“Data is the new oil. It’s valuable, but if unrefined, it cannot really be used” – this quote by the mathematician Clive Humby perfectly signifies the necessity of fine-tuning data and maintaining optimum quality.
Bad data can wreck your business decisions, waste vital resources, and eat up a considerable chunk of revenue. According to an estimation by IBM, poor-quality data causes businesses to lose a massive USD 3.1 trillion every year in the US.
Data issues such as incorrect integration, inconsistencies, redundancies, duplicities, improper formatting, etc., lead to lower business productivity, higher maintenance cost, and lost opportunities. This is where the process of data profiling comes into play. It helps businesses intelligently analyze data, thus improving its quality and subsequent usability.
What is Data Profiling?
Data profiling involves systematic analysis or review of data from an available source. It also involves generating useful summaries pertaining to that data. Simply put, through data profiling, you can determine the hygiene and quality of a specific dataset. Therefore, you can assess large sets of data for completeness, accuracy, validity, and consistency through metric-based procedures and analytical algorithms.
Data profiling allows you to derive critical insights and statistics that may be leveraged to gain a competitive edge in the market. By having a 100% error-free picture of your data, your internal team will understand its business interrelationships, content, and structure more efficiently.
Different Types of Data Profiling
Data profiling can be further classified into three main categories:
-
Content Discovery
This process of content discovery checks individual data items for errors. It emphasizes data quality and helps identify records with any incomplete, ambiguous, incorrect, or null values. For instance, contact numbers without area codes, missing digits, etc.
Vague data like this makes it impossible to reach customers. Content discovery exposes such inconsistencies in your datasets and lets you handle problems likely to arise due to non-standardized data.
-
Structure Discovery
Structure discovery helps businesses understand how efficiently their data is structured. It verifies if the data is consistent, reliable, uniform, and correctly formatted.
Also called structure analysis, this process employs statistical measures to provide insights into the data validity. You get information through means, modes, minimum and maximum values, median, sums, standard deviations, etc. Furthermore, it matches patterns to discover correct format-sets within a data source.
-
Relationship Discovery
Relationship discovery reveals the interconnections between different parts of a dataset. It also specifically tells you the associations between different data sources in terms of distinctions, similarities, overlaps, etc.
For instance, cardinal relationships between tables or cells in a spreadsheet can be identified through relationship discovery. Through this process, metadata analysis is carried out to pin down links between particular data fields. Thus, you can take care of issues that spring up due to the misalignment of data.
Why is Data Profiling Crucial?
Businesses capture data in numerous ways from a host of sources today, such as the Internet of Things (IoT), blogs, online transactions, social media, direct database entries, emails, and so on. Naturally, the probability of erroneous data making its way into the source systems is pretty high. But, you can avoid this by rigorously profiling data.
In a nutshell, as data gets bigger with each passing second and cloud infrastructure establishing dominance, data profiling is becoming increasingly essential.
With this process, you can examine, understand and systemize your customer data. As a result, you can pre-emptively devise a plan to deal with data concerns before they wreak havoc in your business operations.
How is Data Profiled?
The conventional approach to data profiling is to hire a technical programmer or a Data Analyst skilled in the Structured Query Language (SQL), who queries the dataset manually. Thankfully, there are various tools and software available nowadays that automate the process for a more detailed analysis at enhanced speed.
Data profiling can be executed in several ways. Nonetheless, there are four critical techniques through which data profiling tools investigate data.
-
Column Profiling
In this method, an entire table of data is scanned to count the number of times each value comes up within every column. Column profiling is effective in unveiling the frequency distribution and patterns of data values inside a single column.
-
Cross-Table Profiling
This technique assesses duplicate value groups across individual data tables. Cross-table profiling uses foreign key analysis to ascertain the differences and commonalities in data types and syntax.
For instance, it can reveal when same-named columns contain different values or differently-named columns contain the same data values. This process can help decide what data values can be mapped together and lets you cut out redundancies.
-
Cross-Column Profiling
This method is used to shift across columns to conduct dependency and key analysis processes. Special algorithms are used for running the two processes mentioned above, which scan through a table multiple times.
In simple words, both dependency and key analysis processes, covered under cross-column profiling, show the functional dependencies and relationships among different data values within a specific table.
-
Data Rule Validation
This technique involves setting up rules that monitor the integrity and authenticity of data entered into a database or system.
To Sum Up
Know that you are prone to lose out on revenue irrespective of the size of your business due to poor quality data. And, given the dynamic nature and intimidatingly large amounts of data entering your system today, the need for its constant evaluation is undeniable.
Therefore, data profiling is a necessity for ensuring data quality. It can help you reduce a project’s implementation time significantly and deploy effective data strategies for business success.
