What is no-code data merge?
Data merging and survivorship is not a trivial task. It is cumbersome—and expensive—to merge data and get a single source of truth. Yet, it is the need of the hour. When businesses implement digital transformations, get acquired, or launch new initiatives, they need accurate, reliable, and complete master record; also known as the golden record. Data merge is a key function in obtaining this record.
In this article, we’ll cover the basics of data merging and how businesses can use a no-code solution to get a single source of truth in a matter of days.
What is Data Merging?
Data merging combines multiple data sources into a single source of truth for better decision-making. For example, in the image below, a detailed sales performance overview is obtained by combining and analyzing three different data sources.
Traditionally, IT professionals use Excel or Python to combine data for business use. However, this method is rife with errors, takes weeks and months to accomplish, and sets a business back considerably.
In 2022, when the stakes are high and data-driven strategies are a necessity, businesses need automated solutions and data merge tools that can do the job faster, better, with more accurate results.
What is a Single Source of Truth (SSOT)?
A single source of truth is a master record that contains complete, valid, reliable information. It’s a central repository that contains data fit and ready for use, enabling teams to work, communicate, and collaborate better. That said, building an SSOT is a challenge, especially if a business doesn’t have the tools and talents to tackle factors like the volume and quality of data, poor business process and controls, disconnected leadership and isolated departments among many other problems.
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.
A single source of truth is critical for operational efficiency and better business outcomes. A well-planned data merging process ensures your business can build an SSOT effectively.
Why is Data Merging Important?
Data from multiple sources is merged for several key reasons:
1 . Mergers & Acquisitions: When companies unite, so does their data. Data merging combines dozens of data sources from both organizations to create a final, reliable, complete source of truth.
2 . Business Intelligence: Data on its own is meaningless. Data integrated into a BI platform however, gives insights and intelligence for strategic decision-making. For this purpose, data merging functions are used to consolidate internal and external data to gain in-depth insights on customer behavior, market conditions, company growth trajectory and much more. For example, sales and operations managers use BI dashboards to gain insights such as customer profitability and lifetime value.
3 . Digital Transformation: AI, big data, cloud migrations, and other transformation initiatives depend on accurate master records to be successful. For example, if a company moves from a legacy system to Amazon AWS, it will need to clean, merge, and purge data sources to get master records that can be transferred to the new system.
Data merge is also required for data-driven marketing initiatives, targeted ads, personalized product offers, and much more.
What is the Data Merging Process?
Data merging requires a linear plan that includes data prep, data cleaning, and data deduplication.
Here’s a step-by-step process breakdown that you can include in a data merge plan.
1). Data Import
To prepare for merging and treatment, data from different sources must be imported onto one platform. The import process is tedious with multiple manual steps. For example, data analysts spend 80% of their time cleaning and preparing data sets before importing it into a BI dashboard. Manual data prep wastes precious time and also leads to format and version errors, triggering downstream application failures.
Fortunately, most MDM tools have an easy one-click import feature that allows you to connect to multiple data sources with multiple data views.
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
Data sources contain human and system-generated errors such as typos, duplicates, incorrect format, and standards, or incomplete fields. The data profiling process scans data sources to determine the level of quality issues, provides a field-by-field view of errors, and helps the user catch hidden errors.
Some data analysts still perform manual data profiling with Excel formulas or Python scripts, which increases the probability of missing out on hidden errors. For example, the use of commas or full-stops in name fields can be difficult to profile using a manual method.
3). Data Cleansing
Data cleansing removes errors defined in the profiling phase. WinPure, an MDM tool, allows users to take control of their data quality issues with advanced data cleansing options to standardize data and bring it to an acceptable, consistent format.
Related: If you’re interested in learning more about MDM solutions, then check out our comprehensive MDM guide here.
4). Data Matching
At a basic level, data matching is used to judge the similarity of attributes between two data sets. For example, if Source A has Johnathan Smith and Source B has J. Smith, there are high chances it’s the same individual. Data matching identifies this similarity so the user can flag this entry as a duplicate and only keep the most updated, correct version in the master record.
Data match is best done with an MDM tool that uses multiple matching algorithms to find similar attributes. Accuracy is of critical importance in the matching process, therefore, professionals should refrain from attempting manual data matching as it can result in false negatives and positives. Inaccurate data matching results in damaging outcomes such as flagging the wrong individual during a security check or misdiagnosing a patient with a false record.
5). Merge & Survivorship
The last step of the data merge process is to create the golden record. Once you have clean and accurate, data sets, you can merge multiple records to create a single version of the truth. This could be done by either a simple merge (joining data from one set) or a complex merge (joining data from different sets).
When all of this is done, viola! You’ve got your master record!
Challenges with Data Merging
Most data analysts use Python to create merging rules, but the whole process can take months to complete. Worse, a manual approach increases the chances of errors and inaccurate matching.
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 in algorithm research + getting approvals
5 weeks in creating scripts + testing
2 weeks in matching the data
3 weeks in cleaning and deduping data
In an era of automation, losing all this time to manual data prep tasks will set you back!
2 . Managing non-exact and phonetic matches: In non-exact matching, attributes share similar traits instead of exact values. A combination of matching algorithms like Soundex, Levenshtein, and many others are required to detect non-exact or phonetic matches. With increasingly complex data structures, a business can spend months in the data match process alone.
3 . Need specialized talent: Trained & certified specialists (aka expensive resources) are required to pull off a traditional data merge initiative. Businesses could easily spend hundreds of millions of dollars annually on a human resource to perform mundane tasks easily done by a robust solution.
Lastly, in some organizations, teams work in silos to manage different steps of the process causing increase in errors, conflicts, and chaos. Businesses mostly fail with their SSOT initiatives simply because of of operational inefficiencies, led by an extreme dependence on individuals to perform the most basic of tasks.
Using WinPure’s No-Code Solution
Data merging should no longer be a manual process, especially with the emergence of no-code solutions that can do the same job faster, better, with more accurate results – at a fraction of the cost.
WinPure is one such example of a no-code data management solution that offers an easy-to-use interface to manage complex data merge functions. It’s ideal for businesses of all sizes and is a much more affordable alternative to complex platforms like IBM, Semarchy and others. With a point-and-click interface, users can profile, clean, match and merge data within minutes. It also offers the industry’s highest data match accuracy rate.
Here’s a breakdown of the data match and merge function in WinPure’s MDM solution.
1 . Import files from multiple sources: Drag and drop multiple files from your computer and connect your data instantly.
2 . Create tables for mapping configuration: Users 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: Easy point-and-click feature to clean, standardize, normalize, and transform data in bulk.
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: Define custom merge rules and priorities, set master records in bulk, de-duplicate groups, merge/link records and update single data points.
All of this can be done in just 1 day, on 1 platform, without the need for specific programming talent.
Save up to 500 hours & $100K in implementation time & talent onboarding
The world is witnessing an economic crunch and companies are struggling with budgets. But what if you could still create master records and optimize your data management without spending millions? WinPure was built to eliminate redundancy, save hundreds of hours of manpower, nearly $100K in expenses, and gives data professional the opportunity to take on strategic work instead of mundane tasks.
WinPure’s ease of use allows even non-tech users to handle data prep with minimal training.
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
We recommend downloading a free trial and see for yourself the capabilities of WinPure’s data merge & survivorship functions.
Conclusion
Data merge consolidates data sources into a single version of the truth. It’s a complicated, time-consuming process consisting of mundane and redundant tasks. In an age when businesses need instant access to accurate master records, it’s no longer feasible to spend 3 or 4 months in a data merge activity. Hence, no-code tools like WinPure can be used to implement a data merge process faster with minimal resource use.
Let our solution handle the mundane tasks, so your team can focus on strategic efforts.
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