There is a trend right now due to the evolution of digital media called cord cutting. Cord cutters cancel their traditional cable TV subscription and replace it with any combination of digital streaming systems, like Hulu, Netflix, Apple TV, and Roku.
With this new entertainment option, there is definitely the potential to save a lot of money. However, it could potentially end up costing consumers way more. They have much more power to make decisions regarding what to watch and when to watch it. As it is often said, though, with great power comes great responsibility. Take the Roku device for example. With the Roku device consumers have access to over 1,000 channels of content. But, each of those channels has a different business model. Is the channel free or does it cost money to access their content? Do consumers want to pay a monthly fee or purchase individual shows à la carte? Will the content contain commercials or be delivered commercial-free? Can consumers download the videos to view them on their mobile device when they are away from a WiFi signal? All of the sudden, the act of TV watching requires that consumers know more in order to make the right decisions. Blindly using the Roku device, without applying that knowledge, could result in a very costly entertainment experience.
How does this relate to analytics? The same fear and consequences exists. With the continuous evolution of technology over the past few decades, the half life of a skill is shorter and shorter. Like with many other innovations in today’s world, old knowledge and skills need to be unlearned and then new knowledge and skills need to be acquired. If we rely too much on evolving technology and don’t evolve our knowledge in parallel, people who use the technology to make data-driven decisions have a high risk of failure. We are in a consumer-driven world, and we are providing powerful analytic capabilities to users to make important business decisions every day. But those users need the knowledge and data literacy to make insightful decisions.
For example, knowing how to create a bar chart may not be enough knowledge to reliably apply an analytics tool to generate a visualization which allows you to address your specific business question. Bar chart A, as shown in the image below, is based upon a correctly interpreted data model, where purchase data is associated with merchant data and merchant data is associated with category data through unique values in key fields. This data model allows the amount totals to be grouped appropriately in order to generate a bar chart which allows you to compare the total spending in each purchase category.