Analytics and dashboards are perhaps the most often confused and interchangeably used terms. However, these terms are just as far apart from each other as they could be. Now, dashboards, we all understand and can imagine interactive, meaningful graphs and charts, but analytics can be daunting.
But I want to take a quick step back here first, and bring to the top of our minds the functions of dashboards. Then we’ll get to analytics.
Dashboards Answer the “What?”
Dashboards address our curiosity in that they serve to:
• Reveal the current state of our business
• Expose what happened in the business
• Disclose trends in our business
This information gives us something to chew on, and it is always the first step to touch, feel and assess what our data is telling us. Trends break down products by category (think line chart here), or even by time series, geography, heat maps etc. Dashboards also uncover the relative performance in our data. Think bar graph, scatter charts, or area charts.
All dashboard products enable us to share our insights and data discoveries throughout our organization via the ‘server’ option. Simply put, dashboards can help get out the word about data reveals.
Indeed dashboards lend themselves well to structured data discovery processes and structured sharing of those discoveries. Here the “what?” of data is brought to light with the excellent technological workings of the dashboard.
Analytics Answers the “Why Behind the What”
So if dashboards answer the “what,” then analytics answer the “why” behind the what. Analytics take it a step further, digging down deeper into the data. We might pose analytics questions like:
“When users search my site, what are the solid business outcomes/conversions?”
“Why did sales suddenly fall or increase? What are the causal factors, and how strong is the correlation between sales and these business drivers?”
I also call these business drivers as the ‘vectors’ of the business. There may always be a correlation between the business cyclicality and say, the migration of Monarch Butterflies, but is that a really a causal factor?
That’s really what we are looking for – and that is what analytics answers.
Once we have identified those six key vectors that really have a causal relationship to business outcomes, then and only then, should you start modeling and use the models for testing the historical performance and venture into predictive world.
When renowned statistician, George E.P. Box said, “Essentially, all models are wrong, but some are useful,” I believe he said it for those of us who jump in too soon to model without fully understanding the vectors of their business, and without completely grasping data meaning. Read more about George E.P. Box here.
Your Takeaway: Do not sell yourself short, start with just the dashboarding tool, if you have to, but I recommend investing in a true analytics solution right at the outset because sooner rather than later, you will mature to that point! This, I guarantee.