What does it mean to be a data-driven organization? When asked this question in a survey, the majority of respondents answered: “High levels of trust in the quality of data.”
And it’s no wonder. Without trustworthy data, any business decision you make will lack a solid foundation. This can negatively impact your reputation, customer experience, and bottom line.
But, as the saying goes, trust is something that’s earned and not given. The same should apply here. You shouldn’t blindly trust your data, especially if you don’t follow stringent data quality practices. As these practices are very demanding and reliant on strict rule-following, we’re willing to bet many organizations don’t follow them to the letter.
The question is: how can your business turn this problem around?
In this article, we highlight six data quality problems that may impact your organization before explaining how you can build trust.
Often, data quality issues don’t present as technical problems — they manifest as business pain points.
For instance, consider the following common challenges:
The key is to view these business-oriented challenges from a technical perspective. On many occasions, your frustrations are not with business processes but with the data that underpins them. If you can’t trust your data, you definitely can’t trust your subsequent business decisions.
With that in mind, here are six technical issues that can impact the quality of your data.
If your data contains missing required values or fields, it’s incomplete. While you may think partial data is better than no data at all, using it can negatively impact your analytics and decision-making.
For instance, imagine your marketing lead datasets contain incomplete information about contact job roles. This could impact the angle of your marketing campaigns going forward. For instance, you may target your content to a C-suite audience, when in actual fact the majority of your leads are managers.
Data stored in different systems, often owned by different business functions, may use different formatting rules. For instance, one department might format the date fields as MM/YYYY and another might opt for DD/MM/YY. The problem is only exacerbated when you consider these dates and times may be from different time zones.
In addition to this, your teams may define terms differently. One department may decide that “customers” should include all previous customers. Another may define it as only including current customers.
Remedying these formatting and definition differences can cause delays when processing your data, particularly when you do so manually.
Even if your data is complete on the surface, you still shouldn’t blindly trust it. Beyond formatting issues, timeliness problems can also impact the quality of your datasets.
If it takes you weeks or more to access data, it’ll be out of date by the time you use it. To make the most accurate decisions, you need access to real-time insights.
If your data sets contain old data that’s either irrelevant or predates your current standardization and validation processes, this can reduce the overall quality of your data. Once again, this will make data processing a time-consuming and burdensome task.
Reliance on manual data processes will increase the risk of human errors, such as typos or missed fields. Even the simplest of errors can substantially impact the reliability of your data and the decisions you make as a result.
Building automated pipelines to handle bad data
Your business departments may manage their own datasets on their own platforms. Without oversight from technical teams, this data may be low-quality and inconsistent. Not to mention, it could also be hidden from the rest of your organization.
To ensure consistency and trustworthiness, your IT team should control your data pipelines from design to publishing. Otherwise, it becomes impossible to know whether your data is being stored correctly and properly validated.
More than this, if something goes wrong it may escape the notice of your business teams. For instance, if a data pipeline breaks and impacts the quality of your data, this data may filter into reports before the issue is identified.
To ensure everyone follows best practices, it’s essential to promote a data-driven (and quality-driven) culture. Aim to break down siloed information, communicate the importance of IT oversight, and limit any rogue processing or standardization.
This can all be helped with a data integration platform that provides a centralized access point to validated, reliable business data. You should adopt a platform that offers the following:
Through a mixture of governance, intelligent automation, and collaborative features, the right data integration tool will help you regain trust in your data again. The result? You can make better business decisions with added confidence.
Keen to find out how CloverDX ticks all of these boxes? Book a demo today to see for yourself.