Being Smart with Data Analytics

Being Smart with Data Analytics

Being Smart with Data Analytics
Over the past few years business leaders have been throwing around phrases like big data and advanced analytics in internal reporting and analytic scrums and discussions. While those buzz words may sound smart during a meeting, there is one verykey component, that is not as easy as it seems for businesses and their analysts to be conscious of; being data smart.

Your company probably has a lot of data coming from many internal and external sources that you hope can be managed in a way that can help improve your business. The data might be an email subscriber list or a website dashboard or a market research report, or a practically limitless list of other possibilities.

Well, if it isn’t put together correctly the answer is absolutely nothing. One expert in the world of web and data analytics, Avinash Kaushik, often talks about the difference between web reporting and web analytics, and preaches the difference between what he calls data puke and critical thinking accompanied by meaningful observations. The data puke is a presentation, report, or spreadsheet that simply displays numbers and how they’ve changed without substantive observations or recommendations. The data puke won’t help those unfamiliar with the data understand any problems or solutions any better, so especially when you’re presenting to executive stakeholders and clients, it is crucial to turn the analysis level up a notch.

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To follow Avinash’s wisdom, it’s important that your team is linking the correct data with important key performance indicators (KPIs) in order to generate the right metrics which can effectively inform meaningful observations and business decisions. To do that we have to start in the right place.

Many businesses start with Google Analytics to analyze their website visitor data. They begin tracking certain standard items like page views, bounce rate, landing pages, etc. A good starting point, perhaps, but it can be easy to select incorrect metrics without a structured approach. Without a goal in mind it can become very easy to begin down a data rabbit hole.

Enter: KPI identification and identifying analytic maturity. This is of utmost importance. A good analyst needs to have an understanding of your business. To take it a step further, having a hypothesis in regards to what should be expected will also contribute to what’s important to focus on.

Here’s a graphic that shows an example of goal setting in web analytics. You can see that the three distinct goals are Create Awareness, Generate Leads, and Highlight Events. Each one has unique KPIs, data sources and segments, and benchmark targets.

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The point here: KNOW WHERE YOU’RE GOING.

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