Enabling customers and users to quickly find the value within a product is critical for many organizations and at the heart of being a product manager. The approach to driving user growth involves a growth mindset, combining qualitative and quantitative research methods, and driving impactful solutions.
In a company’s early days, the difference between C-level executives and the rest of the organization is simple — employees can walk away from a failure, leaders cannot. Under those conditions, certain types of people thrive in leadership positions and get a company from ideation to production.
For more than 20 years, OpenTable has connected foodies and novice diners with the restaurants they love. But how does its technology work on the back end? To make a long story short: data. Beyond the app and website, OpenTable provides restaurants with software that manages their floor plans, phone reservations, walk-ins, shift scheduling, turn times, and more.
It is becoming increasingly difficult to standardize taste. The myriad culinary preferences and gastric demands of the American population are reflected in the $997B valuation of the U.S. packaged food market in 2020. There has also been a push in recent years to augment trips to the grocery store with at-home meal kits and food delivery services, a trend further accelerated by the onset of quarantine restrictions.
Modern data and analytics leaders know that every business user is different. No two marketers or finance managers will use data in exactly the same way because no two share the same contextual view or understanding of the business. Their challenges are as nuanced as they are complex. And they need insights tailored to their specific needs if they are to be successful at solving business problems with data. Unfortunately, traditional BI tools treat everyone like carbon copies.
Customers wanting to drive self-service analytics as part of creating a data-driven organization will often ask, “Can we achieve self service analytics, when our work force has low data literacy?” Or they might say they are not ready for self-service analytics, incorrectly thinking they need first to improve data literacy. But the two are inextricably linked. I liken it to teaching a child to read without giving them any books on which to build their skills.
Supply chain and logistics operations can be a company's biggest source of financial risk or competitive advantage. The key is reconciling external supplier data like tariff and shipping information with internal data to deliver insights across teams and geographies.
As a modern data leader, you know that real-time access to data-driven insights is key to driving higher levels of business growth and innovation, and better customer experiences. You also know that when frontline employees have easier access to data they’re able to make better decisions that ultimately boost your bottom line. But what happens when employees don’t trust the data in front of them?
In 2020, the pandemic tested supply chains in a manner few have seen in our lifetimes, with businesses like Apple struggling to predict demand and keep factory lines moving. The weaknesses exposed by this crisis are not brand new, but they should be a wake-up call that current strategies are not sustainable. The limitations of modern supply chains were becoming apparent last year when companies struggled to react to new tariffs and restrictions caused by Brexit and the U.S.-China trade war.
Ask any analyst how they spend the majority of their work day and they’ll tell you: Performing remedial tasks that provide no analytics value. 92% of data workers report that their time is being siphoned away performing operational tasks outside of their roles. Data teams waste an inordinate amount of time maintaining the delicate data-to-dashboards pipelines they’ve created, leaving only 50% of their time to actually analyze data.
