Data quality remains a formidable challenge for organizations of all sizes and industries. 

But while clean data is critical to deriving value, it’s only one component of what businesses actually want and need—reliable data. And data quality is not enough in today’s real-time, connected world, which is being pushed forward by the AI boom. 

Data reliability ensures data is consistently accurate, usable, and trustworthy for various applications, by technical and non-technical users to make informed business decisions. What’s more, reliable data helps organizations avoid costly mistakes, project delays, and strategic missteps.

Data reliability comprises these core areas:

• Data unification combines multiple data sources into a cohesive dataset to get a single view of the data.
• Data quality is the process of cleaning data for accuracy, completeness, consistency, and relevance to enable trust in the data.
• Data health is data monitoring and assessment to ensure ongoing reliability for business processes and confident decision-making.

As data ecosystems expand in complexity and the stakes of quality data rise, we found that data quality is only one part of having a healthy data ecosystem. It’s no longer just about having clean data. 

Now, and especially with the rise of AI applications and models, businesses need data they can depend on data that upholds its integrity under the pressure of real-time decision-making and complex analytics moving through multiple programs, data that everyone at your organization can trust and use for confident decision making. 

In this blog, we will review the importance of the evolution from data quality to data reliability and how making data reliability a priority will help your business.

Understanding Data Reliability

Data quality is the more familiar term in the business world. It’s been around for decades and is typically associated with accuracy, completeness and consistency. 

Don’t get us wrong—data quality is important! It ensures the data you use is correct, whole, and uniform. However, data quality is a single part of a multifaceted picture required within our data-dependent, interconnected business world.

However, data reliability allows business and technology leaders to operate competitively.

Reliability means your data remains dependable no matter how much you scale your operations or how extensively you use it. Your data is quality data and consistently supports your business decisions accurately and promptly, even when it moves through different systems. 

Traditionally, businesses have concentrated on cleaning and organizing their data—making sure it's error-free and formatted correctly, but they often don't address reliability. Instead, many businesses would deal with data issues as they arose within the departments that data affected. This piecemeal approach frequently results in data silos, where information becomes isolated and inconsistent across different departments.

With data reliability strategies, we suggest a more proactive approach. A strategy with data reliability creates quality data and builds systems that maintain its integrity so businesses can rely on dynamic data to operate in fast-moving markets.

Why Data Reliability Matters to You and Your Business 

Today, businesses are dealing with an explosion of data from a variety of sources—social media, IoT devices, customer interactions, and more. 

This vast amount of data can be overwhelming without a robust management approach. The more data you have, the harder it is to ensure it's highly usable and available when needed. Not to mention, data regulations are becoming stricter around the globe. 

Laws like GDPR in Europe and CCPA in California require businesses to maintain high data accuracy and security standards. Non-compliance can result in hefty fines and legal troubles. 

If your data is unreliable, proving compliance is difficult, which can put your business at risk. Ensuring data reliability means aligning your data practices with legal standards, which will help you avoid penalties and maintain trust with regulators and customers.

This is essential today because customers and partners want to know how their data is used. They expect transparency in how businesses handle their personal information. If a business fails to meet these expectations, it can lose customer trust and damage the brand’s reputation.

With growing data volumes, stringent regulations, and higher consumer expectations, businesses should prioritize making their data trustworthy and dependable. This focus will safeguard your company from risk and position it to leverage data as a powerful asset. 

Why Focus on Data Reliability Now?

The ability to use Artificial Intelligence (AI) is one of the biggest reasons data reliability should be prioritized. AI can transform businesses by providing deep insights and automating complex processes. However, AI’s effectiveness hinges on the reliability of the data it uses. 

Unreliable data can lead to flawed AI models, resulting in inaccurate predictions, AI hallucinations, and poor decision-making. And predictive analytics is one of the most powerful applications of AI! Without them, you can’t forecast business trends and behaviors. 

For instance, retail companies use predictive analytics to anticipate customer buying patterns and manage inventory levels effectively. The accuracy of these forecasts depends on the reliability of the data used. If the data is flawed or inconsistent, the inventory predictions will be wrong, leading to stockouts or overstocking.

Focusing on data reliability now, no matter your industry, will future-proof your business. As AI continues to evolve and become more integral to business strategy, ensuring your data is reliable will set the stage for AI and other advanced technologies to perform at their best.

How DataGence Can Help 

At DataGence, we have helped many companies increase their data reliability by using our proven data reliability solutions and data quality engine. 

The integrated data quality engine:

• unifies your data from all sources to a centralized platform to eliminate silos and ready orchestration and synchronization.
• validates every record and verifies against our global identity graph for accuracy and authenticity.
• enriches validated records from multiple sources with missing or additional fields while adhering to pre-set rules.
• standardizes data against ISO 8000 specifications or any custom standard set by organizations.
• purifies the data by matching contact and company with hierarchies established to remove duplicates
• authorizes user consent and maintains compliance with global regulatory or organizational standards
• segments the data to support downstream systems for various applications

This process acts as a series of checkpoints you can use to ensure data reliability, confident decision making and improved business outcomes.

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At DataGence, we understand the critical need for reliable data. If you need help unlocking actionable intelligence, let’s chat!