decisions on a daily basis,” said Chris Anderson, faculty author and Professor at the School of Hotel Administration within Cornell’s SC Johnson College of Business. “We’re now at a place where these are critical skills for people who want to set themselves apart.”

The program consists of three three-week courses:

  • Predictive Analytics in R
  • Clustering, Classification, and Machine Learning in R
  • Prescriptive Analytics in R

Upon completion, participants will receive a Data Analytics in R certificate from Cornell University. Learn more about this program by visiting the eCornell website.

Want Better Data? Build from the Bottom Up.

At Cornell University’s Center for Hospitality Research, one of the main aims is to make research available in a digestible format for those in the hospitality and service industries. A large part of that work involves helping the industry not only collect significant data but to make sense of it in order to make better business decisions.

As part of eCornell’s webcast series, the center’s director, Professor Chris Anderson, joined eCornell’s Chris Wofford for a discussion on data analytics and why industry professionals should adopt a bottom-up approach to data. What follows is an abridged transcript of their conversation.

Wofford: Welcome. Let’s talk about data-driven analytics and what the bottom-up approach means.

Anderson: The first thing to note is that good analytics is not necessarily new. I’ve been in this space for a little more than 25 years now. What’s really happened in the last five to ten years is that analytics have become much more accessible — and with that new accessibility comes lower costs. As a result, it’s become much more widely adopted.

But I think we’ve kind of lost a bit of what I refer to as the bottom-up approach, which is involving those who are critically close to the business itself in the data analytics. You need to have an understanding of where that data came from, what potential variables you’re missing, and how it was sampled. In order to get the most out of data analytics, you need a firm understanding of the business itself and how things should be working towards some sort of outcome. In the opposite scenario, the top-down approach, we let technology tell us what’s going on and we sort of let the data drive the solution.

Wofford: Can you give us a real-world example of what you mean?

Anderson: I come at this historically from the hospitality space, from the demand and pricing side of things. That space to me has always been fascinating because, in order to price and control a hotel or an airline, you really have to have a fundamental understanding of where demand comes from, how the business manages that demand, and what kind of decisions they can make. You really get this deep insight into how you make money.

So for a lot of data analytics, that becomes this core set of skills and once we’re good at it, then we really understand our business well and it brings a lot of opportunities for us.

Wofford: What kinds of data analytics are relevant to the hospitality and service industries?

Anderson: There are three basic forms of data analytics. The first is what we refer to as descriptive, where we’re just describing what has happened or just reporting. It’s kind of a backward view of the world.

Our second is the predictive world, it’s the forward-looking part of analytics where we’re trying to use our insight from reporting to help us look at relationships and make predictions about the future. And then predictive analytics goes one step further and tries to see what factors resulted in us achieving previous metrics, what we might do to impact those and what the future outcome might be.

The third part is prescriptive analytics. Once you understand where you’ve been and have a good sense of how to go forward, then you want to use some tools and techniques to make sure you’re going forward in the profit-maximizing or cost-minimizing sort of way.

It’s about using a set of tools to help us do the best going forward, given the insight that we’ve been able to extract from this both descriptive and predictive framework.

Wofford: What are those tools? What are you looking for within the data?

Anderson: We use things like optimization, where we are looking at making multiple decisions at a time. We use things like decision analysis and programming.

We work on incorporating uncertainty into our decisions. No decision is made out of certainty, so we don’t want to just ignore that. We want to make decisions knowing that there is some uncertainty and once I make one decision I can adjust to those uncertainties and make subsequent decisions.

We use different tools if there’s a lot of uncertainty that’s evolving over time and we might use another set of tools if there’s so much complexity that it’s hard for us to map out how things are all working together.

We think about the starting block as being reporting. Your goal is really to understand how well you’ve been doing, so you’re focused on key performance indicators. How was I pricing? How was my competitor pricing? We are just looking at some of these things together in concert with our backward-looking metrics.

This really lays the groundwork of the predictive part, in which we are trying to understand that these things may be impacting some of our key performance indicators, and we may look at those in different ways.

Even before we can start to do this we’ve got to collect the data, put it in a data warehouse, and have it organized in some sort of centralized way. One of the trickiest parts about this is we have to make sure that we have a lot of integrity around that data. We want to have a secure process from which we can extract, pull and analyze, but we don’t want to necessarily change that underlying structure.

There are a lot of pieces we have to make sure are lined up so that if we have lots of users, they are not going to distract from the quality of that data.

Wofford: In your experience, do you find that most companies have their data in order or when you go to work with them, or do you find you have a lot of work to do right out of the gate?

Anderson: For most organizations, it’s about getting their data house together. It’s often not well organized.

Wofford: So getting that data organized is almost always the biggest challenge?

Anderson: That’s right.

Wofford: Once things are put in order, are we then looking at the predictive component? You mentioned using this to reduce uncertainty – how do we do that?

Anderson: Well, let’s say you are looking at what your sales were last year. That would provide a naive estimate for the next year, right? But while you might be able to take last year’s average, there is a lot of variance around that average. So our goal is to generate a better estimate for the future that has less variance around it, so it’s a more refined guess. We try to make less naive guesses by using information from other attributes that may be impacting sales. If we know those factors going forward, that will help us refine the estimate for whatever that metric is, whether it’s sales or some other key performance indicator. The predictive part is all about reducing uncertainty and we do that through different kinds of relationships.

Wofford: Like competitive analysis, for instance?

Anderson: Right. How my competitor is pricing relative to how I’m pricing. But we have to be cautious because there’s no point in looking at the impacts of relationships unless you know those factors in the future. My sales are a function of how I price and how my competitors price but I don’t necessarily know how my competitors are going to price tomorrow or next week or next year.

Once we’ve got those two parts under our belts – the reporting and the predictive – then we can start to make better decisions going forward instead of just shooting from the hip. And that entails using a lot of these mathematical tools, along with our knowledge, intuition and expertise, to look at some of this complexity.

The prescriptive part is getting us beyond just making obvious logical decisions and trying to look at how things are interconnected. We don’t necessarily jump into this part unless we have our foundations in the information because the prescriptive modeling component is going to need inputs from reporting or inputs from our predictive components. They’re the critical first two steps before we get into part three.

Wofford: And the prescriptive element involves running a simulation in some way?

Anderson: Yes, you could think of it like that. You can think of a hotel trying to set optimal prices to maximize revenue. To do that, the hotel owners have to have some estimate of future demands and ideally some estimate of future price-dependent demand. That estimate of future price-dependent demand from our predictive analysis will then be input into our optimization models to help us formulate those decisions going forward.

Wofford: We hear a lot about things like “text analysis” and other new techniques that help us look beyond simple numeric data. Can you tell me about that?

Anderson: Think of Amazon reviews. We’re selling products on Amazon and we’re looking at what consumers are saying. We have to be cognizant that other consumers are reviewing that content. They’re paying attention to that average review score on Amazon but they’re also actually looking at what people said about the product. So we need to look for keywords and repetition of those keywords.

Yes, I could read all that information manually, but we can now use tools to help us pull up keywords and their frequencies to help us get a sense of what’s going on.

Wofford: I’m guessing this is probably common across all industries at this point.

Anderson: Yes, because now you can review anything. And there’s hardly any business that doesn’t have some sort of online chat service where consumers are typing information. So it’s about trying to look at what questions they’re asking, what problems they’re having with your product and then asking yourself how you can use that data to improve the product.

There’s just so much unstructured text today so we’re trying to look for ways to streamline how we extract insight because we don’t have infinite time to read it. Most of the tools for analyzing text are pretty standardized and most of the algorithms that we can use have been well developed. We’re ten-plus years into things like sentiment analysis so it’s not like we have to reinvent the wheel. There are a lot of off-the-shelf approaches.

Wofford: I’d like to turn to a question from the audience. Peter, who identifies himself as a “non-analytics person” posed this question: “In terms of decisions, I sometimes hear, ‘The numbers don’t support that.’ But it’s often on content that I know has not been marketed. So it seems the decision may be made on numbers that are correct, but that the decision comes from a faulty premise. Is this something you see often?”

Anderson: One of the classic things that I see is that organizations think price is going to impact demand, and they think they are changing prices but what they’re really doing is moving prices seasonally. And when things move together, you can’t really tell the impact of the season versus the price, because those are both adjusting together.
So one of the things we see in that data is that we may not have created the right kind of variance in order to see the outcomes.

Most of us don’t experiment with our business on a regular basis but in order to get insight from data, we have perturb those inputs. It’s just like the science experiments with two petri dishes, where you pour bleach on one and not on the other one to see what kind of bugs grow.

We have to have that experimental mindset when generating this data, because if we’re not making those little perturbations to our business practices, then it’s very hard for us to see how A leads to B because we’ve never manipulated A. Or we’ve only manipulated A at the same time we’ve manipulated B, C and D. If I always drop prices and spend more on marketing together, it’s hard for me to unravel which of those was the driving factor. Our data will not tell us that unless we’re cognizant from the business standpoint of having manipulated those things in such a fashion to generate that variance.

Wofford: So to glean real insight, you’ve got to be willing to take risks?

Anderson: Right. Be like a scientist and do some experimenting. You know, the online world has dramatically changed because of what we call A/B testing. Now it’s so easy to tweak something, so we can do all of these little A/B experiments. It’s very easy to create variances and see the outcome.

Wofford: So in some ways, you describe this as a linear process, but at the same time, it’s not. It’s iterative.

Anderson: It is. One minute to the next. The goal of predictive analysis is to look for robust insight into the future. And that is where, for me, the bottom up approach is critical. Yes, we’re trying to understand your business model but nothing is constant. There could be a new competitor, underlying changes in dynamics or some sort of disruption happening. In order to be robust to those changes, the models that we build from the predictive framework have to be grounded in our business practices.

And that comes from this bottom-up approach, versus just letting the data tell us what’s going on. For me, as a data analyst, it’s always about thinking about my two minute elevator pitch. How do I justify my models and can I clearly explain those models in layman’s terms? If I need to use statistical terminology to explain my insight and my models, that is going to tell me that I’m not necessarily grounded, that I’m relying on the data versus relying on my intuition.

It’s some give and take. You have to go back and forth, but the more bottom-up you are, the easier it is for you to justify models and to communicate those models to other people.

Wofford: I want to thank Chris Anderson for joining us today.

Anderson: Thank you, Chris, this was great.

 

Want to hear more? This interview is based on Chris Anderson’s live eCornell WebSeries event, A Bottom Up Approach to Data-Driven Analytics and Why We All Need to Be Involved. Subscribe now to gain access to a recording of this event and other Hospitality topics.