As any successful company knows, data is the lifeblood of the business. But there’s a stipulation. The data must be complete, accurate, current, trusted, and easily accessible to everyone who needs it. That means the data must be integrated, managed, and governed by a user-friendly platform. Sound easy? Not necessarily.

One problem that organizations continue to face is poor data quality, which can negatively impact business processes ranging from analytics to automation to compliance. According to Gartner, every year, poor data quality costs organizations an average of $12.9 million. Gartner notes that poor data quality also increases the complexity of data ecosystems and leads to poor decision-making.

The right approach to enterprise data management helps ensure data quality. Likewise, recognizing and addressing the factors that reduce data quality mitigates problems while enabling benefits across data-driven processes.

Organizations experiencing any of these five issues have poor data quality. Here’s how to identify and fix the problems:

1. Data is siloed for a specific user group

When individual employees or departments make copies of data for their own use or collect data that’s only available to a small user group—and is isolated from the rest of the company—data silos occur. The data is often incomplete or focused on a single department, like marketing. This common problem restricts data sharing and collaboration, offers limited insights based on partial data rather than holistic views into the business, increases costs by maintaining multiple versions of the same data, and several other problems. The solution is to break down silos for a single version of the truth and make integrated data available to all users.

2. A single customer has multiple records

Data duplication is when more than one record exists for a single customer. Duplicated data can end up in different formats, get stored in various systems, and lead to inaccurate reporting. This problem occurs when data about the same customer or entity is stored multiple times, or when existing customers provide different versions of their information, such as Bob and Robert for a name or a new address. In these cases, additional records are created instead of a single record being updated. This can negatively impact customer experiences by bombarding individuals with the same offers multiple times, or marketing being unable to create a full 360-degree profile for targeted offers. Performing data cleansing with the right tools and integrating records can remove duplicate data and potentially create more robust customer profiles.

3. Lacking a current, comprehensive data management strategy

Organizations need a strategy that manages how data is collected, organized, stored, and governed for business use. The strategy establishes the right level of data quality for specific use cases, such as executive-level decision-making, and if executed correctly, prevents data silos and other data quality problems. The right strategy can help with everything from data governance to data security to data quality. Strategically managing and governing data becomes increasingly important as data volumes grow, new sources are added, and more users and processes rely on the data.

4. Data is incomplete

For data to be optimized and trusted, it must be complete. Missing information adds a barrier to generating accurate insights and creating comprehensive business or customer views. By contrast, complete data has all the information the business needs for analytics or other uses, without gaps or missing details that can lead to errors, inaccurate conclusions, and other problems. Organizations can take steps to make sure data is complete by determining which information or fields are needed to reach objectives, then making those fields mandatory when customers fill out information, using data profiling techniques to help with data quality assurance, and integrating data sets.

5. Shadow IT introduces ungoverned data

The practice of using one-off IT systems, devices, apps, or other resources rather than leveraging centralized IT department processes and systems can compromise data quality. That’s because the data may not be governed, cleansed, or secured. These IT workarounds can spread into or across the cloud, leading to data silos, with little to no oversight and resulting in data that does not follow the organization’s compliance requirements. Offering staff easy and instant access to quality data on a single platform that meets their needs discourages the practice of Shadow IT.

Ensuring Data Quality Drives Enterprise-Wide Benefits

Having enterprise data management systems in place to ensure data quality can be a competitive advantage, helping with everything from better data analytics to accelerated innovation. Users throughout the organization also have more confidence in their results when they trust the data quality—and are more likely to follow established protocols for using it.

Achieving and maintaining data quality requires the right technology. Legacy platforms that can’t scale to meet growing data volumes will not support data quality strategies. Likewise, platforms that require ongoing IT intervention to ingest, integrate, and access data are deterrents to data quality because they encourage silos or IT workarounds.

Data quality issues are not limited to on-premises environments. Organizations may find that out the hard way when they migrate their data warehouses to the cloud—any data quality issues on-premises also migrate to the cloud.

One way to avoid data quality issues is to use a modern platform. For example, the Avalanche Cloud Data Platform simplifies how people connect, manage, and analyze their data. The easy-to-use platform provides a unified experience for ingesting, transforming, analyzing, and storing data while enabling best practices for data quality.

Related resources you may find useful:

Introducing Data Quality and DataConnect v12

What is Data Quality Management?

What is Data Management Maturity?

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