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3 Data Quality Issues To Be Aware Of

Poor data quality is a problem every company needs to be on top of. So, the more data quality issues you can avoid, the better.

Elizabeth Moss

By Elizabeth Moss

BairesDev Business Development Executive Elizabeth Moss is responsible for partnership growth, increasing profitability, and customer acquisition.

10 min read

data analytics

Regardless of the industry that you work in, you’ve probably heard a million times how valuable data is for companies today. People are even calling data the “new oil” of the century, and I can’t say I know a single business leader that isn’t interested in managing the continuous improvement of their data strategies. 

Even so, there’s a cost to managing data—and that cost is guaranteed to scale as your company grows. That’s why avoiding data quality issues is so important. The cost of bad quality data is often higher and more unpredictable than anyone anticipates – the US economy alone loses $3.1 trillion per year due to poor data quality.  So, taking a proactive approach to data quality is always a huge plus. Here are 3 things to keep in mind while doing that. 


#1 Anomalies Scale With Data

Finding logical patterns in huge datasets has been one of the greatest challenges data scientists have had to face this decade. Today, we can all agree that data doesn’t always follow a logical pattern, and one of the main reasons behind that is what we know as anomalies. 

I like to believe that anomalies (also known as temporary fluctuations) constantly show up in data patterns to keep us sharp and on the move. And, no, we’re not talking about the seasonal fluctuations due to well-known events like Christmas or Independence Day here. We’re talking about the seemingly-random and short-lived patterns that threaten the decision-making process. 

At the early stages of growth, manually investigating and adjusting for outliers is feasible. But, as your company grows and collects more data, you’ll start running more and more often into new anomalies, and you’ll need the help of more powerful tools. Most companies today make use of custom machine learning algorithms through software development services that allow them to automate the most difficult tasks of this process. Getting powerful technology on your side is certainly a good way to prevent anomalies from snowballing into your decisions. 


#2 All Data Models Have Volume Thresholds

As proud as you might be about the current data model you’re using in your company, know that no data model is made to last forever. There’s always a certain volume of data that will exceed the capabilities of your current data model and introduce tons of inefficiencies. In other words, most data models will begin to break down as data volume increases. 

This isn’t necessarily the consequence of a faulty data model—that’s just how things work right now (or at least until quantum computers bring some groundbreaking magic to the table). All that you can do is accept the truth: most organizations will run into data quality problems as they grow, and they won’t become apparent until the data volume reaches a certain threshold. 

To make matters worse, we’re still dealing with the extreme levels of uncertainty generated by the pandemic. In the next few months, we must be specially prepared to deal with problems in our data models. The changing behavior of consumers, businesses, and the market itself will present a lot of new challenges for modern data models. Be ready to re-scale your data in the mid-term. 


#3 Data Can Be Easily Wasted and Misused

I believe most companies today are collecting data and using it to inform decisions in one way or another. That’s just a standard of competitiveness today. However, I also believe that most companies don’t actually have a data strategy in place, which could lead to a lot of wasted opportunities. 

One of the great things about data is that it is filterable and reusable. A simple example: the raw data used to measure KPIs by the marketing department can also be used to measure the financial performance of a certain product. This sounds like common sense, but I’ve seen too many companies forgetting about things as simple as this. 

If you have no clue about who is using which data and how it’s being used within your company, then you’re in dire need of a data strategy. Having a data strategy is a must if you want to start treating data like what it really is: an asset. And, just like any other asset, it’s better to continually protect it and manage it. 


The Bottom Line

If you’re going to take anything away from this article, I hope that’s the importance of having a proper data strategy in place. The way businesses manage and share data across an organization has changed, and every business leader must understand how their data can be used for the company’s greatest benefit. Beyond that, the issues mentioned above will help you be mindful of the scalability and repeatability of your strategy.

Elizabeth Moss

By Elizabeth Moss

Business Development Executive Elizabeth Moss helps support and grow BairesDev by forming strategic partnerships, increasing profitability, and aiding in customer acquisition. Her previous work experience includes success at tech giants such as Adobe and Jalasoft.

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