Why Is Data Integration Important?
Data integration unifies and standardizes data from different sources to derive actionable insight. It enables businesses to easily access data and consolidate various formats into consistent, standardized data that Business intelligence (BI) utilizes to base decisions on numerical evidence.
Easier data access and decision-making impact various business aspects, such as sales, vendor acquisition, and customer management. Data integration might pose compatibility, security, and performance challenges, but ultimately enhances the products and services.
How Does Data Integration Help Businesses?
Data integration guarantees a consistent and collaborative approach across departments. It also boosts efficiency and improves data quality.
Below are the top 7 benefits of data integration:
1. Cross-Department Collaboration
Data integration enables real-time data access and sharing across departments. This facilitates data silos breakdown and cross-functional analysis.
2. Time Efficiency
The Preparation of data sets for data analytics and processing is a lengthy procedure. It usually involves data cleansing, conversion, and imports. Typically, the more sources are involved, the longer time it takes.
Data integration automates data exports and allows customization. This enables faster data preparation and analysis. It also ensures data consistency across departments and software platforms. As a result, automated and unified views eliminate the need for manual data collection and boost productivity.
3. Error and Rework Reduction
Incomplete, inconsistent, invalid, and inaccurate data sets often stem from manual data collection. This is especially the case when they are executed from different locations and accounts.
Such compromised data typically causes:
- incorrect reports
- misleading analyses
- overall misjudgment
Data integration solutions synchronize data to prevent reporting rework and guarantee it is precise and up-to-date.
4. More Valuable Data
Data centralization and standardization increases the value of business data over time. They help to identify potential problems and enforce upgrades in data accuracy and quality analysis.
5. 360-degree Customer View
Integrating customer data from various touchpoints such as sales, marketing, and customer service systems, helps to build a 360-degree customer view. Such a comprehensive perspective can be crucial for personalized marketing and targeting customers.
6. Easier Compliance and Risk Management
Data integration also facilitates regulatory compliance. Data consistency, validity, and traceability make compliance management and risk mitigation much more efficient.
7. Enables Digital Transformation
Data transformations are virtually impossible without data integration, as companies utilize centralized data to develop innovative products and services and personalize user experiences.
Data Integration Challenges
Data integration is a process that evolves as the company grows. Efficient data integration is essential for enhancing the customer experience or identifying business areas that require improvement.
There are several prominent data integration issues that businesses frequently face:
Increasing Data Volumes
The increasing size of valuable data vastly complicates data integration. Data storage is indeed affordable, and business analytics tools can handle large datasets. However, managing data quality and integration is the real challenge. This is especially the case when dealing with massive volumes. Joining huge datasets, performing large-scale aggregation and sorting operations, and implementing streamlined techniques hinder logistics and routine integration tasks.
Diverse Data Sources
Data sources typically vary between standard structured datasets and streaming data, social media content, and ecosystem data from various stakeholders. Such variety makes data integration even more challenging.
Typically, traditional data warehouses which are built on relational databases can handle some diversity. However, they lack the tools for data stream integration and unstructured data processing. Well-designed data lakes might handle various data types. It is also true that they can fall short of an SQL database in terms of data integrity and availability.
Challenges of Data Quality
Inaccurate, incomplete, and outdated data poses serious challenges. Indeed, soaring volumes and diverse sources invariably hinder maintaining consistency. This could result in decisions based on compromised or duplicate data.
That is why, to address potential issues during integration, technical teams must employ techniques including:
- data validation
- data cleansing
- data deduplication
- data normalization
These are common techniques used to to address potential issues during integration.
Data Security and Privacy Challenges
Data security and privacy necessitate detailed consideration of regulatory compliance, access controls, and encryption. Data transfers could cause unauthorized access and breaches. This in turn can lead to formidable commercial losses. Therefore, data masking techniques, establishing clear data ownership frameworks, and compliance with data protection regulations are critical.
Creating Data Warehouses and Data Lakes
Data warehouses aggregate disparate data sources such as:
- structured data files
- unstructured documents
- streaming data sources
- transaction logs
- social media
They help BI to analyze sizeable datasets, query data autonomously, and improve consistency and quality.
While data warehouses are pivotal for informed decision-making and data fragmentation reduction, they also pose challenges. These include data loading, synchronization with external applications, and additional data wrangling process management. Nevertheless, improving data accessibility, integrity, and analytical capabilities would be impossible without them.
Companies utilize data lakes to consolidate and analyze sizeable datasets from various sources. They also use them to break down barriers between disparate data sources. This approach enables businesses to deepen their understanding of customers by gaining access to a unified and holistic perspective. This in turn results in informed decision-making and personalized experiences.
Solutions like Salesforce’s MuleSoft solve such data integration challenges by releasing new data warehouse connectors for their Anypoint Platform. These connectors simplify the integration and management of data.
They are designed for data warehouses like:
MuleSoft simplifies data warehouse operations by leveraging these connectors to enhance efficiency and connectivity when dealing with diverse data sources.
Benefits of Business Intelligence
The growing number of devices and unstructured sources exponentially increases big data. This in turn, naturally reshapes the business landscape.
BI treats data as an indispensable asset for sustainability and growth, you can find the top benefits of adopting BI below:
1. Strategic Decision-Making
BI extracts actionable insights that finetune decision-making and eliminate guesswork.
2. Competitive Advantage
Investing less time and effort in acquiring and integrating new data accelerates time-to-market and gives a crucial advantage over the competition.
3. Boost ROI
Meaningful insight helps to adequately allocate resources, increase return on investment, and meet goals.
4. Better Visibility
Streamlined processes and data sharing across functional areas vastly improve financial and operational data visibility.
5. Enhanced Reporting and Analysis
Rapid and error-free reporting and analysis help companies replace guesswork with decision-making based on numerical evidence.
6. Target prospects
BI utilizes data insights to comprehend customer behavior and trends or tailor products and services to target prospects.
7. Business operations monitoring
Real-time data analysis and issue identification facilitate insight generation. This way, one can immediately address inefficiencies and boost operational efficiency.
8. Data-driven automation
Predefined algorithms automatically trigger business processes based on user data. This enables companies to address customer requirements and personalize user experience.
ETL, ELT and Data Integration
Gathering the business intelligence necessary for growth takes time, effort, and the right data analytics tools. Many data reporting tools, however, lack the capacity to store all of the data from various analyses. You frequently end up with incomplete or inaccurate data, which can disrupt business operations and reduce revenue.
That is why businesses utilize BI tools powered by:
- ETL (Extract, Transform and Load) or
- ELT (Extract, Load and Transform)
ETL and ELT are two common data integration strategies. ETL transforms data on a separate processing server before transferring it to the data warehouse.
ELT, on the other hand, performs data transformations directly within the data warehouse. Unlike ETL, ELT enables direct raw data transfer to the data warehouse without staging processes.
The ETL and ELT data integration methods focus on data transfer between locations. Despite sharing the same primary goal, each method has unique characteristics and aligns with specific data requirements. The crucial difference is in the order of data transformation. ETL transforms data before loading it onto the server, whereas ELT transforms data afterward.
As an older method, ETL is suitable for complex transformations of smaller data sets. It is also particularly beneficial for those who prioritize data security. Conversely, ELT is a more recent technology that enhances flexibility and is ideal for processing both structured and unstructured data.
The choice between ETL and ELT significantly impacts data storage, analysis, and processing. As the IT landscape shifts to the cloud, insufficient visibility into the internals of cloud databases and applications frequently makes ETL-based integration impossible to implement. When data integration occurs after the fact – typically in interday or intraday batches – the results of this BI data integration are likely to be outdated.
That is why the transition to cloud computing requires a shift from traditional ETL tools, which are inherently batch-oriented. The need for real-time updates, particularly in a globally dispersed workforce, has prompted organizations to seek modern solutions. MuleSoft’s Anypoint Platform emerges as a flexible solution, supporting real-time and batch API integration. Anypoint, unlike ETL tools, isolates applications and databases. It reduces dependencies and facilitates a continuous flow of up-to-date information.
The platform is perfect for:
- fraud detection
- recommendation engines
- network monitoring
MuleSoft Anypoint addresses the issues raised by ETL approaches that often create a chaotic collection of legacy and modern systems. Its flexibility meets the needs of businesses with massive product catalogs for their fast-growing eCommerce presence.
MuleSoft Anypoint provides a repository of pre-built connectors for various protocols, databases, and applications named Anypoint Exchange. These connectors facilitate connectivity, so developers can turn their attention to actual data transformation instead of custom coding. They also grant instant access to BI data across applications.
The MuleSoft integration solutions are also ideal for data cleanup, fine-tuning, and unifying the view of the company’s operations. Businesses can leverage these to capitalize on data and perform complex integrations. Last but not least, such solutions inevitably accelerate time-to-market
Business Intelligence and analytics can only give a competitive edge when utilizing the full potential of the data. This highlights just how crucial data integration and effortless access to all datasets is to any business.
MuleSoft Anypoint can be particularly useful in gathering data from multiple disparate sources and preparing it for data warehouse analysis. Scalefocus has formidable MuleSoft expertise, and our MuleSoft teams have assisted numerous enterprises with their data integration challenges.
You can read a full case study to check how our MuleSoft experts helped a European certification leader increase productivity by 30 per cent.