1 – Don’t Fear AI
How Machine Learning Will Enhance the Analyst
Popular culture is fueling a dystopian view of what machine learning can do. But while research and technology continue to improve, machine learning is rapidly becoming a valuable supplement for the analyst. In fact, machine learning is the ultimate assistant to the analyst.
Imagine needing to quickly look at the impact of a price change on a given product. To do this, you would run a linear regression on your data. Before Excel, R or Tableau, you had to do this all manually and the process took hours. Thanks to machine learning, you can now see the product’s consumption in a matter of minutes, if not seconds. As an analyst, you don’t need to do that heavy lifting, and you can move onto the next question—were the higher consumption months due to an extrinsic factor such as a holiday? Was there a new release? Was there news coverage influencing product purchase or awareness? What you’re not thinking about is how you wish you could have spent more time perfecting your regression model.
There are two ways in which machine learning assists the analyst. The first is efficiency. With the example above, the analyst doesn’t spend valuable time on basic math. The analyst now has more time to think about business implications and the next logical steps. Secondly, it helps the analyst explore and stay in the flow of their data analysis because they no longer have to stop and crunch the numbers. Instead, the analyst is asking the next question. As Ryan Atallah, Staff Software Engineer describes it, “ML helps you look under lots and lots of rocks when you need assistance getting an answer.”
Machine learning’s potential to aid an analyst is undeniable, but it’s critical to recognize that it should be embraced when there are clearly defined outcomes. “Machine learning is not great when your data is subjective,” says Andrew Vigneault, Staff Product Manager with Tableau. For example, when conducting a survey to customers about product satisfaction, ML cannot always pick up on qualitative words.
Additionally, the analyst needs to understand success metrics for the data to make sense of it in a way that is actionable. In other words, inputs into a machine don’t make the outputs meaningful. Only a human can understand if the right amount of context has been applied—which means that machine learning cannot be done in isolation (without an understanding of the model and what inputs/outputs are being made).
While there might be concern over being replaced, machine learning will actually supercharge analysts and make them more efficient, more precise, and more impactful to the business. Instead of fearing machine learning technology, embrace the opportunities it presents.
2 – Liberal Arts Impact
The Human Impact of Liberal Arts in the Analytics Industry
As the analytics industry continues to seek skilled data workers, and organizations look to elevate their analytics team, we may have had a plethora of talent at our fingertips all along. We are familiar with how art and storytelling has helped influence the data analytics industry. That doesn’t come as a surprise. What comes as a surprise is how the technical aspects of creating an analytical dashboard, previously reserved for IT and power users, is being taken over by users who understand the art of storytelling—a skill set primarily coming from the liberal arts. Furthermore, organizations are placing a higher value on hiring workers who can use data and insights to affect change and drive transformation through art and persuasion, not only on the analytics itself.
As technology platforms become easier to use, the focus on tech specialties decreases. Everyone can play with data without needing to have the deep technical skills once required. This is where people with broader skills, including the liberal arts, come into the fold and drive impact where industries and organizations have a data worker shortage. As more organizations focus on data analytics as a business priority, these liberal arts data stewards will help companies realize that empowering their workforce is a competitive advantage.
Not only do we see a broad-base appeal to help hire a new generation of data-workers, we’ve also observed several instances where technology-based companies were led or heavily impacted by founders with a liberal arts education. This includes founders and executives from Slack, LinkedIn, PayPal, Pinterest and several other high-performing technology companies.
One powerful example of bringing in the liberal arts to a predominantly technology company comes from Scott Hartley’s recent book, “the Fuzzy and the Techie.” Nissan hired a PhD anthropologist Melissa Cefkin to lead the company’s research into human-machine interaction, and specifically the interaction between self-driving cars and humans. The technology behind self-driving vehicles has come a long way, but still faces hurdles when mixed human-machine environments persist. Using a four-way stop as an example, humans typically analyze situations on a case-by-case basis, making it nearly impossible to teach a machine. To help combat this scenario, Cefkin was tasked with leveraging her anthropology background to identify patterns in human behavior that can better teach these self-driving cars the patterns that humans follow, and in turn, communicate those back to the human riding in the car.
As analytics evolves to be more art and less science, the focus has shifted from simply delivering the data to crafting data-driven stories that inevitably lead to decisions. Organizations are embracing data at a much larger scale than ever before and the natural progression means more of an emphasis on storytelling and shaping data. The golden age of data storytelling is upon us and somewhere within your organization is a data storyteller waiting to uncover your next major insight.
3 – The NLP Promise
The Promise of Natural Language Processing
2018 will see natural language processing (NLP) grow in prevalence, sophistication, and ubiquity. As developers and engineers continue to refine their understanding of NLP, the integration of it into unrealized areas will also grow. The rising popularity of Amazon Alexa, Google Home, and Microsoft Cortana have nurtured people’s expectations that they can speak to their software and it will understand what to do. For example, by stating a command, “Alexa, play ‘Yellow Submarine’,” the Beatles’ hit plays in your kitchen while making dinner. This same concept is also being applied to data, making it easier for everyone to ask questions and analyze the data they have at hand.
Gartner predicts by 2020 that 50 percent of analytical queries will be generated via search, NLP or voice. This means that suddenly it will be much easier for the CEO on the go to quickly ask his mobile device to tell him: “Total sales by customers who purchased staples in New York,” then filter to “orders in the last 30 days,” and then group by “project owner’s department.” Or, your child’s school principal could ask: “What was the average score of students this year,” then filter to “students in 8th grade,” and group by “teacher’s subject.” NLP will empower people to ask more nuanced questions of data and receive relevant answers that lead to better everyday insights and decisions.
Simultaneously, developers and engineers will make great strides in learning and understanding how people use NLP. They will examine how people ask questions, ranging from instant gratification (“which product had the most sales?”) to exploration (“I don’t know what my data can tell me—how’s my department doing?”). As Ryan Atallah, Staff Software Engineer for Tableau, notes, “This behavior is very much tied to the context in which the question is being asked.” If the end user is on their mobile, they are more likely to ask a question that generates instant gratification, whereas, if they are sitting at a desk looking at a dashboard, they’re probably looking to explore and examine a deeper question.
The biggest analytics gains will come from understanding the diverse workflows that NLP can augment. As Vidya Setlur, Staff Software Engineer with Tableau also puts it, “Ambiguity is a hard problem,” so understanding workflows becomes more important than the input of a specific question. When there are multiple ways of asking the same question of the data (e.g. “What sales rep had the most sales this quarter?” or “Who had the most sales this quarter?”), the end user doesn’t wish to think about the “right” way to ask it, they just want the answer.
Consequently, the opportunity will arise not from placing NLP in every situation, but making it available in the right workflows so it becomes second nature to the person using it.
(To be continued…)