Career Testing
Career Testing
Career Testing
ENTREPRENEURSHIP / APR. 24, 2014
version 6, draft 6

How to Handle Big Data by Creating Compelling Insights

big data
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Big data can be very overwhelming without organisation. Furthermore, you need large amounts of it in order to lower risks when making crucial decisions. Supporting your choices based on small amounts of information can result in losses and instability.

Mining large amounts of data requires key insights in order to turn the information into something useful. Let’s use website traffic as an example. Website traffic is essentially just numbers. You can’t do much with that. But once your business takes a closer look at the figures by separating them into different groups, then you’ll be able to apply the data more effectively.

This example can be applied to any part of an online campaign that requires big data. Email open rates, social media visitors and drives are other components of data than can be broken down into different types of insights.

What’s the Difference Between Data and Insights?

Data or noise is information that is irrelevant to your company’s needs. They are misleading because initially, when presented with large amounts of data, your first instinct is to put everything to use. Using the wrong information can break your digital campaign, sending you in the wrong direction. Some information needs to be categorised or organised in order for it to hold relevancy, such as tweets and blog posts.

Insights or signals are sets of information that can be applied to your business concerns. They are relevant and have the ability to support ideas and action. Working with insights cannot fully eliminate the risks involved with an online campaign, but it can make it more manageable and predictable.

Developing Insights From Big Data

When it comes to handling big data, you have to have the right categories or insights in order to get the answers you’re looking for. If you don’t now where to start, try asking questions. Below are some examples:

  • Who is viewing the website during peaks in online traffic?
  • What time is the website or product converting the most?
  • Why is your competition using a specific type of content format for engagement?

Based on the starter questions, you can begin to breakdown the information into insights.

Connecting the Dots

Once you’ve established the right insights, you need to connect the dots. An effective way in accomplishing this would be by creating a story. Using the questions above as an example, a story using insights could look something like this:

Sarah, a stay at home mom (key demographic), views the website at 2:00pm after lunch (peak traffic) before heading out to do errands. All her online purchases came after viewing the picture of the product (type of content that converts). Other websites that offer the same product uses video reviews, which Sarah may not have time to view because she is always in a hurry during this time.

This method of developing insights from piles of data is personal. It can make your audience more predictable. Additionally, this can affect your approach as well, making it warmer and more open minded. The best way to ensure that you’re on the right track is by testing the responsiveness of your findings in real-time.

Lastly, when it comes to big data classification, there is no such thing as an ultimate category that will give you all the answers to your problems. Valuable insights are composed of small, relevant elements. Because of this, variations of compiling big data are literally endless.

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