Data Profiling: Understanding your data

What is data Profiling ?

DAMA defines data profiling as: An approach to data quality analysis, using statistics to show patterns of usage, and patterns of contents in an automated manner.

Data profiling goals are gaining insights into data structure, quality, and content.? Two distinct sections of data profiling are, collecting data on data (AKA metadata) and creating actionable outcomes for improved data quality.

Its has following activities:

? Collecting statistics.

? Summarizing data characteristics.

? Identifying patterns & anomalies.

Its has following outcomes:

? Identify inaccuracies.

? Uncover inconsistencies.

? Investigate & report missing data.

How to build Data profiling?

While comprehensive data profiling is a large topic, here are 10 data profiling metrics built most effective data quality coverage.?

Steal these and use them no matter what tool or programming language you use for data onboarding.

? Duplicates: Understand the business definition of unique value(s) and identify duplicates, build exception processes to handle them. This should be doe at every incoming data point such as flat files, APIs, Mobile apps, Web pages.

? Counts (Total count, Unique value counts): Measure the counts to understand the trends in increase or decrease in these counts, Any sudden spikes - both up and down - will help? identify any anomalies in the data.

? Min, Max, Average values: Understanding valid value ranges especially with amounts and counts can help detect any abnormal and /or invalid values early in the data pipelines,

? Missing data: Detect missing data such missing data elements, missing values, missing data records to prevent downstream issues with data integrity and completeness.

? Data value ranges: Checking for the acceptable data values is a powerful method to detect data issues. These out of bound checks can, not only be applied to numeric values but can be applied to lookup values and/or reference values.

? Data Types (numeric, date, character) consistency across records: Checking for expected vs received data types especially with special data values such as dates can be a powerful way to detect data quality issues.?

? Data Format consistency (Dates, Phone numbers, etc.,.): Data formatting or lack and inconsistency in it can cause major data issues in downstream analytics processes. Simple example like MM/DD vs DD/MM can create havoc if its undetected.

? Invalid characters : Special characters, invalid characters can create confusion in the end point reporting and /or analytics. Checking for them and validating them through out the data pipelines can help prevent end user data issues

? Inter-column dependencies: Referential integrity between multiple columns within a record is essential also. For example keeping pre-calculated age in a static manner can become invalid after a while and become inconsistent with DOB value of the same record.

? Inter-table dependencies: Referential integrity across data sets play crucial role in ensuring data quality. As an example invalid invoice number in a line item table can leave that record orphaned and creates wrong report/dashboard

Who are using it ?

Here are quick examples of how 10 largest industries use data profiling for data quality, data integrity that drive better analytics, better data driven decisions to drive profits.

? Retail and E-commerce: Retailers and e-commerce businesses use data profiling to analyze customer purchase history, preferences, and browsing behavior. It helps in customer segmentation, personalized marketing, inventory management, and demand forecasting.

? Banking and Finance: Banks and financial institutions use data profiling to detect anomalies in financial transactions, identify potential fraud, and assess credit risk. It also aids in customer profiling, investment analysis, and compliance with regulatory requirements.

? Healthcare: Healthcare industry uses data profiling to analyze patient health records, identify patterns in medical data, and monitor patient outcomes and provider efficiencies. It helps in population health management, disease surveillance, clinical research and prevent fraud, waste and abuse.

? Manufacturing: Monitor and optimize production processes, identify defects or quality issues in products, and predict maintenance needs are some of the areas data profile is used. It improves operational efficiency, reduces? downtime, and ensures product quality.

? Telecommunications: Data profiling is leveraged to analyze customer usage patterns, identify network congestion or service issues, and predict customer churn. It supports them in network planning, capacity management, and targeted marketing campaigns.

? Insurance: Insurance companies assess risk, underwrite policies, and detect fraudulent claims using data profiling. Profiling helps in customer segmentation, pricing models, and predicting claim severity or frequency.

? Marketing and Advertising: This industry uses data profiling extensively to understand customer preferences, target specific audiences, and measure campaign effectiveness. It aids in customer segmentation, behavioral analysis, and personalized marketing strategies.

? Energy and Utilities: Data profiling used to monitor and optimize energy consumption, identify energy theft or anomalies, and forecast demand. It results? in improved energy efficiency, grid management, and resource planning.

? Transportation and Logistics: Data profiling helps them in optimizing routes, managing fleet operations, and predicting maintenance needs. Improves business outcomes in delivery schedules, reducing costs, and enhancing customer satisfaction.

? Government and Public Sector: Used in analyzing census data, crime statistics, or public health records. Outcomes are applied in policy-making, urban planning, and resource allocation etc.,..

Conclusion

Data profiling can start small with few profiling statistics and starting with data onboarding, As it generates value, it can be expanded to be applied through the data lifecycle across the organization.

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