Many misconceptions can arise when learning a new language.
For example, a Spanish learner may believe that saying “Yo soy embarazada” means “I’m embarrassed.” (it actually means “I’m expecting.”)
Every language is unique, and even within a language, dialects differ in subtle ways that can make translation challenging.
Surprisingly, though, this isn’t a problem that only affects people.
The wide range of languages used in the digital world can cause miscommunication, particularly when data needs to move across applications.
Thankfully, there’s a procedure called “data mapping” that works as a translator to make sure your databases can speak with each other more effectively.
Data mapping is essential for many data operations to be successful.
A single data mapping issue might cause inaccuracies to spread throughout your company, which can result in faulty analysis.
Every business will transfer data between different SaaS platforms at some point.
Furthermore, different systems store the same data in various ways.
Therefore, a roadmap is required to ensure that data is appropriately transported to its destination while moving and consolidating data for analysis or other operations.
The quality of data mapping will dictate the data quality to be analyzed for insights into processes such as data integration, data migration, data warehouse automation, data synchronization, automated data extraction, and other data management projects.
This article will help you understand what data mapping is for modern business, its benefits, importance, techniques, processes, and what to look for in a data mapping tool.
What is Data mapping?
The process of matching fields between databases is known as data mapping.
This is the initial step to simplify data transfer, data integration, and other data management tasks.
Data must be homogenized so decision-makers can access it before it can be analyzed for business insights.
These days, there are numerous data sources, and each one can uniquely define similar data points.
For example, a source system’s state field might display Illinois as “Illinois,” but the destination system might save it as “IL.”
When data is moved from a source to a destination, data mapping fills in the gaps between the two systems or models, ensuring that the data is accurate and useful.
Data mapping has long been a widespread business practice.
Still, as data volumes and sources rise, the process has grown more complicated and requires automated technologies to be viable for big data sets.
Importance of data mapping in business intelligence
In data analytics and business intelligence, mapping is an essential initial step towards discovering important insights.
Suppose the data flows into your system untested.
Analysts won’t be able to determine which data sources are unnecessary, whether systems exist without IT oversight (often called “shadow data systems”), or whether some data overemphasizes outliers and anomalies.
Intentional governance can help prevent unmapped data from leading to inaccurate analytics outputs.
Clear pathways and improved procedures for reliable sources of truth can be established by employing data maps to combine sources into a trustworthy database through consolidation strategies.
How data mapping helps in data management
A crucial component of many data management procedures is data mapping.
Data may become corrupted on the way to its destination if it is not mapped correctly.
Quality in data mapping is essential to get the most out of your data in data migrations, integrations, transformations, and warehouse populations.
Data migration
Transferring data as a one-time event from one system to another is called data migration.
This is data that usually stays the same over time.
Following the migration, the source of the data is retired, and the destination is the newly moved data source.
Data mapping facilitates the migration process by mapping source fields to destination fields.
Data integration
The continuous practice of routinely transferring data from one system to another is known as data integration.
The integration might be initiated by an event or scheduled, for example, quarterly or monthly.
Both the source and the destination keep and store data.
Data mappings for integrations connect source fields with destination fields like data migration does.
Data transformation
The process of transforming data from one source to a destination format is known as data transformation.
This can involve aggregating data, enriching the data, removing nulls or duplicates, or doing other modifications to cleanse the data.
Data warehousing
Data is typically pooled in a data warehouse if the objective is to collect it into a single source for analysis or other purposes.
The data used in reports, queries, and analyses is sourced from the warehouse.
The warehouse has already migrated, integrated, and transformed data.
Data mapping ensures that data enters the warehouse and reaches its correct location as intended.
Benefits of Data mapping tool
Among the several benefits of data mapping are the proper integration of data and ensuring its accuracy and consistency.
Advanced cloud-based data mapping and transformation tools can help businesses use their data more effectively without breaking the bank.
Organizations used to record data mappings on paper, which worked well enough back then.
But the setting has grown considerably more complex.
Paper-based systems cannot keep up with increasing data, mappings, and changes.
They are not transparent enough and fail to monitor the unavoidable changes to the data models.
Hand mapping requires hand-coding changes, which are time-consuming and susceptible to mistakes.
Below are some of the benefits and how the correct data mapping can help your organization
Data consistency
The accuracy and completeness of data across systems and databases are referred to as data consistency.
For example, a company has two customer profiles for the same person.
Which of these two profiles should be considered because they contain contradictory information (such as different home locations) that makes the data inconsistent?
Businesses might suffer from various consequences if they attempt to answer that question but cannot do so, including varying customer experiences, miscommunications among teams, and poor decision-making based on inaccurate data.
Organizations can ensure accuracy and synchronization by aligning related data fields across systems using data field mapping.
This also allows organizations to merge duplicate entries or identify incomplete records.
Transparency for analysts and architects
Data analysts and architects require an accurate, real-time picture of the data at its source and destination since data quality is crucial.
Data mapping tools give analysts and architects a standard view of the data structures they are mapping, enabling them to visualize the data content, flow, and transformations.
Data integration
Combining data from various forms and sources into a single, integrated viewpoint is known as data integration.
By defining the relationships between data fields in various systems, data mapping is essential to data integration (i.e., which data fields should populate in its target system when it’s being moved or copied over).
Data Governance
A company’s policies for managing its data, such as defining naming conventions, designating key stakeholders and data stewards, implementing data democratization, and ensuring privacy regulations, are all considered data governance.
It is essential to understand the flow of data within an organization and its current storage location to protect against improper handling of particular data categories, such as personally identifiable information.
Lesser challenges in changing data models
Data mapping is a continuous process.
Maps require maintenance due to changes in data standards, reporting requirements, and systems.
A cloud-based data mapping solution eliminates the possibility of stakeholders misplacing change documents.
Users using effective data mapping tools can monitor the effects of map updates.
Users of data mapping tools can also reuse maps, saving them the trouble of creating new ones every time.
Techniques of Data mapping
Choose the techniques that best fit your objectives and situation and the overall cost of ownership of the analytics platform that will handle the data integration work.
Data mapping requires technical expertise, even though some techniques do most of the work for you.
There are several fully automated and entirely manual data mapping strategies, and each has pros and cons.
Below, we have listed the most common data mapping techniques to help you choose.
Automated data mapping
Specialized software is needed for automated data mapping, which compares new data to your current structure or schema.
These tools typically employ machine learning to regularly track and enhance your data models.
The following are just a few advantages of automating data mapping:
- Obtaining data with ease from thousands or hundreds of sources
- Giving non-technical employees a user-friendly user interface to manage complicated data operations
- Seeing eye-catching graphics depicts your data flow.
- Getting alerts when problems occur
- Investigating problems to make specific repairs
- Although some businesses are reluctant to spend money on this software, the correct data mapping tool may save you a ton of time in meetings, training sessions, and other work-related activities.
Semi-automated data mapping
Schema mapping, another name for semi-automated data mapping, is a hybrid technique that combines fully automated and manual data mapping.
Software that mainly establishes links between many sources and their targets is used by developers.
Someone from your team will manually review the system and make any required adjustments after the process has been mapped.
This is an excellent approach for working with small volumes of data for smaller teams on a tighter budget for simple integrations, migrations, or transformations.
Manual data mapping
A developer who can write code rules to transfer or inject data from one source field to another is needed for manual data mapping.
Given the volume of data that modern businesses have access to, developing a robust data management plan without the help of automated software programs is getting more and more difficult.
Alternatively, when the database isn’t too extensive, manual data mapping works well for one-time tasks (like data warehousing).
What are the steps of data mapping?
1. List every data field that must be mapped.
Identifying which data needs to be transferred or transformed is the first step in the data mapping process.
Unfortunately, there isn’t a recipe that works for everyone.
Everything will rely on the goals you have for your data mapping:
- Integration: Examine your data sources to determine the amount of information that must be combined, the number of sources it comes from, and the frequency of integration.
Large and frequent integrations are a sign that an automated tool is required. One-time tasks requiring little data can be completed by hand.
- Migration: Analyse the source data and determine what will be required at the destination.
Yet again, your technique will depend on how much data you’re working with; the more data involved, the more beneficial automated software will be to the migration.
- Transformation: Determine the format you want for your target destination by looking at your data source.
Most modern changes will require automated tooling. However, smaller tasks might be handled manually.
2. Make naming standards uniform between sources.
Determine the structure and format of the data in each source, then establish a target data format and structure.
3. Establish schema logic and data transformation rules.
How you map your data will play a significant role in this step:
- Automated– Drag-and-drop user interfaces automate the laborious tasks for you. Even non-technical staff members can map out complex data in minutes with a suitable data mapper—no coding knowledge is needed.
- Semi-automated– Connect your data sources to their intended destinations using your program. Next, have a data scientist or skilled developer manually verify these links are operational.
- Manual– Employ a skilled software developer to create schemas or complex codes that connect your data sources to their destinations.
4. Analyze your reasoning
Move a tiny portion of the mapped data, then manually review it for mistakes. This is to ensure that the quality of your data never drops.
Validation is easy using automated data mapping software because these products often come with real-time alerts and built-in verifications.
To ensure your software performs as promised, you should review a small batch of data in your migration, integration, or transformation.
A highly skilled developer or data scientist will be required if you manually map the data to ensure everything functions as it should.
5. Finish the migration, integration, or transformation
After testing your reasoning, you can finish your migration, integration, or transformation.
The final objective you aim for and the tools you use to get there will determine how challenging the process will be.
What should you look for in a data mapping tool?
Cloud-based data mapping software solutions are designed to meet demanding mapping requirements without breaking the bank because they are quick, flexible, and expandable.
There are specific basic requirements to look for, even though the features and capabilities of a data mapping tool vary on the company’s demands.
Broad support for formats
Basic file formats like Excel, delimited text files, XML, JSON, EBCDIC, and others are supported by most tools.
Seek a tool that can work with standard formats in your setting, like DB2, Sybase, Oracle, SQL Server, or other formats.
Enterprise applications like SAP, SAS, Marketo, Microsoft CRM, or SugarCRM, as well as data from cloud services like Salesforce or Database.com, may be handled by a competent mapping tool.
Automated and intuitive
To reduce time, fatigue, and the possibility of human error, an easy-to-use cloud-based application is made to automate repeated operations.
Seek for drag-and-drop features that eliminate the need for coding by enabling users to match fields and apply the built-in transformation rapidly.
Flow and Scheduling
To complete the automation capabilities, seek a tool to build a comprehensive mapping workflow and schedule mapping jobs triggered by events or the calendar.
Conclusion
Data mapping is crucial to maintain data accuracy during data transfer from a source to a destination.
In the data warehouse, high-quality data is provided by effective data mapping.
With an end-to-end data integration and administration solution, you can take advantage of everything the cloud offers and use more data.
