The rise of technological advancements in the business realm has led to the rapid generation of voluminous data that is accompanied by increasing complexity. Various aspects of the business required the innovation of multiple tools and applications to aid business processes. With a myriad of these offerings being adopted by businesses, data has come to be vast in nature, but also in silos, residing in disparate sources. Different applications also come with varying methods of data storage, requiring different formats and data structures. As businesses increasingly rely on their data to derive insights into operations or other critical aspects, the ability to parse through the sheer size and complexity of the data is becoming increasingly challenging. Businesses that seek to leverage all their data to its full potential can turn to a process known as data integration.
Data that is left in silos and disparate sources can be challenging to utilize for analysis, especially since it would be a time-consuming process to trawl through the data manually. Additionally, inconsistencies in the data, such as missing values, could lead to inaccurate and misleading insights, that can hold the potential to do more harm than good.
Data integration allows businesses to unify their data from all their data sources into a single source of truth, eliminating the barriers of varying formats and storage systems. With data integration, data from different applications, such as ERPs, CRMs, CMS, GIS, Financial Management Systems, Project Management Systems, etc, can be collectively leveraged and analyzed, bringing forth unified and accurate insights across the enterprise. Data integration does not only hold the capacity to unify all an organization’s data under one roof, but also assist in the augmentation of accuracy and integrity of the data, further leading to enhanced insights.
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The Benefits of Leveraging Integrated Data for Mining Insights
Establishing a unified data source through data integration can assist organizations in boosting efficiency, increasing productivity and deriving trustworthy insights to back important decisions with data.
Increased Data Quality & Trustworthy Insights
Data integration practices can assist in eliminating challenges pertaining to data quality. Issues such as inconsistencies and missing values in the dataset can be identified and eliminated using data integration. This leads to a higher quality dataset which leads to more accurate insights with analysis.
Critical Decisions Backed by Data
Data integration enables all departments within an organization to have access to the same data and ensures that everyone is on the same page. This leads to a data-driven culture across the enterprise and fosters collaboration.
Increased Agility & Productivity
As data integration automates the handling of data from various sources and enabling quick resolutions to underlying problems with data quality, analysis and IT professionals can focus on more strategic initiatives instead of manually trawling through data. This leads to enhanced efficiency and increased productivity across the board.
Augmented Data Governance & Security
With a unified data source in place, data governance policies can be effectively leveraged. When all users have access to the same data source, roles and responsibilities-based access protocols can be put in place, augmenting security and mitigating the chances of data breaches or leaks.
Data Integration: Function Principles
In order to have an effective data integration strategy, the following steps need to be followed –
Identification of Primary Data Sources
The most critical step in the data integration process is selecting all the essential data sources that need to be mapped together. The data sources can range from organization applications and databases to spreadsheets.
Extracting the Required Data from Sources
After all the necessary data sources have been determined, the next step is to extract the data required. This stage involves querying databases, using application export systems of leveraging APIs for data extraction.
Mapping and Standardization
Data that resides in various sources is bound to have multiple formats and structures. In order to unify all the data, it is essential to create a standard format by mapping the data and their relationships to ensure proper context.
Quality Checks and Validation
Before the data can be loaded into the final system it must go through quality checks to ensure correctness. This process is essential to ensure accuracy and reliability of the insights that will be extracted through the analysis of this data.
Transformation and Loading
Once the data has been extracted from the sources and has been mapped and quality checked, it can now be loaded into its final destination. In order to achieve this, the data is cleaned and normalized and then finally it is loaded into the target database and is ready for organizational use.
Data Integration, An Essential Prerequisite for Augmenting Data Analysis
Data integration is an essential step for businesses looking to make the most out of their data resources. Data integration plays a pivotal role in ensuring that organizations’ business intelligence initiatives can benefit from the most up-to-date data from every important source and provide the most granular and meaningful insights into operations and other aspects to deliver a true data-driven culture across the enterprise.
