DEV Community: Zara Ziad

Finding and removing duplicates through software tools

Zara Ziad — Thu, 07 Jul 2022 11:53:46 +0000

In Datafold’s recent survey on The State of Data Quality in 2021, most respondents said that they do not use any tool or software to clean data, and manually perform data quality verification and fixes. Nowadays, process optimization and operational efficiency are impossible without technology. A team that relies on manual effort to execute complicated tasks, such as bulk data cleansing and deduplication, is bound to make mistakes and produce inconsistent results.

In this blog, we will look at what a data deduplication software is, the most crucial features and functionalities found in such a tool, and how it can help your data quality teams to save time and money. Let's get started.

Data deduplication software

Data deduplication software is a tool that efficiently executes the data deduplication process which includes:
Comparing two or more records,
Computing the probability of these records being similar or belonging to the same entity,
Deciding which information to retain and which to override,
Merging records to get a single comprehensive record for an entity,
Deleting and discarding duplicate records.

The data deduplication process requires advanced knowledge about data and how to deal with it to get optimal results. Otherwise, you may end up losing crucial information. Data deduplication tools come with advanced data profiling, cleaning, and matching algorithms – that are capable of processing millions of records in a matter of minutes. This is where automated tools can perform better and more quickly, accurately, and consistently – as compared to human effort.

Prerequisites of accurate data deduplication

Since datasets contain a variety of data quality errors, the accuracy of your data deduplication process depends on a number of pre-requirements, such as:
Data residing at disparate sources must be combined or integrated together for the process.
All records must follow a standardized format and pattern.
Data values must be present in the same data type and unit of measurement.
Data match definitions must be configured to define which attributes should be used for matching data.
Rules must be defined to compare duplicate records and make decisions to merge and override data.
There must be a way to export or store duplicate records in case you don’t want to delete them.

Features of data deduplication software

If you take the prerequisites into consideration, it becomes clear that a data deduplication tool must be equipped with all these features. Let’s discuss the most crucial features to look for in data deduplication software:
Data ingestion: the ability to import, connect, or integrate data residing across disparate sources, including local files, databases, third-party applications, etc.
Data profiling: the ability to uncover hidden details about your data in terms of blank values, incorrect patterns, formats, and datatypes, to identify possible cleaning opportunities.
Data cleansing and standardization: the ability to eliminate incorrect information and transform data values to follow a standardized view across all sources.
Data parsing and merging: the ability to divide one column into multiple columns, or inversely, combining two or more columns into one.
Data match configuration: the ability to configure match definitions and tune algorithms according to the nature of data to get optimal results.
Data deduplication: the ability to execute different types of matching (exact or fuzzy) and compute the probability score of two or more records belonging to the same entity.
Data merge and survivorship: the ability to merge and override duplicate records and get a single comprehensive view of entities.
Data export or load: the ability to transfer a single source of truth to the required destination, such as a local file, database, or any third-party application.

Factors to consider while employing data deduplication software

There are quite a few vendors in the market that offer the features mentioned above in their data deduplication tools. But there are some factors to consider while choosing such a tool:
What does your organization require?
Data quality means something different for every organization. For this reason, instead of buying a tool that you heard works for somewhere else, you need to find out what will possibly work for you. Here, a list of data quality KPIs will help you understand what you are looking to achieve and whether the solution under consideration can help you implement that vision.
How much time and budget are you willing to invest in this tool?
Adapting to technological changes in an organization asks for time and money. You may need to assess what budget you are willing to invest in this tool. Also consider the fact that it might take some time for your team members to learn the new tool and use it efficiently.
What does your data quality team prefer?
This is a key player in your decision to choose a data deduplication tool. Data quality team members are often present in organizations as data analysts, data stewards, or data managers. These individuals spend most of their day dealing with multiple data applications, sources, and tools. Let them decide which tool helps them get their job done most efficiently.

Conclusion

Data deduplication is the first step in enabling a reliable data culture at a company and creating a single source of truth that is accessible to everyone. When your datasets are free from duplicates, you can get many benefits, such as accurate data analysis, customer personalization, data compliance, brand loyalty, and operational efficiency. Investing in such tools will definitely reduce rework and free up your team members to focus on more important tasks.

A codeless solution for cleaning and verifying your address data

Zara Ziad — Wed, 23 Feb 2022 10:10:15 +0000

Today, data has become one of the greatest assets of an organization. Whether you want to design customer journeys or forecast business future, data is the main ingredient that helps to attain successful outcomes. This is why business owners invest in developing custom solutions for keeping their data clean – especially a customer or contacts database.

But since multiple employees at a company work with, manipulate, and use the contacts dataset, it is soon filled with inconsistencies and inaccuracies. And then the company’s IT staff is expected to build an in-house solution that magically gets rid of all errors present in the database.

Coding every solution from scratch

Although it is possible to write code for cleaning and standardizing datasets, but it is definitely an inefficient solution – considering the number of resources (time, people, and money) required for its implementation. And after factoring in the cost of annual maintenance and upgrades, it is 2-3 times more expensive than adopting existing solutions.

This reminds me of something one of my coder friends told me recently: At some point in every developer’s life, they realize how unproductive it is to code every solution by hand. Sometimes it is more efficient to adopt existing solutions available in the market – open-source libraries or commercial products – rather than coding solutions from scratch.

In this blog, I will explain some common terminologies and steps involved in cleaning and validating addresses present in a customer’s database. This will definitely help you to understand what to look for while choosing an existing solution available in the market. Let's get started.

Common terminologies involved

Before we get into specifics about the process, there are some common terminologies used in this domain, let’s first go over them and see what they mean.

Address standardization

Address standardization (also known as address normalization) means updating the format of an address according to an authoritative standard (such as the USPS addressing standard in US). This process makes sure that the addresses are present in an acceptable format – includes correct spelling, abbreviations, geocodes, as well as is appended with ZIP+4 values.

Address verification

Address verification (also known as address validation) is the process of running the standardized addresses against an authoritative database (such as the USPS in US), and making sure that these addresses are real – meaning, they are mailable and valid locations within the country for mail delivery.

Difference between the two

Sometimes both these terms are used interchangeably, but there’s a difference between the two. Addresses should be first standardized to follow an acceptable format. Once standardized, they are now ready to be verified to check if these addresses are real and valid.

Process of standardizing and validating addresses

The following steps are involved in this process:

1. Profiling addresses

Before any activity can be performed on the address database, it is important to assess its current state. This is where address profiling can be very helpful. It identifies the records that contain incomplete or missing address information, as well as the ones that don’t follow a standardized pattern.

Address profiling highlights potential cleansing and standardization opportunities present in your dataset. Furthermore, this profile report is usually generated again at the end of the process so that both the initial and ending reports are compared to see if there are still errors present in the dataset.

2. Parsing addresses

The address standardization starts by parsing every address into its sub-components. This is important since addresses are mostly stored as a single field in a dataset. And running validation checks on the entire field is not as accurate as running them on its sub-pieces. For this reason, a single address is usually parsed into street number, street name, zip code, postal code, directions, city, state, and county.

3. Geocoding

In this step, the latitudinal and longitudinal geocodes are computed for all addresses. In addition to that, depending on the computed geocodes, you can also find out the 5-digit zip codes and 4-digit routes of delivery area.

4. Reconstructing addresses

Once all this information is computed and standardized, it is not time to reformat and reconstruct the addresses in the required format. This can be done and then saved in the database, or if needed, it can be computed in real-time whenever and however needed.

An example of such formatting is the USPS addressing standard that requires the delivery address to cover three lines – the first one contains the recipient’s name, the second one contains the street address, and the third one contains the city, state, and zip code.

5. Verifying addresses

When an address has all the necessary components, you can now verify its validity against any authoritative database to find out whether the address is an actual, mailable location. In addition to verification, such databases can also tell the type of address – residential or business – as well as some other secondary details.

Conclusion

And there you have it, a 5-step, codeless process for cleaning and verifying your address data. Implementing such a solution from scratch can be very challenging and can take years to improve the result accuracy.

There are many address verification tools in the industry today, including some that are CASS-certified – a certification title that the USPS assigns to software vendors offering accurate address standardization and verification services.

Such tools can definitely improve your team’s operational efficiency and enable them to design exceptional experiences for customers by using correct and accurate location information.