Introduction
Data integration combines data into one unified view for more practical use by the business. The process starts with data ingestion, cleansing, mapping, and transformation, and moving into storage with the goal of complete integration across all apps, systems, and locations to boost data quality and reliability. Extract, transform, and load (ETL), in particular, is an infamous strategy for copying data from heterogeneous sources to a destination source.
With data integration, you can access the data gathered in one system in another. For example, you might collect customer data in your CRM, which nobody outside your sales and marketing can access. This leads to data being shared via spreadsheets, emails, and phone calls.
With data integration, information is shared seamlessly between systems. For example, staff can access ERP data in your CRM system and vice-versa. As a result, mistakes are eliminated, everyone in your business has the data they need to perform at their best, and you get the best possible value out of the systems you already have.
Integration is essential to data pipeline design, from ingestion and processing to transformation and storage. It involves stitching together different subsystems to create a more extensive, comprehensive, and standardized system between multiple teams. Data integration ensures that all of an organization’s data is readily available to all the necessary procedures and services, bringing many benefits.
This data cannot be merged and analyzed in raw form. Instead, companies need sophisticated platforms and industry domain knowledge of therapeutic areas, ontologies, and methodologies for defining data attributes to combine, clean, format, and link them to create a 360 view. This requires several tools, capabilities, and expertise of a skilled data manager.
Data Integration Process
The Data Integration process may have below steps:
- Extract, Convert and Load: Dataset copies are compiled, harmonized and loaded into a data warehouse or archive from various sources.
- Extract, install and transform: data is loaded into a big data system and converted for specific analytics use at a later time.
- Change Data Capture: identifies in real-time information changes in databases and applies them to a data warehouse or other repository
- Data replication: data is replicated to other databases in one database to keep the information synchronized for operational and backup uses.
Data Integration strategy
The data integration journey begins with a strategy that includes both the technical skills required to build and support the environment, along with a data framework that defines what data assets the company wants to integrate, what types of data users need, and any format or structural requirements that must be applied to the data. They must also understand the data’s structure, delivery, and flow. For example, in some cases, data assets like electronic medical records and insurance claims will be updated monthly or weekly; others, like Google Analytics or wearable device readings from clinical study patents, will update in near real-time.
When companies choose data integration platforms and tools, they should look for a technology layer that can handle the technical and business process flows across the spectrum of disparate data sources and seamlessly adapt to changes in these process flows. Establishing this foundation will help companies choose the best tools for data integration and analytics and ensure they deliver measurable value to the organization.
Managing Reference data
Reference data is the backbone of how companies manage data around customers, products, organizations, peers, and patients. Most data assets will have the same entities within them, but they will look and feel different due to variations in data collection strategies. For example, the same healthcare provider might be referenced in a specialty pharmacy database, medical claims records, and your CRM system.
MDM [Master Data Management]
For companies to harmonize entities across different sources, they need to be able to sort which entities are the same person and which are other people with similar names, addresses, etc. Companies need Master Data Management (MDM) and reference data management solutions to provide this clarity, delivering a concise and accurate view of the entity.
The MDM system enables users to connect the dots, tying all transactional data into the organization via every channel. That includes results of sales calls, medical information requests, media and events, and other customer and clinical touchpoints.
Business rule management systems.
Companies then need to formulate business rules to generate insights from this data. This includes a territory alignment engine (for commercial use cases), using conversation algorithms and natural language processing, and deidentification systems to transform raw data into meaningful data that can be utilized for analytics while maintaining data privacy. These rules need to be defined by data consumers since they know how the data will need to be used for clinical and commercial applications.
Data Integration Benefits
Every scaling business must be prepared to work with large and complex data sets. And failing to organize this data can prove beneficial. Business applications depend on data every day across departments. Order invoices, product inventory, and supply chain logistics are some examples where you might recognize data’s integrality.
Advanced analytics and refined management processes are only a few benefits that successful data integration can elicit. But it should be clear why data integration is paramount. So let’s discuss a few of the main advantages of having Data Integrated:
Better Decision-Making:
Data integration can help make smarter business decisions by providing a more comprehensive view of your business or organization. This can help you identify trends, patterns, and issues that may need to be apparent when looking at data from a single source.
Data integration can also help you better use your resources by allowing you to focus on high-value activities rather than spending time and resources on manual data entry and management. This can help you make your decision-making more efficient and effective, as you have more time and energy to analyze and interpret the data.
Centralized access to data
Data integration can provide a centralized view of data from multiple sources, making it easier for people to access and share data. This can help to improve collaboration by enabling people to access the data they need more easily without having to request it from multiple sources. By integrating data from numerous sources and standardizing it, data integration can help to improve the quality of the data, making it more reliable and accurate. This can help to support better collaboration by providing a foundation of high-quality data that can be used to inform and guide teamwork and decision-making.
Cost Reduction
By integrating data from different sources, organizations can streamline their processes and eliminate the need for manual data entry and reconciliation. This can lead to significant time and cost savings. Manual processes are often time-consuming, expensive, and prone to errors, so using automated data integration can help reduce costs by eliminating these workflows.
Integrating data from different systems can help reduce the need for custom integrations, which can be time-consuming and expensive to develop and maintain.
Better Personalization
Integrating data from different sources, you understand your customers’ overall perspectives and preferences. This can help you tailor your products, services, and marketing efforts to meet their needs and expectations better, which can lead to a more personalized and satisfying customer experience.
Better problem resolution
When data from different sources is integrated, it can be easier to identify and resolve customer issues and complaints. For example, by integrating data from various sources such as electronic medical records, insurance claims, and wearable devices, healthcare providers can have a more comprehensive view of a patient’s health history and condition. This can help with diagnosis and treatment planning and identify potential risk factors for future health issues.
Greater convenience
Data integration can help you streamline processes and reduce the time and effort customers need to spend interacting with your business. For example, if you’re a bank, integrating data from different systems can help you provide a more seamless and convenient experience for customers who want to open new accounts, apply for loans, or access their account information online.
Improved data quality
By integrating data from multiple sources and standardizing it, data integration can help to improve the quality of the data, making it more reliable and accurate. This can support innovation by providing a foundation of high-quality data that can be used to inform and guide new ideas and approaches.
Improved Security
Organizations can access all the data in a centralized repository by integrating data from different sources. This can help them identify potential security threats more efficiently and take appropriate action to mitigate them. Also, by integrating data from various sources, organizations can understand the risks they face and the likelihood of those risks occurring. This can help them prioritize their efforts and allocate resources more effectively to address the most significant threats.
Conclusion
Above, we have discussed various benefits of data integration; we understand that Data Integration offers a range of benefits for businesses across multiple industries. By combining data from various sources and systems, organizations can gain a more complete and accurate understanding of their operations, customers, and markets. This can help improve decision-making, increase efficiency, and drive growth. Data integration can also help businesses better understand and meet the needs of their customers, leading to improved customer satisfaction and loyalty. Data integration is a valuable tool that can help organizations leverage their data to drive success.
After deciding on your data integration strategy, you can opt for the data integration technology that is economical and efficient for you. Building new connections from scratch might be a practical choice if you only handle a handful of data sources. However, if you need to replicate data every few hours from a sea of sources and perform multiple transformations, you can use an automated ETL tool.
