by Maulik Shay –
The key to your business gaining a sharper competitive edge, making more informed decisions, and ensuring customers have a more engaging experience was right under your employees’ noses all along.
So what is it?
It’s the ability to more effectively utilize the data your organization already has. That’s where data profiling comes into play.
Data profiling is the process of analyzing, filtering, and assessing the quality and characteristics of data within an organization, and then creating informative summaries about that data so it is more accurate and complete1. Profiling also involves examining data elements, identifying patterns, and understanding data relationships to gain insights and ensure data integrity.
By performing data profiling, businesses can make informed decisions, improve data-driven processes, and mitigate risks associated with inaccurate or incomplete data.
Here’s how:
Data Profiling Approaches: Improving Data Quality for Better Insights
While there are many different ways that organizations can approach data profiling, they all share the same goal: Consistently improving data quality to allow a better understanding of it. Some of the more common ways include2:
Structured Discovery
Structured discovery is a data analysis approach that ensures data consistency and formatting by employing techniques such as pattern matching, thereby validating the integrity of the information. Additionally, it leverages basic statistical analysis to gain valuable insights into the validity of the data.
Content Discovery
Content discovery is a methodology that concentrates on examining individual elements within a database to assess the quality of the data, ensuring accuracy and reliability. It achieves this by identifying null values, incorrect or ambiguous data, and standardizing inconsistent entries—thus enhancing the overall data quality and integrity.
Relationship Discovery
Relationship discovery involves analyzing metadata to uncover and comprehend the connections and relationships between different data sets. By doing so, it aids in aligning data and identifying overlaps, thereby mitigating potential issues within data warehouses or other datasets and enhancing data management and integration.
Unlocking the Value of Data Profiling for Your Business and Customers
Data profiling is a powerful way to maximize the value of an organization’s data, delivering a number of key benefits:
1. Improved Data Quality
Data profiling can improve data quality by identifying and resolving data inconsistencies. For instance, it can detect where some data sets use a two-digit state code while others use the fully spelled version.
2. Enhanced Data Understanding
Data profiling can also help to reveal valuable insights into the structure and relationships within the data, uncovering connections that span different databases, source applications, or tables.
3. Compliance and Risk Mitigation
Leveraging data profiling can also be a key tool for ensuring compliance and mitigating risks associated with data privacy and security. By detecting sensitive or non-compliant data elements, it helps businesses identify potential compliance gaps and rectify them proactively.
4. Improved Confidence in Data
Finally, data profiling contributes to improved business efficiency by instilling confidence in the accuracy and reliability of the data. With a solid data profiling process in place, organizations can make data-driven decisions confidently because decision-makers can rely on the integrity and quality of the data.
Data Profiling in Practice: Three Use Cases
Healthcare: Ensuring Patient Data Accuracy
By validating patient demographics and medical records, healthcare providers can ensure that the information they have is up-to-date and reliable, ensuring accuracy and consistency in diagnoses, treatments, and care plans. By detecting any data discrepancies or inaccuracies—such as duplicate or incomplete records—healthcare organizations can increase the ease of coordination across systems and enable more efficient healthcare delivery.
Finance: Fraud Detection and Risk Mitigation
The ability to perform real-time analysis of financial transaction data across multiple systems helps financial organizations identify fraud faster. By profiling customer transactions and identifying normal patterns, financial institutions can proactively identify suspicious activities and potential compliance breaches, ensuring regulatory adherence.
Insurance: Claims Processing and Risk Assessment
Profiling insurance claims that using historical claim data as a guide allows for more accurate and efficient processing by identifying patterns and anomalies. This approach enables insurers to streamline their claim processes, offer more tailored insurance packages, and evaluate risk factors to prevent fraudulent claims.
Bringing It All Together
Whether your organization is just beginning to explore the power of data profiling or your team wants to refine your existing program, RCG has a proven track record in successfully delivering digital solutions that drive real impact.
With their extensive experience, RCG understands the complexities of data profiling and how it can be leveraged to deliver tangible results. Their expertise in bridging the gap between innovation and practical implementation enables them to design and execute strategies that maximize the potential of data profiling, leading to real-world impact for organizations—no matter their industry or scale of operations.
Want to learn more about how organizations like yours can better leverage their data to wow their customers? Then make sure to check out our eBook, Customer xDNA.
Works Cited
1. Geeks for Geeks "Understanding Data Profiling" Retrieved from https://www.geeksforgeeks.org/understanding-data-profiling/
2. SAS "What is data profiling and how does it make big data easier?" Retrieved from https://www.sas.com/en_us/insights/articles/data-management/what-is-data-profiling-and-how-does-it-make-big-data-easier.html#/
