What is data profiling?

The process of data profiling involves screening data thoroughly to ensure its quality and accuracy. Several analytical algorithms can examine dataset features like its mean or percentile, minimum or maximum, and even its frequency, right down to every value it contains. This analysis is followed by methods that help bring hidden metadata, such as frequency distributions or key connects, co-relations, and dependencies, to the forefront. All this information is used to determine how these factors make a difference to an organization and its operations.

Data profiling can ensure that errors, often a part of customer datasets, don’t cost an organization. Null values, or values of exceptionally high or low frequencies, which are missing or don’t follow a particular pattern, can result in the corruption of a data set and can negatively impact a company if not detected early on.

Why use data profiling?

Here are three key reasons why organizations use data profiling.

Providing validity to a project

When a project is at the ideation stage, managers may use data profiling to determine if they have a strong enough base to get the project off the ground. With data profiling, a manager can decide whether or not they have enough information to make the right decisions needed for the project. There are times the data will show that there isn’t a solid foundation to proceed, and this is best determined at the beginning rather than midway through a project.

Enhancing data quality

With data profiling, organizations can ensure that their data is clean, of top quality and acceptable for distribution across various departments. Ensuring such quality becomes especially important when dealing with legacy systems and datasets where information was manually entered and has now been moved to the cloud or is being used as direct inputs for a project.

Enhancing discoverability

Organizations today must be agile. This means that employees should find specific data input easily when they search for it to make timely decisions. Data that doesn’t lend itself to searches can cause problems when a user needs to locate it inside a larger string. Categorizing and tagging data makes it easily accessible with just a few targeted keywords. Essential to improving discoverability is uncovering and examining all metadata in a source dataset. This metadata needs to be scrutinized and updated before any data project begins.

Understanding the benefits of data profiling

There are a range of benefits of data profiling for a business or organization.

Leverage quality data for improved decision-making

Data profiling is a process that can be used to ensure that the data the user is working with is of the highest quality. When a business works with quality, reliable data, it can leverage that to derive information that can positively impact the business. This information can be from different categories and be utilized by people across the company for various applications. It can help identify possible challenges and predict the business trajectory.

Active crisis management

Data profiling can identify problem areas and deal with them before they escalate.

Predictive decision-making

With data profiling, even the most minor mistakes can be stopped from growing into a more significant problem. Businesses can understand the various outcomes for a wide range of scenarios. Such capabilities help create an accurate picture of the status of a business and aid decision-making for long-term improvements.

Ensuring organized sorting

Datasets tend to have a diverse set of data origins in multiple sources. These sources can be social media, customer surveys, and big data markets. A user can trace data right back to its source with profiling, which makes way for ideal encryption. Professionals can then analyze the various datasets and references to ensure that data is in keeping with standard statistical parameters and rules of business.

Data profiling steps

With data profiling, an organization is working on analyzing an extensive amount of data in a systematic, repetitive process. This process is consistent and based on fixed metrics. With data being dynamic in the current business atmosphere, being able to evaluate its quality becomes a necessity constantly. However, businesses' main problems are building internal data profiling tools and the high costs involved. If an organization wants to begin data profiling, there are four main steps to set the correct, stable, and consistent base.

1. Set the base with discovery

Every business planning to begin data profiling needs to start with discovery. This is the discovery of structure, content, and relationship.

2. Steps in profiling

In profiling, an organization begins by listing details present in every dataset they are working with. Consider it a dataset that gives a clear insight into all the user datasets. While larger companies depend on enterprise resource planning (ERP) systems or have exclusive data management platforms, smaller organizations tend to use options such as spreadsheets. Once profiling is done, data can be segregated based on how useful and easily accessible it may be, compared to other data lower on the priority scale. The latter can be stored in low-priced storage set-ups.

3. Data standardization

With data segregated and easily accessible, the next step is a standardization of data across the board. For example, phone numbers can be included with the international dialing code or without it, or zip codes may be separated with a hyphen or spacing or without them. Once all the data is standardized and follows a single format, analysis–by humans and by computers–becomes more accessible.

4. Cleansing for better standardization

After standardization comes the last step of cleansing data, this is another level of standardization that ensures every formatting error that arises from the application of new standardization rules is fixed. Any data that is corrupt or irrelevant is removed at this stage. Robust profiling policies and powerful backup can prevent any data issues beyond this.

The data profiling process

With data profiling, users can examine the quality of data that they work with. It is a process that consists of several analyses that evaluate the structure and content of data and then conclude it. Once this evaluation is complete, the user may take up the conclusions or reject them.

Column analysis

Except for cross-domain analysis, column analysis is required for all other forms of research. Here, a frequency distribution is set up for column or field data analyzed in a table or a file. The frequency distribution will summarize results for every column with information like statistics or any deductions based on the data features. Any anomalies that arise can be removed with data cleansing tools. Frequency distribution is utilized by primary key and baseline analysis, both of which are subsequent forms. Within column analysis, there are four kinds of comments:

Domain analysis: Invalid values or those that are incomplete affect the quality of an organization’s data, making it challenging to utilize. Results from domain analysis can be used when a user plans to apply a data cleansing tool to eliminate anomalies.

Data classification analysis: With this, the user can arrive at a data class for every column of data. This classification system helps with categorizing data. It becomes increasingly difficult to compare with data from other domains when classified with erroneous data. Data domains are reached when a user looks for data with similar values.

Format analysis: This form of research helps create a format expression for every data value. Format expression refers to a pattern where a character symbol represents every distinct character contained in a column. An example would be where every alphabetic character can be described with the character symbol of ‘A’ and every numeric character with ‘9’. Creating precise formats keeps an organization’s data compliant with established standards.

Data properties analysis: In this form of research, a comparison is made between the defined properties of data before analysis with the properties inferred by the system during the examination. Data properties look at data features like the field length or data type. When properties are well defined, the user can ensure that the data will be efficiently used.

Key and cross-domain analysis

In a key and cross-domain analysis, the relationships between data of different tables are assessed. Data values are examined for both foreign and defined essential allotments. A data column may be designated as one for a foreign key when the importance of the data matches that of an associated primary or natural key. An incorrect foreign key loses its relationship with a genuine key present in another table.

Once this key and cross-domain analysis are done, the next step is to run the data through a referential integrity analysis. This is an analysis capable of identifying any relationship violations between the foreign and natural keys. In this analysis, foreign key candidates are examined closely to corroborate that they match perfectly with the values of their associated genuine key.

A user can ascertain whether different columns share domains with the key and cross-domain analysis. When there are several columns with overlapping data, they are said to have a common domain. Such columns with a common domain may indicate a relationship between a foreign and primary key. This can be examined in depth with foreign critical analysis. But, in most cases of common domains, this means nothing more than a redundancy between the columns. These redundancies can be removed with a data cleansing tool to free up memory and speed up any processes that are related to them.

Baseline analysis

A baseline analysis is done when a user wants to run a comparison between an earlier set of analysis results and a more current one for the same data source. If they find differences, they can determine if the change is something significant and whether it has improved the data quality.

The larger the dataset, the greater the need for data profiling

In an increasingly connected world, the amount of data and various sources will only increase with each passing year. Data profiling is a robust assessment that uses many business rules and analysis algorithms to find, assess and address inconsistencies in data. Having this kind of knowledge helps improve the quality of an organization's data and helps improve the consistency and heath of the ever changing growth of data that it will work with.

The need for and use of data profiling will continue to grow exponentially. Large corporate-related data warehouses will interact with ever-growing data sets emerging from various sources. With the Internet of Things in play, these sources will only increase, as will the amount of data produced. Identifying all the inconsistencies within data sets is the beginning of making the best use, and data profiling is a significant first step towards delivering quality data.