At some point in our lives, we all figure out the difference between “hearing” vs. “listening”. Often we learn the hard way. Maybe you were presenting a conference and three quarters of the audience were staring like zombies into their mobile phones. Or maybe a close friend was trying to share some important news while you were distracted by other matters. Hearing is predominantly a passive act and doesn’t require much effort. If a sound is audible, even just so, your ears can hear it and store it. The act of hearing can fill our brains with a lot of data, whether we choose to “process” it or not.
No matter the circumstance, only hearing (failing to listen) almost always results the same way; misunderstandings and missed opportunities.
Listening, however, is active and requires intent. The goal in listening is to understand and is inherently a dialogue.
The concept of hearing vs. listening is also very relevant when it comes to big data and analytics. As databases and technology continue to evolve at a breathtaking pace, most organisations are able to “hear” data at great scale. But most companies still have lots of room to improve their data listening skills. Let’s take a look at three of the more impactful areas of data listening.
Classifying and organising data is essential in order to make sense of it. Over the last three decades, data professionals and databases have gotten very good at handling sales data, dates & times, product hierarchies, customer segments, etc. But today’s big data teams are faced with new and ever evolving challenges; unstructured data such as blogs and reviews, determining sentiment and intent on social media, location, weather, and the list goes on.
An organisations ability to listen to data is directly related to its skill in enriching data into insights. This requires both the latest tools and techniques (machine learning, natural language processing, modern graph network technologies, etc.), as well as creativity and curiosity. There are many industries where competitors have largely the same raw data available. But those who put more effort and science into enriching data tend to drive more actionable insight.
Having a mountain of insights at your disposal is next to useless if you don’t follow the path it is leading you on. So many times we see business leaders looking to their big data solutions to justify actions they have already taken or that fit with their current plans or incentives. When the only tool you’re holding is a hammer, everything starts to look like a nail.
Learning to ask big, broad questions of your insights can dramatically increase understanding and drive an organisation forward. Finding incremental improvements for your current activities should not be overlooked, but be brave and tackle the hard stuff.
A Two-way Street
If good listening is practiced as a dialogue, each party needs evidence that the other’s level of understanding is rising. For big data solutions, demonstrating to customers that they were “listened to” often comes down to operations; how fast and accurately can we listen to their behaviour and reflect that behaviour in their next experience.
This is one area where investments in automation and speedy data flows become extremely evident. Most organisations routinely automate repetitive functions in order to reduce labor costs. But many still lag when it comes to automating the feedback loops in their big data solutions. If you truly want to listen to customers and serve them in the most relevant way, automating key steps in your data and learning processes is paramount.
In fact, last week I purchased a promoted item on a retailer website that I frequent quite often. Later that day I got a confirmation email and a tracking number for shipment. Three days later I received an email from the same retailer inviting me to purchase the very same item.
So they “heard” my sale but clearly weren’t “listening”.