Enterprises today have mostly become data-driven. Businesses no longer make decisions based on hunches or anecdotal trends as they used to do earlier. Criticial business decisions are now backed by solid data and analytical information.
More and more organizations are leveraging the power of Machine Learning (ML) and Artificial Intelligence (AI) to get better-optimized results. At the same time, it needs to be ensured that there’s a conversation around the quality – completeness, consistency, validity, timeliness, and uniqueness – of the data used by these tools.
The insights companies expect from technologies like AI and ML are only as good as the data used to power them.
According to a survey, poor data quality leads to increased complexity of data ecosystems and poor decision-making in the long run. Statistics show that approximately USD12.9 million is lost every year because of poor data quality.
As data volume increases, so will the challenges that businesses face with validating their data. To overcome data quality challenges, companies need to know the context in which the data elements will be used and the best practices to guide the initiatives along.
Factors responsible for data quality issues are duplicate data, unstructured data, incomplete data, different data formats, or difficulty accessing the data.
In the following blog, we will discuss the some of the most common quality issues with data and how to overcome them.
Data quality issues
Multiple copies of the same records harm computing and storage but may also offer skewed or inaccurate insights when they remain untraceable. One critical issue could be a human mistake — someone accidentally entering data many times — or a faulty algorithm.
The proposed remedy to this problem is known as ‘data deduplication.’ It uses a combination of human intuition, data analysis, and algorithms to discover suspected duplicates depending on chance scores and common sense to determine where records appear to be similar.
Data security and compliance requirements come from industry and government standards such as Healthcare Information Portability and Accountability Act (HIPAA) and PCI DSS (Payment Card Industry Data Security Standard) and various sources, including organizational requirements. Disobeying these guidelines can result in significant fines and, perhaps, a loss of client loyalty. HIPAA and PCI regulations also offer a compelling argument for a strong data quality management system.
Consolidating privacy and security enforcement management as part of an integrated data governance program provides substantial advantage. This could include integrated data management and auditor-validated data quality control methods, providing business leaders and IT assurance that their organization meets important privacy obligations and safeguards against data leaks. Customers are encouraged to form deep and lasting ties with the business when they safeguard customer data integrity with a uniform data quality program.
Often, when data is not entered correctly in the system or when the files are corrupted, the remaining data has many missing variables. For example, if an office address does not include a pin code at all, the rest of the other details will not have much value as it will become difficult to determine the geographical dimension.
Here a data integration tool can help convert improper data into a structured format. Also, move data from various formats into one consistent form.
Today, many companies use just about 20% of their data while making business intelligence decisions, leaving the rest (80%) to sit in a metaphorical dumpster. When it comes to customer behavior, hidden data is the most beneficial.
Customers increasingly interact with businesses through various channels, including in-person, phone, and online. Data on when, how, and why customers interact with a firm might be invaluable but rarely used.
Using a tool like the Datumize Data Collector (DDC) to capture hidden data can provide many more insights into the hidden data acquired.
The last concern is that running big data analytics or communicating with customers based on incorrect data makes no sense. Data can quickly become inaccurate.
Unless the data is not gathered completely, it cannot be said as complete data. This limits users from making decisions based on complete and accurate data sets.
Other reasons for data inaccuracy can be feeding incorrect data in the system. For example, typing in the wrong information received from the customer or placing details in the wrong field.
These can be the most complicated data quality issue that could be found, mainly if the encoding is still appropriate. For example, entering an inaccurate but legitimate social security number can go unchecked by a database that only checks the information’s accuracy in isolation.
Hence, there is no permanent cure for human error, but implementing clear procedures that are consistently followed is a good practice. Also, using automation tools to reduce manual work while moving data between systems can even help reduce the risk of mistakes by tired or bored workers.
Data as fuel for enterprises
As food is essential to the human body for survival, data is essential for organizations. To use a common metaphor, data is the most valuable resource today, “Data is the new oil” to the world. At the same time, organizations can have the most complex infrastructure, but it’s useless if the food (or data) running through those pipelines isn’t digestible.
People wanting to consume this food must have easy access to it; they must know that it’s eatable and not rotten. They must also be aware when supply is low. Lastly, the suppliers/gatekeepers must understand who is accessing it.
Just as access to hygienic food helps communities in various ways, improved access to data, well-designed data quality frameworks, and deeper, accurate data quality culture can protect data-reliant programs and insights, adding spur innovation and efficiency within organizations around the world.
To learn more about data security and other related topics, visit our whitepapers here.