Today, markets are more competitive than ever. To succeed, you must capitalize on your data. Data profiling is usually the first step in the process of gaining control over your data. Its aim is to ascertain the condition of the data stored in various locations and forms throughout your company.
A data profiling tool will plug into a data source. Then, it will provide an important amount of useful insight into the quality of your data. This knowledge is an essential component in the process of improving the health of your data.
Why Is Data Profiling Needed?
For companies, the ever-increasing quantities of data they need to properly manage is only one part of the problem. Data quality is the other. For example, if you don’t correctly formator standardize your data, you could miss sales opportunities. Also, you can make bad business decisions overall. As mentioned, data profiling will diagnose the quality of your data. Based on these insights, you will be able to create a plan to increase the health of your data.
For starters, data profiling provides the answer to the most important questions in data management: does the information stored in my systems match its description?
Let’s say you have the answer to this question. Now you can dig deeper and understand the relationships between data stored in different systems. But data profiling goes beyond this by helping you check if your data matches the company’s business rules.
How Does Data Profiling Work?
However complex, you can separate data profiling processes into two main categories: structure discovery and content discovery.
You could use structure discovery to validate the consistency of your data. Also, you can use it to check if your data is correctly formatted. One of the most common approach to achieve this goal is pattern matching. For example, you could apply pattern matching on a list of phone numbers, to identify the valid sets out of the entire dataset. Most importantly, you could use structure discovery to gain insight into the validity of the data by using statistical information like minimum, maximum, or average values.
Usually, you perform this step after you finish analyzing the structure of your data. It looks more closely at the individual elements and helps you gain an even more accurate image of the quality of your data. For example, the content discovery could help you find incorrect or ambiguous values that could prove costly if not discovered early.
How to Start Your Data Profiling Project
Data profiling provides the means of analyzing large amounts of data using a systematic, consistent, repeatable and metrics-based process. Given today’s data dynamic nature, you should continuously assess the quality of your data.
However, for many businesses, there is a problem. Data profiling projects are stuck between two options. On the one side, one must consider the time required to build an in-house data profiling tool. On the other side, one must consider the costs. Most suppliers ask for costly yearly subscriptions.
With WinPure Clean & Match you just have to pay one affordable cost for life. Behind its friendly user-friendly interface, our Data Profiling / Statistics module provides a powerful data profiling tool that can help your business discover patterns and meaning in your data. It also checks the quality of your data by analyzing formats, types, completeness and value counts. Moreover, WinPure Clean & Match provides a complete sets of statistics are specially designed to help to cleanse and correct your data and prepare it for data matching.
Usually, data management projects start with an accounting of all the inconsistencies within your data sets. The potential problems that usually arise from the usage of non-standardized data, like the inability of reaching customers via mail due to incorrectly formatted addresses, are costly. Fortunately, The Data Profiling / Statistics module of WinPure Clean & Match is designed to help you address and fix these issues early in your data management project.