Fueling Data-Driven Decision-Making with Data Validation and Enrichment Processes

77% of data and analytics professionals say data-driven decision-making is the top goal for their data programs.

Data-driven decision-making and initiatives are certainly in demand, but their success hinges on … well, the data that supports them. More specifically, the quality and integrity of that data.

It seems obvious enough, but checking that your data is up to the task and taking any necessary steps to improve and maintain its quality can be easier said than done. An important part of this journey is the data validation and enrichment process.

Defining Data Validation and Enrichment Processes

Before we explore the benefits of data validation and enrichment and how these processes support the data you need for powerful decision-making, let’s define each term.

Data validation is the process of determining whether a particular piece of information falls within the acceptable range of values for a given field.

Think of address data, for example. In the United States, every street address should include a distinct field for the state, populated by values like “NH”, “ND”, and “AK” to conform to the list of state abbreviations as defined by the U.S. Postal Service. Inputting one incorrect character, like “NG” instead of “NH” for New Hampshire, could essentially invalidate the entire address.

Data validation performs a check against existing values in a database to ensure that they fall within valid parameters.

Data enrichment  is the process of enhancing your data by appending relevant context from additional sources – improving its overall value, accuracy, and usability. Enriched data is valuable for any organization because it provides greater context and deeper insights that fuel confident decision-making.

Data enrichment empowers your data to complete a bigger picture and answer more complex questions. If we look at address data as an example again, you can enrich the address to understand if the address is in high elevation and safe from flooding or other natural risks. What are the businesses near it? What are the demographics in the area? What marketing is effective in this area? What times of the day are busy in the area, and are roads accessible? Data enrichment helps provide a 360^o view which informs better decisions around insuring, purchasing, financing, customer targeting, and more.

Together, data validation and enrichment form a powerful combination that delivers even bigger results for your business.

Turning Raw Data into Meaningful Insights

Even though organizations value data-driven decision-making more than ever before, data quality remains a major barrier across industries. The 2023 Data Integrity Trends and Insights Report, published in partnership between Precisely and Drexel University’s LeBow College of Business, found that of the 450 data and analytics professionals surveyed:

• 70% who struggle to trust their data say data quality is their biggest issue.
• 66% of respondents rate the quality of organizational data as “average,” “low,” or “very low
• 53% rank data quality as their top priority for improving data integrity
• 41% say the biggest challenge keeping their organization from effectively using location data for decision-making is that address data needs to be standardized, verified, and fit for purpose

Data that’s incomplete, inaccurate, and/or inconsistent not only provides no business value – it puts you at greater risk.

When internal data systems aren’t connected, they’re not collecting all the information needed in the manner required for analytics. That means that maintenance and enhancement processes are complicated, complex, error-prone, and very lengthy. If you can’t react fast and deliver the services and features that your customers expect, your overall growth and critical business goals are put at risk.

So how does the data validation process help on the journey to better data quality and ultimately, data integrity? Let’s explore.

• Accuracy: data validation can recognize inaccuracies within imported data by comparing it against predefined rules. Is there missing information? Inconsistent formats? Incorrect values?
• Completeness: to avoid skewed analysis, bad decision-making, and unreliable data-driven applications, data validation checks that all required fields are populated with the information needed.
• Consistency: are data elements consistent across systems? Perhaps a mailing address was updated in the marketing system, but not in the sales system. Cohesion helps ensure reliable reporting, analysis, and application functionality, and validation is key. 
• Error prevention: all of these data validation checks above contribute to a more proactive approach that minimizes the chance of downstream errors, and in turn, the effort required for data cleansing and correction later.

Transforming raw data into actionable insights is challenging for many organizations across industries, but the need to turn that issue around has never been more vital. You accomplish that with data validation, paired with data enrichment processes. We’ll talk about enrichment next.

The Benefits of the Data Enrichment Process

Think of all the data you regularly generate about your customers and operations. What could you accomplish with even deeper insights from internal and third-party data?

For many businesses, these insights lead to results like:

• informed decision-making around site selection for stores, restaurants, and infrastructure
• optimized business processes
• inspired product innovation
• personalized omnichannel marketing messages
• more accurate risk evaluation
• stronger compliance

Do any of those resonate with your own goals? If so, data enrichment is a necessity.

Every company is unique and use cases will vary, but regardless of your objectives, the uses of data enrichment are virtually unlimited. When it comes to third-party data, you just need to find the best quality data and sources that deliver the results you need – whether you’re using that information for business intelligence dashboards, problem-solving, analytics, or AI/ML applications.

Data validation and high-quality data enrichment processes both aim to make trusted data accessible enterprise-wide. But this process takes countless hours of time and effort.

What if you could easily select, incorporate, enrich, and interact with internal and external data? What perspectives and opportunities could you uncover?

Streamline the Process with Precisely

Let’s talk about address data. Addresses can act as a linkage point for connecting datasets, but they’re often complex and don’t provide a complete view of the location.

A unique and persistent identifier, like our PreciselyID, can enable data stewards to apend thousands of data points to specific geolocations based on latitude and longitude – data like parcels, building footprints, attributes, demographics, and socio-economic data.

Then, you can analyze and enrich the data for greater understanding and even more powerful insights. With 400 datasets containing more than 9,000 attributes, our data enrichment catalog is ready to help.

Ultimately, we want you to spend less time sourcing, preparing, quality checking, and updating information, and more time making impactful decisions that move your business forward – and we do that by meeting your data validation and enrichment needs wherever your data lives, while you continue to leverage your current investments.

Want more on how to streamline data validation and enrichment for better results? Read our eBook, Validation and Enrichment: Harnessing Insights from Raw Data.