Data quality must be at the forefront of any data warehouse and analytics project to guarantee validity and value within the information you receive.
The key is to focus on three main areas to build a solid data quality best practice standard.
The three main areas of a successful data quality strategy include:
- Data Terminology
- Data Governance
- Data Profiling
Now that we’ve discussed the importance of a sound data governance foundation and data terminology in parts 1 and 2 of this blog series, today we will dive into the MOST critical aspect of data quality…data profiling.
What Exactly Is Data Profiling?
Data profiling is the process of examining data from the source, collecting statistics, and creating detailed summaries of that data. A detailed analysis of the data source must happen to produce these summaries. Like it or not, you can’t trust copybooks, data models, or source system experts. Regardless of how hard we may try, errors inevitably find their way into our systems, resulting in poor data quality. You must know your data before you can fix it.
Why Is Data Profiling So Important?
Data profiling is vital because data processing and trusted analysis cannot happen without it. When data profiling starts off a project, it can significantly shorten the development cycle by identifying source system data anomalies and accelerates an understanding of source system data. Data profiling should be the best practice at the start to discover if data is suitable for analysis—and make a “go / no go” decision on the project.
Data warehouse and business intelligence projects depend on data profiling to uncover data-quality issues and determine corrections needed during the extract-transform-load (ETL) process. Data Profiling can also highlight problem sources and identifies the root behind the problem (i.e. user inputs, errors in interfaces, and data corruption). When profiling the data, analysts review the structure, check formats, mathematical equations (min, max, and sum) and delve into content by looking at individual data records to discover errors, analyze the completeness of data and investigate the relationship of the data between tables, spreadsheets, and other sources.
What Is the Best Practice for Data Profiling?
Like most things in life, there are numerous ways to accomplish this. The old way (also known as the long way) required a skilled resource to manually query data. The new way (and our recommendation) is to utilize data profiling software. For our projects, we use the Oracle Enterprise Data Quality Profile and Audit software product. With advanced data profiling software, you increase the data profiling speed. In addition to saving time and money, it also allows your project to move forward with sound, reliable data. Data profiling software also creates a common repository utilized by the entire team. Finally, it provides a more thorough analysis of the data sources compared to manual processes that only query a subset of the data.
Data Profiling, Data Terminology, And Data Governance; The Key to Real Data Quality
If someone asked if your data is sound, would you say yes? What if I asked you to bet your job on it? I bet you paused before answering, didn’t you? A robust data quality process involving data profiling, data terminology standardization, and data governance is the only way to overcome skepticism and distrust in your data. Using these three data quality techniques will have you answering, “yes” in no time at all.
