Marketers around the world are embracing data-driven marketing to drive better results from their campaigns. However, while doing so, you need to work with a lot of data and this could lead to some big data mistakes.

But why use data-driven marketing in the first place?

When you collect data about your audience and campaigns, you’ll be better placed to understand what works for them and what doesn’t. This will mean that you can customize your campaigns better and drive results.

So, what can happen if you end up committing big data mistakes?

If you don’t manage your big data well, the mistakes may end up giving you incorrect insights. This, in turn, can negatively impact your marketing campaigns.

To help you identify and resolve these mistakes, we’ve put together this guide on the various big data mistakes that marketers tend to make.

Big Data Mistakes You Must Avoid

Here are some common big data mistakes you must avoid to ensure that your campaigns aren’t affected.

1. Ignoring Data Quality

One of the biggest big data mistakes that you can make as a marketer is that of ignoring the quality of your data. You need to sort your data, tag it well, and even quality control it to ensure that the data points are relevant and accurate.

But why?

If bad data infiltrates your analytics data, then it’s likely that it may affect the insights that you can gather from it. This, in turn, would mean that your campaigns will be affected, leading to wastage of your marketing budget. It would also go against the entire point of using data for marketing.

To avoid this, you should consider:

  • Adding meta-tags
  • Coming up with a taxonomy governance
  • Applying version control
  • Scan data regularly to correct problems

2. Using Small Datasets

The whole point of using big data to make informed marketing decisions is the fact that it removes inconsistencies. This is because numerous data points help average out errors.

However, when you choose to use small datasets from your big data for marketing campaigns, there’s a greater chance that these inconsistencies will be amplified. As a result, it could affect your campaign and reduce its effectiveness.

The solution to this problem is to use as much relevant data as possible and analyze it. This can help you get more accurate insights.

3. Analyzing Data Without a Goal

When you’re using big data for marketing, you must also know the reason why you’re using the data. Without having a set goal in mind, you won’t be able to analyze the dataset accurately and derive relevant insights.

If you’re tracking a certain set of metrics without understanding why you’re tracking them in the first place, you’ll likely end up wasting your budget. Additionally, it could also lead to failed campaigns if those weren’t the metrics you were supposed to track.

What’s the workaround for this?

You should write down the goals of your marketing campaigns right at the onset and also find relevant benchmarks for the same. This can help you not only understand why you’re tracking the metrics but also allows you to put all your resources to work to achieve the goal.

4. Not Having a Data Architecture Plan

Data quality matters, but along with that, even its structure matters. When you’re dealing with big data, it’s essential that you manage it well. Without a data governance framework in place, you won’t be able to find and retrieve the required data with ease.

What’s more?

Your data might end up in silos and then, given the vastness of the data, you might end up with constraints when you want to retrieve it to obtain insights.

To solve this big data mistake, you need to establish a data architecture plan for its storage. Additionally, you should invest in high-quality data storage options — be it cloud storage or on-site storage. It also helps to use low-latency tools for managing data to manage it well.

5. Incorrect Data Visualization

While it’s important to find and manage your data, visualizing it is equally critical. Failing to do so is one of the biggest big data mistakes that you can make as a marketer.

But why is data visualization important?

Once you’ve gathered the data and derived insights from it, you need to have a method in place to showcase that data. That’s where data visualization comes into the picture. It’s a great way to represent your data and its insights in a manner that conveys all the information with ease.

The idea is to present your data in such a way that it becomes easy for your audience to grasp the information while saving time too. Additionally, the visual nature of the data presented will also mean that it will be visually pleasing for the audience.

It helps to leverage design tools to quickly come up with visuals and infographics to represent your data.

6. Following Misleading Trends

One of the other common big data mistakes that you can commit would be following the wrong trends. While you’re tracking and analyzing data, you might encounter new connections between various data points that may appear to be trends.

However, sometimes, these may not be actual trends and might appear could be occurring due to different phenomena.

How can you avoid them?

The best way to avoid these misleading trends is to look for the cause behind them. This can help you understand if you should be following them or not.

Final Thoughts

Big data forms an important part of a big data marketing strategy. However, it’s important to understand big data mistakes and stay away from them to ensure your strategy is error-proof.

You should pay attention to the data quality and architecture to make sure that the data is helpful and easily accessible. Also, you should avoid small datasets to reduce the chances of errors creeping in.

It’s equally important to analyze your data with a specific goal in mind. You must also pay attention to data visualization as it can help represent your data in an easy-to-understand way.

Finally, make sure that you identify and stay away from misleading data trends.

Do you have any questions about the big data mistakes mentioned above? Ask them in the comments section below. 

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