In order to stand out from your competitors, innovate, and offer personalized products and services, collecting data is essential. However, managing data isn’t a walk in the park: every day, small problems can affect their quality. Incomplete or inaccurate data, security problems, hidden data, duplicates, inconsistencies, or inaccuracies, and the list goes on.
Here is an overview of the most common data quality related issues and some best practices to use to curb them for good!
The risks associated with poor data quality
As it’s been said over and over again, when it comes to data, the real issue is not the quantity of data but its quality. Data Quality Management (DQM) is a demanding discipline that relies on the endless questioning of data processes and constant surveillance of the very nature of the information that constitutes your data assets. Poor data quality can directly translate into lower revenues and higher operational costs, potentially resulting in financial losses for your company.
When data quality is degraded, analyses, projections, forecasts, and even decisions can be distorted. And the greater the volume of degraded data is, the greater the gap between reality and your understanding of reality is. Ensuring data quality starts with a good understanding of the errors that can affect it.
The most common data quality issues
Ensuring data quality is a key topic for any company that bases its development strategy on data. To carry out targeted actions, you need to prioritize tasks and not spread yourself too thin. Data Quality Management consists in identifying all the erroneous information that could distort your decision-making. This erroneous data can be classified into four categories.
When data is duplicated, it means that the same information is present multiple times in the same database or file. Data duplication is hence one of the most harmful issues because it is often difficult to detect. Beyond 5% of duplicated data, it is considered that the quality of the data starts to be degraded. For example, CRM tools often generate duplicate data, because their users sometimes add contacts without checking their presence in the database.
On a daily basis, your business generates an increasing amount of data. Very often, you only leverage a limited portion of the available information. The rest of the data produced by your business gets scattered and diluted in data silos. It then remains permanently untapped. For example, a customer’s purchase history is not always available to customer service teams. Yet, this information would allow them to better identify the customer’s profile and therefore, provide more relevant answers to their specific requests, or even upsell or cross-sell by making adapted suggestions.
Are John Smith and Jon Smith really two different customers? Inconsistent data significantly affects data quality. It can also be created by another well-known phenomenon: redundancy. This phenomenon occurs when you work with multiple sources (including third-party data) in addition to your own data. Discrepancies in data formats, units, or even spelling must be tracked in a data quality approach.
It may seem obvious, but inaccurate data is probably one of the worst issues that can undermine data quality. When customer data is inaccurate, any personalized experience will not be relevant. For example, if your data inventory is inaccurate, supply difficulties or storage costs can skyrocket. Whether it’s incorrect contact information or missing or empty fields, you need to do everything you can to eradicate inaccurate data.
How to solve data quality problems
While common sense often presides over good data quality management, they are not enough to ensure it.
To meet these challenges and solve your data quality issues, you’ll need a Data Quality Management tool. But in order to choose the right solution, you will need to start by mapping your data assets in order to identify and evaluate their actual quality. Deploying a Data Quality Management solution, data governance, training, and raising awareness of your teams to good data management… are all essential pillars to limit data quality-related issues!
To learn more about DQM, feel free to download our Guide to Data Quality Management