Data Quality Assurance Strategies for Effective Digital Transformation

Digital transformation is a crucial step for any business today. Companies must embrace new technologies — especially data-centric ones like artificial intelligence (AI) — to compete in the current market. However, these transitions are only effective with high-quality data.

Poor-quality data costs organizations $12.9 million a year, and these losses may grow as businesses rely more on AI. You can prevent such missteps by creating a formal data quality assurance program. Here are some steps to follow to implement one.

Set Relevant Data Quality Objectives

The first stage in any effective strategy is to outline your goals. You cannot measure your data quality if you have no set baseline or targets to compare it to.

Start by determining what types of information you need to support your long-term business objectives. Don’t overlook the potential of unstructured data, as organizations that value these insights are 24% more likely to exceed goals than those that don’t.

Once you’ve identified relevant data types, you can decide how best to track quality metrics like accuracy, timeliness, and consistency. Make sure you use specific, measurable factors and benchmark your current performance to inform your future goals.

Create a Formal Data Cleansing Process

After setting relevant standards, you must create a workflow that transforms raw information into insights that meet these benchmarks. Data is rarely in an optimal state when you collect it. On the flip side, this means incomplete records are not unusable. Rather, you must process data before analyzing it.

Cleansing workflows should compare datasets to your predetermined standards to identify any gaps or errors. From there, you can fix them as necessary, whether that’s correcting typos, finding missing information, or removing outdated records. You can use synthetic data to enrich some insufficient datasets, as AI trained on it can be more accurate than those using real-world data.

Automate Standardization and Validation

When establishing data cleansing protocols, you should automate the process as much as possible. These steps take time and introduce room for error, but automation can quell those concerns.

Storing data in a centralized cloud database is a good first step, as the cloud exponentially increases productivity by streamlining access. Once everything is in one place, you can use basic AI models to automate standardization and validation.

Clustering algorithms and random forests both do a good job of detecting outliers and verifying records’ formats and completeness. However, it’s best to have a human expert handle processes like enrichment or other nuanced work where automation may introduce more errors.

Secure Data Access Policies

While security may seem like a separate issue from quality, it’s a crucial part of any data strategy. You cannot trust any information which you do not secure.

Unsecured AI training databases can open them to data poisoning attacks. Outside of malicious activity, open access policies mean more people’s errors can affect your dataset’s quality. Considering how common human error is, such a risk is too significant to ignore.

Only professionals who must access datasets for their jobs should have permission to see and edit them. Remember that these stricter policies are most effective when you also use secure identification methods like multi-factor authentication (MFA).

Get Specific About Data Governance

Data quality assurance strategies must also cover governance. Determining who is responsible for what is critical to both regulatory compliance and enforcing quality standards.

Proper governance starts with assigning specific roles. Appoint a committee to set quality and regulatory standards, a group to manage and enforce principles for each dataset, and a separate individual to oversee it all. All parties must work together to form specific processes and enforcement policies.

Specific governance requirements will vary depending on the regulations you are subject to. At least 40 states have data privacy laws that may introduce unique concerns, so review these before developing a governance strategy.

Regularly Review Data Quality Strategies

Finally, recognize that your data quality protocols and goals may have to adapt over time. Regulations change, industry standards rise, and technological advancement introduces new possibilities. Consequently, what’s acceptable today may not be tomorrow.

At least once a year, assess your data quality metrics, comparing them to your goals. You may need to adjust your approach if you’ve fallen short of expectations. Alternatively, you could see that conditions have changed, requiring you to adapt the goals themselves. In either case, regular review is necessary to ensure ongoing success.

Digital Transformation Needs Data Quality Assurance

Digital transformation depends on data. As such, it depends on processes to ensure your information is accurate, relevant, and reliable.

These six steps will help you promote higher data quality standards in your organization. Once you do that, you pave the way for a higher return on investment with your digital transformation initiatives.

Originally posted on OpenDataScience.com

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