{"id":7252,"date":"2018-01-18T17:50:19","date_gmt":"2018-01-18T17:50:19","guid":{"rendered":"https:\/\/www.gosquared.com\/blog\/?p=7252"},"modified":"2019-11-28T11:08:29","modified_gmt":"2019-11-28T11:08:29","slug":"avoid-data-paralysis","status":"publish","type":"post","link":"https:\/\/www.gosquared.com\/blog\/avoid-data-paralysis","title":{"rendered":"How to avoid data paralysis"},"content":{"rendered":"

\"Data<\/p>\n

Data is like garbage. You’d better know what you are going to do with it before you collect it. \u2013\u00a0Mark Twain<\/p><\/blockquote>\n

At GoSquared, we\u2019re lucky to work with both innovative early-stage customers<\/a> and bigger, more established enterprise clients<\/a>.<\/p>\n

In many situations though, we find ourselves in conversations with customers at both ends of the spectrum discussing how to interpret the data they collect.<\/p>\n

Smaller start-ups often find themselves in a position where they “don\u2019t know what they don\u2019t know” so initially try to track anything and everything.<\/p>\n

Larger, more established companies often have staff that inherit a tool with a wealth of data, but it’s overwhelming and impossible to make sense of.<\/p>\n

Both scenarios can lead to data paralysis \u2013 where you have access to so much data that you’re afraid to make a decision.<\/strong> You fear making a call because you’re terrified of not having considered every possibility.<\/p>\n

Focus on the action<\/h2>\n

If you\u2019ve found yourself hit with a sudden case of data paralysis, then the top advice any of us can give is to focus on the action<\/strong> you’re trying to take.<\/p>\n

If you have existing data, you may want to focus on purging or archiving that around a timeframe that makes sense to you.<\/p>\n

Don\u2019t be afraid to archive older, irrelevant data. You don’t necessarily have to completely delete older data, but clearing it out of your primary day-to-day view can be a huge relief \u2013\u00a0like clearing out your spare room full of junk.<\/p>\n

Another area of increasing importance \u2013 if you\u2019re holding personally identifiable information (PII), deleting unnecessary data is a very sensible approach with the impending changes in data laws such as the GDPR<\/a>.<\/p>\n

If you’re hesitant to delete old data, try auditing your existing data to see if it can help you take action today. How has your product, market, and customer changed? How much relevancy does your old data have?<\/p>\n

The less relevant, the less impact it will have on the action you end up taking from it.<\/p>\n

Start with an objective<\/h2>\n

If you\u2019re new to tracking and analysing data, start with an objective, but most importantly think of the resulting action<\/strong>. In a lot of situations people start hunting for insights before they even know what they\u2019re looking for.<\/p>\n

For example, look at a scenario where you may want to track the effectiveness of a new piece of content you\u2019ve produced. Think about the action you\u2019ll take off the back of this.<\/p>\n

If your content performs well, will you focus your content strategy around producing similar pieces of content?<\/p>\n

If it performs poorly, what alternative pieces of content or strategies can you consider?<\/p>\n

It may seem straightforward, but having a success or failure action pre-planned avoids you hitting a \u201cnow what?\u201d moment when you analyse your data.<\/p>\n

On the note of success and failure, you may struggle to find existing benchmarks and find yourself thinking \u201cis this data telling me the result is good or bad?\u201d Don\u2019t be afraid to trust your gut on this in the beginning until you build enough relevant data to validate your understanding.<\/p>\n

Disagree and commit<\/h2>\n

Importantly, don\u2019t wait for perfect data<\/strong>. It never will be. If you\u2019re running experiments, the best data is the data you can control without external factors influencing it. The longer you leave data before you take action on it, the more variables can come into play that effect the quality of it. Give yourself a timeframe in which you plan to take action after running an experiment and stick to it.<\/p>\n

Regardless of your size, the ability to take decisive action is always a competitive advantage. It’s very easy waste hours in meetings deliberating people’s interpretation and opinions of a dataset. As Jeff Bezos of Amazon says, it\u2019s better to \u201cdisagree and commit<\/strong>\u201d \u2013 you ideally want to act with 70% of the data you need. If you wait for more you’ll move too slowly.<\/p>\n

Keep it simple<\/h2>\n

Finally, use tools that focus on simplicity and encourage you to take action. If you struggle to understand the tools that help you understand data, it\u2019s going to be to an uphill battle \u2013 for you and your team.<\/p>\n

When exploring tools, it\u2019s always tempting to focus on the capabilities you may need in the future. First and foremost, focus on what\u2019s going to help you today<\/em>, and worry about how you can scale later.<\/p>\n

There’s a wealth of tools that can help you with metrics and data, but if you\u2019d like to learn more about how GoSquared can help you, we’d love to chat<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Data is like garbage. You’d better know what you are going to do with it before you collect it. \u2013\u00a0Mark…<\/p>\n","protected":false},"author":25,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1452],"tags":[96,1333,598],"class_list":["post-7252","post","type-post","status-publish","format-standard","hentry","category-engineering","tag-analytics","tag-data-driven","tag-measurement"],"yoast_head":"\nHow to avoid data paralysis - GoSquared Blog<\/title>\n<meta name=\"description\" content=\"There is such a thing as "too much data" \u2013 the amount of information we have now often leads to data paralysis. 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