Data Quality isn’t all Rainbows: Here are 4 Challenges to Be Wary Of

Today, companies of all sizes increasingly deal with incredibly complex IT systems that are established across several systems and geographies.

In addition to utilizing core ERP, the inclusion of domain-specific and specialized software for managing pricing, CRM, demand planning and several other critical functions within businesses has inevitably made data management much more challenging. 

_^Source

High-quality data is essentially a prerequisite in utilizing and analyzing big data along with guaranteeing the value it holds. However, comprehensive analysis, quality standard research and quality assessment methods are currently found to be lacking concerning big data.

_^Source

This punches a huge hole for large corporations in the way it functions as data has essentially become a pivotal member in most, if not all business transactions.

Before we head deeper into which challenges big corporations currently face concerning data quality, let's discuss the challenges data quality assessment currently poses.

1. Measuring data accuracy

Data accuracy indicates the data present in your database correlates to reality. Finding reliable references can be difficult to find, but it’s not completely impossible.

For instance, businesses can employ machine learning regarding product or customer names. However, this doesn’t entirely resolve the issue so finding a great balance between required efforts and the value anticipated out of it can still be extremely challenging. 

2. Measuring data consistency

Data consistency typically means your data is without any contradictions. But this particular issue isn’t quite as simple as that. For instance, each time a customer chooses to share their personal information while shopping online, they could either be a registered user or a guest.

This means the retailer may either identify the name or not. Customers could also skip providing addresses if they’re not interested in delivery. In cases like this, retailers could end up with databases that carry contradictory information.

This in turn begs the question: Through your perspective, how many individuals (consumers/customers) are hiding behind their given records?

4 of the top data quality challenges as faced by corporations

Given the influence data holds, data quality issues stem from dozens of origins, so for now we’ll be discussing 4 of the most common and biggest challenge that currently plagues numerous corporations and businesses.

1. Duplicate data

Having multiple records can heavily affect computing and storing; however, that’s not the end of it. When these are left undetected, it ends up producing altered or incorrect insights. In such situations, the most probable critical problem is none other than human error.

It could typically result from an individual accidentally entering data several times or perhaps even an algorithm error.

And the solution? This issue is usually tackled through data deduplication. What this does is combine various factors such as data analysis, human intuition and algorithms for detecting possible duplicates by basing it on common sense and chance scores. 

2. Unstructured data

Most of the time, unstructured data rolls into the picture whenever data is entered incorrectly within the system or when certain files get corrupted, thus the remaining data ends up containing missing variables like an address missing its zip code.

Reports from the International Data Group reveal how unstructured data annually grows at an astounding rate of 62%. For most businesses, that’s quite the number to keep up with (if they can).

This basically means they’re collecting information that they may remain unaware of. This in turn promises challenges for utilizing and securing data. However, what makes this situation even gloomier is the lack of alert or the inability to properly handle the issue.

3. Security-related problems

In addition to regulatory standards like PCI DSS or HIPAA, data security along with compliance requirements arrives from various sources and normally includes organizational requirements. The guidelines provided by these also remark a compelling argument towards a robust system for data quality management.

To further complicate things, failure of compliance can summon hefty fines. But that’s not all. It can lead to losing much more expensive components such as customer loyalty.

Hence, consolidating security enforcement and privacy management as an effort for an overall data program provides significant advantages for businesses. This may include procedures such as auditor-validated data quality control and integrated data management.

4. Inaccurate data

Considering how one in five companies/businesses admit to losing a customer because of inaccurate or incomplete data, this no doubt serves as one of the most common and biggest challenges for businesses in managing data quality.

^_Source

Furthermore, this issue has been identified as a huge challenge across various business sizes with 25% of businesses containing over 500 workers, 32% of businesses containing between 250 & 500 workers, and 16% of small businesses containing around 10 employees reportedly losing customers because of poor data quality.

Now there’s really no point in performing big data analytics, and/or communicating with audiences based on data that’s simply wrong. This is because it doesn’t take long for data to become inaccurate.

Hence, when your data is incomplete, it limits businesses from making decisions that are based on accurate datasets.

Final thoughts

These four data quality challenges that are difficult to avoid let alone eradicate. In fact, the most sensible way of dealing with any data quality-related problems is first to recognize them as inevitable, yet manageable.

Of course, a great portion of data quality challenges does fall on how teams operate data management processes but that doesn’t entirely make the process flawed. These are no ordinary issues and sometimes even the best of data operation may not be able to avoid them.

However, there are highly effective solutions such as data integration or data quality tools that can immensely aid businesses in minimizing data errors and help overcome these challenges in the process.