Insight Through Analytics and Data

Business Intelligence Data and Analytics 

Here ‘s a really interesting fact: We will create more data in 2017 than the previous 5,000 years of human existence – combined.

This abundance of data available has created a new in-demand position: data analyst The average annual salary for a data analyst is well over $100,000 and the demand for them is currently and should continue increasing rapidly

There are amazing benefits to businesses utilizing data analysts. Insights into customer behaviors, evaluations of the user experience, and social trends are the tip of the iceberg of what type of information is available by through utilizing the solutions of the analysts who share the insights enterprises require to optimize marketing campaigns and increase customer value.

Analytics are meant to provide insights into how a digital experience helps users reach their objective online. Data analytics is a process that is continuously being refined.

Utilize Analytical Tools

Data is the most valuable asset of an enterprise, but it’s easy to take the wrong path. With so much information available, how do you know what’s important, what’s somewhat important and what can be left out? 

The biggest mistake taken by those looking to use data  to gain access to information that delivers insights and provides a competitive edge is in the failure to identify and choose the right data. In order to be successful and gain meaningful insight from data, you have to know what you’re looking at and why it matters. 

Successful data analysts have the ability (and experience) to know what type of data is going to be most appropriate in a particular situation. For example, there are descriptive analytics, predictive analytics and even prescriptive analytics. Failure to realize these are interrelated typically results in an under-performing data analytics initiative and could have disastrous effects.

Analytics platforms provide different types of data. The best way to gain insight from that data is to combine it with data from other systems. That can be extremely complicated and that’s why you have to ask questions when considering a data management solution. The key question is: “How easily can this information transferred”? Some other questions you’ll want to ask are: What other solutions is it compatible with? How about reporting?  What features are in the dashboard? Ask a lot of questions and then test the solution out. 

Every enterprise is different, but it’s crucial to have a solid understanding of the key metrics that should be monitored.

Avoid Data Overload 

It’s easy to get sidetracked when you’re looking at spreadsheets full of data. That’s when analysts run into data overload. The main issue with analytics solutions today is they are being used incorrectly. Yes, you track unique visitors, channel of acquisition, average order value, etc. But, that insight only tells you so much. 

There are many underlying metrics. One could argue that there are too many. For example, A/B testing – you know how your current website is performing, but what if you changed an element? Would it improve the user experience? Typical answer, “we’re not sure”. Well, you should be sure because unless you don’t update your website, the answer is in the data. 

Many web pros get trapped in using standard analytics solutions that only report on metrics like page views, time on site and bounce rate. That’s great information to have, but it won’t position you to improve. For that, you have to dig deep into the data. If you use Google Analytics though, that’s about to get much easier.  Google is introducing a feature that will let you ask questions and get relevant answers in Analytics. This will be done through the utilization of machine learning. The  machine learning feature will give marketers the ability to better understand data and implement solutions that act upon those insights. 

The element of an actual human sorting through the data must exist, but you can expect machine learning and artificial intelligence to replace humans in many data analysis capacities.  Machines will tell us a lot about the user experience and it will be the responsibility of the data analyst to shape that into actionable web improvements.

The sheer amount of data that’s available now can feel overwhelming to under-prepared enterprises and the marketing department, those who know what they need from an analytics initiative, know how to get there by utilizing the right suite of solutions and know the  are those best positioned on the path toward data analytics fluency and a more profitable enterprise in the short and long run.

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