The pace and evolution of business intelligence solutions mean what’s working now may need refining tomorrow. From natural language processing to the rise in data insurance, we interviewed customers and Tableau staff to identify the 10 impactful trends you will be talking about in 2018. Whether you’re a data rockstar or an IT hero or an executive building your BI empire, these trends emphasize strategic priorities that could help take your organization to the next level.
- Don’t Fear AI.
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.
- Liberal Arts Impact
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.
- The NLP Promise
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.