How to Improve Data Quality with Data Enrichment
Some people already know everything they need to know about their customers – for everyone else, there’s data enrichment.
In today’s digital economy, knowing how to deliver a positive customer experience is a significant competitive advantage. Customers expect every interaction with every company to be contextual, relevant, and effortless – every time.
Organizations that put the customer at the center of everything value data as an essential asset. Every relationship built with a prospect or acquired customer provides new opportunities for business growth.
While the benefits are great, most organizations are unable to gain this hard-to-find 360 degree view of a potential customer or business partner. Without detailed information about a potential business prospect, relationships suffer, and opportunities are missed.
As raw data is captured about customers and prospects from multiple channels into multiple systems, the data has limited value outside the context of the system it is captured in. Even then, if there are missing values, inaccurate data or data is in a non-standardized format, its value is diminished further. Typically, this raw data is cleansed and standardized before data enrichment – see the section Data Quality Methodology for Data Enrichment.
What is data enrichment?
Data enrichment is the process of integrating external and internal information with existing data to increase data accuracy and value. Combining trusted and authoritative information with existing data enables better business decisions and customer experiences. For example, by combining geocodes and address data with other business data, insurance companies can better assess if a prospect lives in a high-risk area or a marketing department could analyze where customers or prospects live to determine the most effective advertising strategy.
Benefits of data enrichment
Data enrichment has many benefits for an organization, from delivering a more relevant customer experience to converting more prospects to managing risk and compliance. Let’s consider this in a bit more detail.
Segmentation and personalization
By enriching customer profiles with trusted external third-party data, marketers augment their contact data records to get a 360 degree view of customers. For example, marketers can know exactly where their customers are located by appending geocoordinates to address locations. This allows marketers to map out customer locations differently and see where clusters of their most loyal customers exist. This provides marketers more capabilities than what’s available through zip code analysis.
In addition, demographic and firmographic data about the people and businesses near an address can be appended to the customer record. With data about the types of individuals in an area, marketers can get a sense of how to more precisely target and message to their audience – without worrying about personally identifiable information (PII) and related regulations.
Lead scoring and increased sales
With a 360 degree view of customers, marketers can also better utilize lead scoring for prioritization. Lead scoring engines perform better with as complete a picture as possible by combing information provided by the leads themselves with first-party data and data from trusted external sources.
In the past, I have seen leads that flow into sales systems being ignored and marked as junk or incomplete because there are some gaps in the data. The result is that salespeople begin losing confidence in the quality of the leads and marketing sees a lower ROI on their campaigns.
To address these challenges, sales and marketing should collaborate on defining the key data elements needed as well as the data enrichments that would provide a fuller picture of the customer. This leads to more engaging conversations and increased sales efficiency. Also, with a 360 degree view of the customer, sales and marketing can quickly identify upsell and cross-sell opportunities.
Use data enrichment to learn more about customers—their preferences, brand affinities, buying behaviors, visit patterns and what they are interested in. This makes for a more engaging and rewarding customer experience.
As we have seen over the past year, COVID-19 has accelerated the consumers’ transition to online purchases. In a survey by McKinsey, they estimate we have seen 10 years’ growth in ecommerce in just three months. When consumers go online to purchase an item, they want to know as much as possible before making a decision. Shoppers today are savvy and will quickly switch to another website to find the information they need and make a purchase. Therefore, the product data needs to be complete, consistent, and accurate.
Compliance and risk
While not as exciting as the previous benefits outlined above, data enrichment has a key role to play in complying with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) among others.
A data quality process that cleanses, standardizes, deduplicates and enriches data enables you to identify PII data and respond to requests to share and update personal information made by data subjects. Another example of how data enrichment helps with compliance is around “Do Not Call.” Contacting a person on a “Do Not Call” register can result in large fines and reputational damage.
Identifying and quantifying risk is crucial for insurance companies – and data enrichment is helping. For example, in addition to details provided by the policy holder for household insurance, insurance companies are augmenting the data with geospatial information around environmental risk. Similarly, sensors on cars are providing additional data enrichments like driving behavior and location.
Data quality methodology for data enrichment
Data enrichment is not a once of process – data decays over time while new data sources are added. For example, people move location, their marital status changes or sensor data becomes available. As these changes accelerate, a structured approach is needed to apply data quality and data enrichments on a continuous basis.
The Informatica Cloud Data Quality solution provides a powerful technology base to support data quality processes and data enrichment. Informatica Cloud Data Quality delivers the full range of data quality capabilities – including data profiling, cleansing, verification, enrichment, and monitoring – companies need to ensure that all data is complete, consistent, accurate, and current, regardless of where it resides. Our approach to data cleansing and enrichment has four distinct phases.
Four steps to improving your data quality
Step 1: Profile your data
The first step is to profile data to discover and understand anomalies in all data sources. You need to identify the data fields that are critical to operational success. Investigate each data attribute to uncover information. Define metrics to measure the quality of key data elements like completeness, conformity, and accuracy.
Step 2: Define Data Quality Rules and Enrichments
The next step is to define the cleansing and standardization rules that ensure data is fit for purpose. Informatica Cloud Data Quality provides easy-to-use drag-and-configure capabilities that let business users and data analysts rapidly build, test, and run data quality plans to analyze, cleanse, standardize, enrich and match data as well as monitor data quality on an ongoing basis.
Step 3: Apply the rules to your data quality processes
Next, integrate the defined rules into your data quality processes. You can run data quality rules as data is migrated to a new cloud data warehouse or SaaS application as a preventative data quality measure.
You can also run them on an ongoing basis to ensure data always remains trusted, timely and relevant.
As new data sources come online and need to be reconciled with existing data, you can reuse all profiling and rule specifications from business analysts and data stewards across all applications and projects.
The ability to be agile and reuse existing data assets, and rules are critical to staying on top of ongoing change, at scale
Step 4: Continuously monitor and report on data quality
As noted earlier, this is not a one-off process, you need to continuously monitor and report on data quality against all targets and across all business applications. Data quality reports should reflect status against quality dimensions defined for key data elements.
These reports should indicate whether data quality levels have increased or decreased and offer explanations about the change.
Reports should also link data quality metrics to your company’s key performance indicators to highlight the positive returns on improved data quality or the negative impact of low-quality data.
Get started with data enrichment now
With clean and enriched data, your organization can:
- Make better decisions by extracting more meaningful and trusted insights from your data
- Increase sales by improved targeting and engaging your prospective audience at each stage of the buying cycle
- Maintain data compliance and reduce risk by ensuring your data is accurate and complete
As noted in the Data Quality Methodology for Data Enrichment section, data profiling is the first step to cleansing and enriching data. So, why not sign up for a free 30-day trial of Informatica Cloud Data Quality to understand the quality of your data and what data can be enriched.
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