Common Mistakes You're Making with Your Data
Businesses rely on data. Numbers can show mistakes that decision-makers within a company made in the past, and they can direct a business the right way by predicting the future of the market. However, data can be this useful only if you know how to input, analyze, and interpret the numbers.
If you make mistakes during these three processes, then the data of your company isn't just useless, but it's also extremely harmful – it can guide you to the wrong conclusions that can drive your business into the ground.
Therefore, you need to be aware of common mistakes and do your best to avoid them.
Applying all metrics at your disposal
If you put all the metrics on one datasheet, you won't be able to do anything with it. By doing so, you'll create a difficult read with highly concentrated pieces of information that you won't be able to interpret properly.
If you want to actually understand what a certain metric is telling you, you need to pay attention to fine details. Numbers are all about precision; if you fail to file them properly, they will become misleading.
Not using filters
Trying to do something with data in bulk is a lost cause. Even if you're a small business, you need to filter your data and sort it into categories.
For example, if you're trying to estimate page views of the official website of your company, you need to have in mind that not all of those views are coming from reliable sources. Chances are your employees have visited it for hundreds, even thousands of times. This particular inflow of traffic isn't relevant to your business at all.
Using only one dashboard
For each of the metrics you intend to apply, you should use a different data dashboard. This problem is similar to the first one we listed – one dashboard subjected to multiple metrics won't be very helpful.
So, you should either apply related metrics to one dashboard, or use a separate dashboard for one metric. This is the only way to achieve high precision during the interpretation of your numbers.
Not having goals
Every calculation you make needs to have a purpose. So before you actually apply an analytics tool to your data dashboard, make sure that you have a goal for it. If not, you'll just end up with results and numbers that have no concrete meaning to you.
Not using tools for visualization
Interpreting sheets of numbers requires an enviable level of focus and concentration. One of the things in the job description of a data analyst is that they must possess a sharp eye that's resistant to mistakes.
However, you can't expect your boss or a stakeholder to have the same skillset. During your presentation, you need to wrap the results of your analysis into a neat package. Visualizing your data isn't a time-consuming process – you can simply transfer them into pie charts and graphs within seconds.
Making mistakes during the mining process
The most common problem that leads to a series of mistakes happens during the process of data mining. Unlike visualizing data results, this is a long, demanding process that requires full attention throughout.
If your team isn't qualified enough for the job, perhaps you should consider outsourcing mining to a business that focuses only on that. This is a smart step, and especially so if you already had to deal with mistakes that were traced back to improper data mining.
Everything starts with the initial part. If your team fails to conduct it properly, then each following action will be pointless.
Now that you're familiar with the most common problems regarding data, you should be able to steer clear of them. However, it's not enough that only you, as an individual, apply this newfound knowledge, so make sure to share it with your team. Once every member of your team is familiar with the common mistakes, you'll be able to bring the efficiency of your whole business to a new level.
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This is a guest post by Jacob Haney. Jacob is a content marketer presently working with Research Optimus, a business research outsourcing company. A writer by day and a reader by night, he is loathed to discuss himself in the third person but can be persuaded to do so from time to time.