Data is hot right now, and it seems everywhere in business these days there is another tool or framework on how to leverage data to drive a real difference in your business.
A good metaphor to understanding data effectively though, is training for an Olympic event (great timing for the metaphor, eh?). Most athletes train, not for the sake of training, but most likely because they want to stand on the podium with a medal as a statement to how good they are at the event. Anyone can train for the sake of training, but competing and winning is really the whole culmination of 4 years of tireless preparation and training.
The same is true for data visualization, in that the real goal should not be building dashboards and pretty charts, but pointing to the direct impact that data had on driving a top or bottom line impact on the company’s revenues. Yes, in larger companies it’s very hard to make a difference on the overall number, but every piece of data should tie to some positive contribution or else what is the point?
Yet, with so much data being made available, and so many people learning how to mine data for insights, there’s a lot of very pretty pictures out there which don’t move the needle at all. Yes, it’s great to hear about athlete’s and how they train, but the credibility isn’t there to the same degree as it is if they’e wearing an Olympic medal. If you’re a budding data analyst, or seasons chart builder, imagine if everything you built had a number next to it that said “this piece of data drove this quantifiable business impact”.
Though data visualization can serve qualitative benefits, such as monitoring a key business process or helping change a perspective on a topic, there should still be some way to tie even those things back to the difference it made (or could make) on the business.
Here’s three steps then, to help that mindset along
1. Understand the Reliability and Structure of the Data You’re Using
All because you have data, and have access to data, doesn’t mean it’s useful or all that important. You having access to log files from a server, which spits out information on usage patterns of your e-commerce system throughout the day only matters if you’re able to impact that usage in some meaningful way, then tie it back to a positive revenue lift. More importantly though, you have to know the data is reliable and understand how to model it in a way that it produces accurate conclusions. Build some baseline metrics, and measure against numbers you know are correct before going into any complex modeling exercise. Once you put a chart in a slide, it’s out there. So make sure you’re starting from the right data set to begin with.
2. Develop a Series of Hypothesis about Your Business
Once you know the data you’re working with, and have a good sense of how reliable it is, think about the business as it relates to parts you can actually control / influence. If you’re in advertising, don’t focus on product improvements. If you’re in product development, don’t worry about retention patterns on the website. Think about 3-5 gut instincts you have about how the business could operate differently, then use the data to test out those theories. Don’t simply mine the data, hoping the magical insights just pop out at you. Data, like a car, helps you get to where you’re going – it won’t take you there on it’s own. You need to at least have a rough idea of what you’re looking for, so you can build worthwhile visualization to help vet that hypothesis into an actual conclusion.
3. Focus on Low Hanging KPIs, then Expand from There
It’s easy today, with technology making more data accessible with less effort, to try and go after ground breaking insights. However, you no doubt have areas of your business you can directly impact and know will make a sizable difference to the bottom line, that you need help in influencing. Start with thinking through 3-5 KPIs coming from your hypothesis that you can build from the data you’ve vetted, to influence key business people or back up your assertions with a new direction you’re moving your team in.
If you can measure it, you can prove it (as long as it’s reliable and true), but remember that it’s never a silver bullet or the whole story. Data can be powerful, but it can also lead people astray or get people focused on the wrong things. When done right though, using validated data tied to a hypothesis and measured in a way that drives a meaningful revenue outcome, it can make a big difference.