Data merging & survivorship is not a trivial task. It can be cumbersome—and expensive—to resolve conflicts, merge data, and combine data sets. It takes a lot of time, money, and effort to merge records in a database when done manually. If you’re looking for an easier way to do this, you’ve come to the right place.

In this post, we discuss a no-code solution that allows users to cleanse and de-duplicate data.

Let’s dive in with some basics! 

What Does it Mean to Merge Data? 

Customer, vendor, and product records are stored in multiple data sources. In some instances, businesses also have to store customer data from 3rd party tools which makes for a complex data infrastructure. 

An example is an automotive company that stores actual sales records and sales targets in one system but in separate databases. To validate sales performance, users would need to combine the two data sets. 

Data merging, thus means combining multiple data sources or data sets for better decision-making. More importantly, it’s done to get a single source of truth. 

Data Merging to Get a Single  Source of Truth (SSOT)

A single source of truth is a master record that contains complete, valid, and reliable information that everyone agrees with. An SSOT ensures efficiency as teams get access to clean, consistent, accurate data.

Creating this central repository isn’t easy — in fact, it can be downright challenging due to various factors including:

  • extensive data volume
  • multiple sources
  • incompatible formats
  • constantly changing schemas/data models etc
  • lack of access controls etc…

SSOT initiatives can significantly impact project management and communication. According to an Autodesk report on the construction industry, $31.3 billion in rework was caused by poor project data and miscommunication in the U.S. alone in 2018. Poor project data can lead to costly rework for data-expensive businesses such as banks and financial institutions.

Data merging is not only important for maintaining a single source truth; there are other reasons as well.

Why is Data Merging Important?

Data from multiple sources is merged for several important reasons: 

Why is Data Merging Important

1 . Mergers & Acquisitions: Data is merged when two or more organizations are acquired. This becomes more difficult if companies have different data management systems or use completely different data management methods.

One example is Company A, which may still use a legacy system while Company B might be using advanced cloud computing. Company A’s database will need to be subject to a comprehensive data quality assessment. Data will be merged, duplicates would be removed, and standardization rules created to suit Company B’s needs.

Data merging is crucial in these situations because it allows companies to combine multiple data sets, add, delete and update records as needed.

2 . Business Intelligence: BI’s purpose is to improve decision-making. This is possible only when your data sets contain reliable information. Data merging allows you to connect multiple data sources to generate insights and analytics, which is how business intelligence is generated. Sales and operations managers use BI dashboards to gain insight such as customer profitability and lifetime value. BI can also be used to help operations analyze supply chain metrics and find ways to optimize their processes.

3 . Digital Transformation: Moving to the cloud? Migrating from legacy systems to advanced data management infrastructures? Data merging and survivorship functions create master records that you can save for backup in order to prevent information from being lost during the migration.

Data merging is a common requirement in all major functions of an organization to improve workflow, increase data management capabilities, and give users quick and easy access to reliable data.

What is the Data Merging Process?

Merging identical records in a database may seem like a straightforward process but it can go awry if you skip key steps like data cleaning, de-duplication, and standardizing the data. The first step is to ensure that the duplicate records have the same structure and data.

This can be done using an ETL tool like WinPure which uses a visual interface to merge records in a database. Once you have identical data, the next step is to identify the fields that need to be merged on and create two-way merge rules for those fields.

Step by step guide to data merging (1)

Here’s a step-by-step process of data merging.

1). Data Import

To prepare for merging and treatment, data from different sources must be imported onto one platform. The import process itself is tedious as there are lots of manual steps involved in extracting data from different sources. Experts must spend considerable time cleaning and preparing multiple CSV files. This is how most of the data is exported leading to high chances of errors.

Program Duplicates

Fortunately, most MDM tools today make it easy to import data and connect with other sources. A good MDM tool should make it simple to import multiple data sets and allow you to connect to multiple data sources.

WinPure’s MDM platform, for example, allows you to import from SQL Server, Excel, and CSV. The Clean & Match Windows software has all the out-of-the-box connectors that can connect to anything from personal spreadsheets to large data warehouses or massive big data systems.

2). Data Profiling

If you have to deal with different data sources, there are likely many errors you cannot catch. These errors could be human-caused, such as typos or insufficient details. Or technical errors like incorrect formatting rules. Data profiling, a function found in many MDM tools, scans data sources to determine their health. This allows you to see row-by-row the errors that are affecting your source.

Advanced errors are difficult to detect if it is done manually. This could include missing data, duplicate entries, inconsistent formatting, and other errors users might not be aware of. Hidden errors are more likely to be discovered if a tool has advanced profiling capabilities.

3). Data Cleansing

After errors have been identified in the data profile it is time to conduct a data cleansing activity. This will remove errors such as numbers in text fields, commas, spaces, etc, or incomplete entries from the data profile. WinPure, an MDM tool, allows users to take control of their data quality issues by offering advanced data cleansing options that allow you to standardize data and bring it to an acceptable, consistent format.

Clean Screen

4). Data Matching

This stage is crucial in the data merging process and requires an advanced fuzzy data matching engine or tool to match multiple data sources and eliminate duplicate entries. Users cannot rely solely on Excel or custom-coded programs to eliminate duplicate entries due to the complexity and unstructured nature of data. It can take months to set up a match structure and implement it. Even with the best team in place, it is still difficult to get a near-accurate match.

5). Merge & Survivorship

The final step in the data merging process is to actually do it. Once you have clean and accurate, reliable, groups of matching records, users can create criteria to merge multiple records. This could be done by either a simple merge (joining data from one set) or a complex merge (joining data from different sets).

Not Updated View

When all of this is done, viola! You’ve got your master record!

Technical Challenges with Data Merging 

Data merging can be complicated by legacy software and manual matching. These are both time-consuming and very difficult to use data with any flexibility. Even though Python programming can be used to create and execute scripts, it still takes hours for simple data sets to be matched. It would take longer to achieve accurate results for complex matches and merge operations.

Here are some of the main challenges associated with custom programming on Python for data matching and data merging: 

1 . Requires significant processing power: Processing this level of information takes a lot of time, regardless of the fact that Python automates it.

If we were to calculate for a 1,000-row data set, at best, you’d be losing:

4 weeks = algorithm research + getting approvals
5 weeks = creating scripts + testing
2 weeks = matching the data
3 weeks = cleaning and deduping data

 

Following this, you will have to keep tweaking and adjusting the algorithm to meet constantly evolving data requirements.

2 . Data profiling and prepping become a challenge: Before running the match algorithm, you’ll have to profile the data for errors and make sure that typos, non-standardized texts, and duplicates are all resolved before matching. Even if this is done via Python, it could take ages and doesn’t always end in a positive result.

3 . Managing non-exact and phonetic matches: Many datasets have non-exact duplicates – for instance, Johnny Junior could be a duplicate of Johnny Jr. Moreover, phonetic words pose a significant challenge when matching and while Python can account for this using the Levenshtein distance, there is still room for error. You’d also have to combine several match algorithms: Soundex, Levenshtein, deterministic, probabilistic, etc to get the results you need. Data sets today are complex and cannot be matched with just one type of algorithm.

4 . Need specialized talent: Yep, you’ll need to hire people who can use Python for creating data match expressions. Not only are they costly to onboard, but are also hard to come by! 

In some organizations, data teams work in silos to manage different steps of the process. For example, interns do the cleanup, then more senior professionals would do the matching and merging. Working in silos creates more errors, leads to more disconnected insights, and also increases conflict between teams as no one has a consolidated view of the process. 

This is where a no-code, easy-to-use, simple MDM solution can help you and your team address technical challenges while getting the job done on time. 

Using WinPure’s No-Code Solution 

WinPure may be the solution for you if you are looking for an easy-to-use, no-code solution with a user-friendly interface.

WinPure is a no-code data management and data merging solution that offers an easy-to-use interface. This is a great solution for small businesses or organizations that want to ensure their employees have all the information they need about customers, vendors, employees, partners, vendors, and other clients.

Here’s a breakdown of the data match and merge function in WinPure’s MDM solution. 

1 . Import files from multiple sources: WinPure allows for easy import of files from multiple sources. Drag and drop multiple files from your computer to connect to your data.

2 . Create tables for a mapping configuration: You can create tables to support a mapping configuration. The user can import files, lists, or data sources that contain column structure/schema and then import the data to the “host” table.

3 . Simple and advanced data cleaning: Data cleaning options offer a set of powerful point-and-click features to clean, standardize, normalize, and transform data, apply changes in bulk, and noise removal.

4 . Match configuration: Offers all of the features necessary to create multiple data matching rules, offering flexible settings to produce the best quality results. Match configurations can also be saved as templates to be used or modified later. Users can select the exact match for fuzzy matching levels (20% – 95%) to get the accuracy they require. You also get the deduplication feature that will let you keep the best record as the surviving record in the final results, while deleting non-master duplicates.

5 . Merge and survivorship:  This allows you to use multiple functions to define rules and priorities, set master records in bulk, de-duplicate groups, merge/link records and update single data points. You can also export the final results. First, run a matching process. The output of the match process can be used to select the source for the Merge and Survivorship module.

6 . Setting the master record: After the match and merger are completed, the final step would be to create the master record. The most common option is the most populated record from any data source or from a specific data source involved in the match process. 

All of this can be done in just 1 day, on 1 platform, without the need for specific programming talent. 

Save on time, money, & talent

As shown above, it takes weeks for a team to search, test, and implement match algorithms. Add profiling, cleaning, and standardizing processes to this list, and you’re booked for months!

With WinPure’s no-code, and point-to-click solution for data merging, you can do 4 months’ work in literally 4 days. No extra costs, no need for hiring specific talents, and no lagging. Imagine being able to create a single source of truth for your organization’s data without having to spend millions of dollars.

Once the record is set, WinPure’s automation feature lets you schedule routine maintenance which means you’ll be staying on top of your data quality challenges (available only in the Enterprise edition).

Additionally, with the whole import, cleaning, matching, and merging functions in one dashboard, you save hundreds of thousands of dollars as there are very few tools out there that give you a one-stop platform.

Lastly, the ease of use with WinPure means anyone in your team can use the tool without requiring any kind of tech training. We know how simplicity and ease of use are important for users when it comes to dealing with data.

Overall, with WinPure, you can save:

  • ** Thousands of hours of manpower 
  • ** At least $500K in talent recruitment 
  • ** At least $100K in buying expensive solutions that you might not even need 

But before you make a decision, we recommend downloading a free trial of the tool and see for yourself the capabilities of WinPure’s data merge & survivorship functions within the MDM platform.

Conclusion 

In the end, data merging is all about ensuring a single source of truth. If you’ve ever worked in an organization with multiple data sources, you’ve probably experienced the pain of having to pull information from several different places just to get an accurate picture of where things stand overall. And while there are many ways to merge your data, WinPure offers one that’s less technical and more affordable than others on the market today. Interested? 

Download your free trial and test it out!

DOWNLOAD DEMO

 

By Farah Kim | June 23rd, 2022 | Posted in Merge Purge Software

About Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science and a MA degree in Linguistics. She is fascinated with data management and aims to help businesses overcome operational inefficiencies caused by ineffective data management practices.

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