Analyzing Your Analytics Solution
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Analyzing Your Analytics Solution

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Philip Lima
Tuesday April 04, 2017

In today’s data-driven world of digital commerce, the demand for business intelligence and analytics solutions is greater than ever. The need for access to more data is nearly universal for nearly any business or industry, from healthcare to retail. Such solutions, of course, require investments of both time and money. In an effort to save on costs, some consider implementing their new application with their own internal resources - many times done by individuals who have little to no experience or track record of successful BI implementation.

This underinvestment can lead to failed user adoption or poorly informed decisions caused by inaccurate or limited interpretations of their data. What a number of companies have come to realize (often through these painful lessons), is that the improper setup, customization, and use of an analytics solution can result in missed ROI for the application, and even worse, a lack of trust in the ability for the application to support the business. This failed attempt can cost the business more than the initial software and implementation cost.

In this article, we will look at some fairly common mistakes and errors that can cause analytics solutions to go from useful tools to problematic headaches.

Many of the problems encountered by analytics users are the result of hasty or poorly executed choices made during the solution’s initial setup. Below are some questions that should be answered before implementing a new analytics solution.

Have the proper Filters Been Chosen? Poorly or wrongly setup filters can lead to all sorts of headaches, for example, when it comes to tracking your website traffic data. Any legitimate analytics solution should provide users with filters capable of removing any traffic from Spam-Bots or Web Crawlers from your data pool. If these filters are not activated during setup, then you will be provided with an inaccurate view of your website traffic. In a 2015 report, published by Imperva, it was found that “nearly half of all internet traffic (49 percent)” was performed by various web bots. This means that failure to setup the necessary bot filters will leave you with an inaccurate view of your website traffic. The data shows you that over 1,250 unique users are visiting your website each day, when the actual number is only 613 visits a day.

What Data Is Needed? Missing or incorrect filters in the data extracts can lead to all sorts of headaches, for example, when it comes to tracking your website traffic data. Your analytics solution may need to provide users with filters capable of removing any traffic from Spam-Bots or Web Crawlers from your data pool, or it may be required to exclude it all together. If these filters are not configured properly during setup, then you will be provided with an inaccurate view of your website traffic. In a 2015 report, published by Imperva, it was found that “nearly half of all internet traffic (49 percent)” was performed by various web bots. This means that failure to setup the necessary bot filters will leave you with an inaccurate view of your website traffic. So if your data shows that over 1,250 unique users are visiting your website each day, the actual number might be closer to 613 visits a day.

What Level of Detail Is Needed? Knowing what data you need is only part of the battle. Your data may be so detailed that the most granular view of the data is meaningless and the only sensible way to use the data is at an aggregate level. On the other hand, you may require the ability to drill down to data specifics. If your users are not working with detailed enough information, or if data details are not being brought into your analytics solution, you are limiting the value of your data. Analytics solutions only work as well as the data they are given.

The answer is not always to simply bring in the most granular data possible, because depending on the solution you are using, it may not perform well at scale if you bring in all your data. Additionally, by bringing in useless data attributes, you and your users will be flooded with information that isn’t relevant, and this too can jeopardize user adoption.

How Will Your Data Be Visualized? Data is no good if it is not being properly visualized. Keep in mind that what the business thinks they need and what they actually need may be different. For example, a pie chart showing the sales distribution of inventory items over the last 30 days might be considered relatively useful. However, it does not allow you to see which items sell more often at particular days, times, seasons, etc. This is why pie charts are considered one of the least informative ways to view data (see links below). This further drives home the importance of identifying the kinds of views and displays that will be the most beneficial to your organization’s strategic needs.

Why Pie Charts Are Bad - What do you mean I'm not supposed to use Pie Charts?! - The Worst Chart In The World - Pros and Cons of Pie Charts - The only thing worse than one pie chart is lots of them

Remember, an analytics solution is a tool, and as with any tool, they work best when used properly and for the right tasks. If used in the wrong manner or for a task to which it is not suited, however, it has the potential to do more harm than good. Keep in mind that products don’t solve problems, people do. Just because you buy a hammer, it does not mean that it will build a house for you, or that the house will never need upgrades and maintenance.

Make sure you get the most value out of your investment by starting off right, with a good implementations and roll out plan for your new analytics application. Mashey has a track record of success in BI implementations of all sizes and across many industries and use cases.

Contact us if you would like to discuss how we can help make you more successful with data analytics.

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