Data Validation & Data Integration: Difference & Importance

Data validation and data integration are essential components of any data handling activity, whether you work in data analysis, data collection, or preparing to present the analysed or collected data to stakeholders. We share data daily, and its accuracy is paramount in today’s IoT and big data world. Accurate data ensures that corporations arrive at viable data-driven solutions to help their businesses succeed. This article will address data validation and data integration differences and importance. But what do both mean?

Data Validation and Data Integration Difference and Importance

What is Data Validation?

It is the process of ensuring the quality and accuracy of data. Data validation is implemented by building various checks into a report or system to ensure a logical consistency of both input and stored data. These tests may be format, uniqueness, consistency, data type, code, and range checks. If the data is not accurate and consistent from the beginning, the results and subsequent decisions will not be accurate.

Therefore, you must validate and verify the data before using it.

While data validation is vital in data workflow, most people skip it. Although it might seem like a step that slows down your work’s pace, it is essential as it helps you in creating the best possible results. With data integration platforms that can automate and incorporate the data validation process, validating data nowadays is a quicker process than you may think.

Importance of Data Validation

Prevents Data Loss

Getting multiple data feeds from various data sources can result in changes in metadata and subsequent data loss. If there is a list of data formats or systems and the variations between data sources, you can prevent data loss through data validation.

Enhances the Trustworthiness of Data

Missing data, contradictory data, or duplicates may result in a variable interpretation of the data landscape. If the data’s trustworthiness is questioned, data analysis becomes less relevant or flawed. This may result in dire operational consequences. If you miss data from a source or the source is incorrect, everything you do after that will always be flawed.

It Is Critical in Decision Making and Downstream Data Analysis

In today’s world, businesses rely on big data for predictive purposes, gain business intelligence insights, and for decision making. If you have a good data source and quality data, then the decisions you will make afterward will be sound. With quality data, analytics can be applied to get results that you can use for sound decision-making.

What Is Data Integration?

Data integration refers to merging data from various sources into a unified, single view. Data integration starts with ingestion and includes steps like data cleaning, ETL mapping, and transformation. Ultimately, data integration helps the analytics tools produce actionable and effective business intelligence.

The ability to use and share data is plagued with interoperability problems. With data integration, you can alleviate this issue. Many organisations and businesses use various tools to manage their data, inevitably meaning various data formats exist in a single working entity. Data integration combines multiple data types and formats in a single location called a data warehouse. Data integration aims to generate usable and valuable information to help solve problems and gain new insights.

Importance of Data integration

Improves Unification and Collaboration of Systems

Employees in each department and sometimes in separate physical locations need to access the company’s data for individual or shared processes. There is a need for a secure solution to deliver data through self-service across the entire business.

Furthermore, the employees in almost all departments generate and improve the data needed by the rest of the business. There is a unification and improved collaboration throughout the enterprise with data integration.

Reduces Errors and Rework

There is a lot to keep up with when it comes to the company’s data resources. Before beginning any activity, workers must know the account or location they need to explore in manual data collection and have all the required software. They also have to ensure that their data sets are accurate and complete.

With data integration, there are automated updates, and you can run the results swiftly in real-time whenever you need them.

Delivers valuable data

Data integration solutions can improve the quality of an organisation’s data over time. Because data is integrated into a central system, the quality issues are quickly identified, and the necessary enhancements are implemented. This results in more accurate data.

Boosts Efficiency and Saves Time

Whenever a company institutes measures for proper data integration, it significantly cuts down on data analysis and preparation time. Automation of the unified views reduces the need to collect the data manually, and the employees don’t need to create connections from scratch when they want to build an application or run a report. Using the right tools instead of hand-coding the data integration saves more time and resources to the dev team.

Difference Between Data Validation and Data Integration

Although data integration and validation are vital in data processing, they are two very different processes, though interdependent. Below are their main differences:

  1. Data integration refers to the movement or transfer of data from disparate sources into a unified, single target, commonly called a data warehouse. On the other hand, data validation is how data is cleansed to ensure correctness and quality.
  2. Data integration is used when replacing or upgrading the existing system, while data validation happens when collecting or gathering the data.
  3. Data validation happens on the original copies of data while data integration is carried out on verified and validated data. Therefore, the data used in integration has already undergone various processes, unlike data validation.
  4. Data integration is a more complex process, and the complexity may increase depending on the number of platforms involved, the size of a business, and the amount of data. Conversely, data validation depends on the data entry points in an organisation and may involve a human-computer interaction while integration can be fully automated.
  5. Data validation checks whether data falls under the acceptable range of values while data integration combines various data sources.

Take Away

With Big data taking the business world by a storm, knowing various concepts in data processing is vital. Businesses collect data from disparate sources in their daily operations. Since that data may have different formats, data validation is critical. Data integrations allow timely sharing of consistent data that the employees can use in daily operations and the upper management for predictive business analytics, business intelligence (BI), and decision making.

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