Simple datasets just won’t cut it anymore. To get truly powerful insights, you need to pull in data from multiple sources. The more complex and diverse your datasets, the more surprising and potent the insights they’ll produce. Additional data sources increase your chances to inform actions, fueling top-line and bottom-line growth.
How does it work in real life? Read on to find out how Measuremen optimizes workspace utilization, Skullcandy minimizes product returns, and Air Canada improves airline safety.
Measuremen: Optimizing facilities use with data from numerous sources
When Measuremen CEO Vincent le Noble began the company in 2005, he wanted to help his clients make the best use of their workspaces. He leaned into his facilities-management experience, taking careful note of how organizations used desks, chairs, meeting rooms, and amenities. At the start, Measuremen observed and recorded utilization data and began to log the kinds of activities each space enabled (such as collaboration, individual work, and high-concentration work).
Since those early days, Measuremen has broadened its data sources. A mobile app is used to collect granular information about how each component or meeting space is used. The app not only documents utilization data, but allows users to add subjective inputs such as personal preferences. According to Vincent, it allows Measuremen to ask customers questions such as: what made users come to the office, how they foresee the future for their departments, whether they will grow, whether they will shrink, and what kind of activities they will be doing.
In the last two years, Measuremen has added location-based sensors, which record data on a more permanent and real-time basis. The result of the various inputs — self-reporting, app-based logs, static sensors, and other data sources — allows Measuremen to assess much more than unused desk spaces or underutilized meeting rooms. It can give businesses a better understanding of how employees experience the workplace and let companies tailor their resources to better suit employee needs. As a result, companies can proactively address more challenging problems like employee productivity, retention, and work-life quality. And Measuremen is hardly finished.
“We’ve been deepening our analysis for the last four years now with Sisense,” said Vincent. “All the data streams combined … give us the insights and decision-making power to help users to improve workplaces and work life for employees. And that journey is still going on.”
Skullcandy: Listening to the market
The datasets you collect, the way you combine them, and the insights that can come from them depend on your industry, the realities of your business, and your imagination. Personal audio brand Skullcandy had a huge dataset and a huge challenge: using analytics to explore returns and reviews data to inform future product decisions.
Machine learning and predictive modeling allowed the company to use complex historical warranty claim and cost information, previous and new product attributes, and forecasting data to create a predictive data model for future warranty costs. The information not only helps Skullcandy with resource allocation for future warranty fulfillment, but can also drive design improvements.
Skullcandy’s methodology included delving into sentiment analysis gleaned from online reviews and other customer feedback, which gave rise to exciting revelations. For example, when customer comments focus on a particular defect, Skullcandy can pinpoint the problem, examine the future warranty claims effects, and engage its engineers to make design modifications to help head off these returns. Skullcandy is also exploring ways to use disparate data streams to inform decision-making that improves customer relationships, customer education, and e-commerce ecosystems.
Air Canada: Taking data to new heights
The value of large, varied data sources is becoming obvious in air travel, too. Air Canada uses Sisense to collect and translate a wide variety of safety, quality, environmental, and security data. Safety Analytics & Innovation Manager Shaul Shalev said, “We collect hundreds of gigs of data … but unless you have a clear method of slicing and dicing that data and presenting it to users, it’s not really useful. With a tool like Sisense, it changes the game altogether.”
The ability to collect data and make it useful allows Air Canada to identify important insights and extract actionable intel so frontline employees can respond to it in real time. In a more forward-looking posture, AI can employ the data to predict component failure, so Air Canada can replace parts before they cease functioning.
Seek out varied datasets to transform your business
The power of vast, varied data sources isn’t constrained to any segment of the economy. Here are a few more stories of companies finding success with large, complex datasets:
- Healthcare technology leader Glytec used data to help improve care during COVID-19
- Wheelchair-accessibility company BraunAbility synthesizes sales, marketing, and logistics information to determine promotions’ success and forecast future campaigns
- Crowd Media, a micro-job firm, collects huge amounts of marketing, operations, and financial data and uses it to identify best-performing channels, improve earnings, and increase customer retention
As you can see, leveraging diverse datasets to generate game-changing insights spans industries. The takeaway is that you should cast the widest net you can and leverage whatever data you can get your hands on. The benefits you’ll reap from bringing that data together can help drive revolutionary change at your business and further evolve you in a tumultuous business world.
Adam Luba is an Analytics Engineer at Sisense who boasts almost five years in the data and analytics space. He’s passionate about empowering data-driven business decisions and loves working with data across its full life cycle.