A Beginner’s Guide to Data Integration Approaches in Business Intelligence
An integrated BI system has a trickle-down effect on all business processes, especially reporting and analytics. Find out how integration can help you leverage the power of BI.
By Tehreem Naeem, Technical Content Strategist
Do you know, a business typically uses 928 apps, in addition to several other on-premise systems to store enterprise data? This means that an organization might need to manage data from various vendors, the majority of which is stored in different source systems and formats.
With such a wide variety of data disseminated throughout the organization, making sense of it is not an easy task. This is where data integration steps in.
In this article, we’ll explain how data integration helps standardize data for BI and reporting. Plus, we’ll look at 4 commonly used data integration approaches that can help fulfil your company’s BI needs.
What’s Data Integration and Why Do You Need It?
Data integration is a process that systematically consolidates data from disparate source systems and transforms it into useful and meaningful information.
So how does it help with BI?
Let’s consider a practical scenario.
To get a holistic view of your customers, you will have to combine data from your CRM system, marketing automation software, customer-facing apps, etc. Performing analysis of this data is impossible without pulling it from its respective sources.
Now imagine if your data engineers or developers were tasked to bring all this data together manually.
Do you think it would be a time-efficient approach? Of course not!
This is where automated data integration can help you.
It allows you to access unified data and generate comprehensive reports without manually logging into several accounts in several locations. You can efficiently retrieve information from several sources, reformat it, and clean it before performing any analysis – and automate this entire cycle for faster processing.
Benefits of Automating Data Integration
In this section, we’ve compiled 4 benefits of automating the integration process for your business.
1. Improves Data Reliability
In today’s world, secure and reliable data is indispensable for improved BI. Using data integration tools, users can create error-free, reliable, and consolidated data sets for data-driven initiatives.
2. Reduces Data Silos
Several incoherent systems or an obsolete application infrastructure can destructively influence your business and upturn expenditures. Data integration tools automate repetitive tasks, which improves the customer experience and business process implementation.
3. Improves Collaboration
For improved business intelligence, collaboration is essential with internal and external resources. This is only possible by unifying disparate sources to leverage the value of insights.
Data integration simplifies collaboration with your team and stakeholders, ultimately improving BI as they are on the same page when it comes to decision-making.
4. Improves Analytics and Business Decision-Making
When all your data is available in one location, you can easily analyze the available info.
Data integration provides access to valuable insights, helping you to become practical, discover opportunities, and identify possible bottlenecks before they even happen. As a result, your business decision-making improves considerably.
Data Integration Approaches Used in Business Intelligence
The following are the four most commonly used data integration approaches in business intelligence:
Data consolidation physically fetches data from numerous isolated systems, creating a unified version of the combined data in one repository. The main objective of consolidation is to reduce the number of data storage systems and consistently present the information.
This is where the ETL process plays a crucial role. It fetches data from source systems, transforms it into a comprehensible format, and then sends it to the destination system, which could be a database or a data warehouse. However, before populating the new system, the data is massaged, filtered, and checked for quality to ensure accuracy.
Another data consolidation approach includes ELT (Extract, Load, and Transform). This process extracts data from the source system, loads it into the destination system, and performs transformations within the database at query runtime. This approach is more efficient for large data volumes as compared to ELT.
In today’s competitive business environment, you need agile access to data that’s arranged in a way that refines business performance and delivers fast, precise, and appropriate data insights.
This is where a data warehousing architecture steps in.
The data consolidated via ETL or ELT approach is loaded into a centralized depository called a data warehouse that is designed in a manner that expedites reporting, business intelligence, and ad-hoc queries.
Data warehousing offers a comprehensive, integrated view of all data assets, with relevant data bundled together.
As data warehousing integrates data in a single place, it becomes easier to identify data patterns and make plans accordingly.
Unlike the ETL/ELT process, data virtualization creates an abstracted layer to present an almost real-time, integrated view of data from diverse source systems. This approach enables you to view data in a unified place; however, the data is not stored at that site.
The main advantage of data virtualization in improving BI is that it is easier to manage and access a virtual structure than a physical structure like a data warehouse.
Plus, it improves speed-to-market as you can construct a solution in lesser time than it takes to build a data warehouse. It eliminates the need to design and construct the data warehouse and the ETL/ELT technology to move data into it. As a result, you can quickly implement business processes, improving your overall performance.
Also, unlike data warehousing, it’s a cost-effective approach as moving the data means additional hardware expenses, supplementary software licenses, extra ETL flows to construct and maintain, more data discrepancies, and higher data governance expenditures.
Technically, data federation is a type of data virtualization as it uses a virtual data store and constructs a shared data model for various data from multiple sources. It brings data together and makes it available from a single point of access. But unlike data virtualization, it enforces strict data models.
Using data federation, you can have a consolidated and up-to-date view of your critical data, such as customer information, across all channels in real-time. As a result, you can better understand your customers and their experiences with your business without necessitating precarious and laborious physical data transfers that may cause quality and replication problems.
It significantly reduces the overhead associated with continuous data capture, conversion, and transfer. You can dramatically simplify BI and other analytics by leveraging information from numerous sources and querying it as a whole.
In this article, we have explained four common data integration approaches that you can use to improve business intelligence. Ideally, your selected tool should support a range of source systems, built-in transformations, and automation features to streamline the process. Each method has pros and cons, so select the one that best fits your use case.
Bio: Tehreem Naeem is a technical content strategist at Astera, a data integration solution provider, where she creates product-focused content. She holds an electronics engineering degree from a reputable institution and has 7+ years of experience in the field.
- Manual Coding or Automated Data Integration – What’s the Best Way to Integrate Your Enterprise Data?
- What’s the Difference Between Data Integration and Data Engineering?
- Would you buy insights from this guy? (How to assess and manage a Data Science vendor)