Data makes the world go around, there’s no doubt about it – and with increasing innovation and automation shaping our day-to-day, it’s possible to collect and store more information than before, making access to data easier than ever.
However, ensuring enterprise data quality proves to be a consequential hurdle for many companies, with a lengthy list of reasons causing businesses to struggle with the soundness of their data. For example:
- Some companies encounter data inconsistencies across different departments.
- Others find that the raw data that flows into their data supply chain, both internally and externally, poses the very real threat to create data quality issues along with a lack of standardisation.
Did you know that poor-quality data doesn’t just cost businesses revenue, but has an impact on customers, too? In fact, one in five companies are reported to have lost a customer as a result of bad data and more than a quarter feel that accurate data is their greatest data challenge.
The cost that poor-quality data possesses is ever increasing, so it stands to reason that organisations everywhere want to improve their data quality processes. At the end of the day, analysis is flawed, data insights are unreliable, and data assets can quickly become liabilities if data quality isn’t up to standard.
Why is High-Quality Data So Important?
It’s simple, really. The second that data is obtained by a company, the quality of that data is at risk. This is because, as data flows through systems, processes and various environments, its integrity is consistently threatened. This leads to additional operational risks.
A business risks making inaccurate of misinformed decisions, sending out incorrect communications to customers (invoices, for example) or even regulatory noncompliance if there is a lack of high-quality data.
With that being said, it’s not hard to understand how poor data quality can cause a world of problems for a business, including bad conclusions, loss of revenue and unhappy customers. The dangers don’t just stop there, though. Let’s take a look at more specific examples.
There are tens of thousands of health insurance claims files each and every day. In order to ensure regulatory compliance, health insurance companies are required to identify missing, late and duplicate claims within their processes.
One company, in particular, was reported to be having trouble with this as their data quality tool was not able to manage the sheer volume of claims that were coming in each day and, what’s more, is that it was unable to handle the format in which they were coming in too.
On this occasion, the health insurer was able to resolve the problem by replacing the bad software with an all-inclusive data intelligence platform that had robust data quality capabilities.
The new system was able to quality check high volumes of incoming data including consistency, conformity and timelines to ensure the accuracy and completeness of the data. This allowed the company to keep a trusted eye on all claims across all systems, automate reconciliation processes and reduce fines to zero.
Satellite Radio Providers
Another great example is of a satellite radio provider who was experiencing challenges of poor-quality data as a result of subscriber demographics coming in from third parties. Subsequently, the company suffered low retention rates based on campaign integrity issues and found it difficult to optimise their marketing strategies.
Like the health insurance company, this radio provider chose to adopt an enterprise data intelligence system to establish robust quality controls on data, from both internal and external sources.
As a result, the company’s data integrity issues were a thing of the past and they were also able to:
- Significantly increase their customer retention rates,
- Optimise their marketing programs; and,
- Achieve additional revenues in excess of $3 million within the first quarter of implementation.
Once again, another example of data quality challenges is represented by another branch in the telecom industry.
A mobile provider needed help with IFRS15, which provides standards on accounting for revenue from customer contracts. This company was struggling to ensure compliance within their finance and accounting teams. The problem was affecting five separate lines of business with over 30 diverse source systems and applications and file structures, with around 5 billion records coming in every day.
The company implemented an integrated data intelligence system to perform high-volume data quality checks and the new platform also helped them to build a fully automated data integrity solution with its robust analytics capabilities.
All of these examples illustrate that the best strategy for improving and protecting data quality across an organization’s data supply chain is through an enterprise data intelligence platform with a comprehensive set of capabilities.
Data Governance, Data Quality, and Analytics Working Together
As you can see from these few examples, the best way to improve and protect data quality is to implement an enterprise data intelligence platform with a comprehensive and robust set of capabilities.
By having data governance, data quality and analytics capabilities working together, companies are in a position to gain more value from their data and ensure the quality of the data across systems and processes.
Having a data intelligence platform in place provides a solid data governance framework, helping to facilitate processes that are intended to ensure data quality throughout the data supply chain.
When data is first created, companies want and need data to be easily understood, accessed and trusted by stakeholders every step of the way. This allows for the data to be used to generate meaningful insights and drive business decisions.
In order to understand what data means and its quality level, businesses must first determine its attributes, lineage, metadata and quality.
- The data governance capabilities of a good data intelligence platform deliver these critical capabilities as well as providing integrated data quality features which help to monitor and improve data.
- Data quality capabilities in an integration solution enable high-volume data integrity check to verify the quality of data across an organisation and fosters trust in data users.
- With the addition of analytics capabilities and machine learning algorithms, a data intelligence platform can aid self-learning to continuously improve data integrity.
The platform should also facilitate a full understanding of a company’s data landscape. This understanding enables all users to trust the quality of all their data to ensure regulatory compliance and make confident business decisions.
We have put together a whitepaper on how to create a business case for data governance which you can download for FREE.