Business intelligence is among the most innovative technological solutions of our era.
It converts raw, previously useless data into valuable, analytics-ready insights for decision-making and enterprise planning.
It’s thus no surprise that the global BI market is set to attain a global market value of $42.95 billion by 2028.
However, note that, although BI tools boast a full range of advanced analytical features, the accuracy of generated insights is only as good as the data used.
Because of this, effective data integration is a requirement for any BI framework. Discover more about data integration in business intelligence below.
What is Data Integration and Why is it Important in Business Intelligence?
Data integration is the lifeline of any successful data management and business intelligence strategy. It refers to the processes and architectural frameworks that facilitate data aggregation from multiple sources.
The goal is to create a single, unified dataset, thus allowing enterprises to leverage all available data types. Data integration is critical to BI strategies because:
It Eliminates Data Silos
Enterprises today use and generate data from operational software, customer-facing applications, IoT devices, websites, and even on-premise systems.
On the one hand, this is great because it means more data for BI. On the other hand, it creates data silos with heterogeneous data formats scattered across multiple departments. Silos can potentially pile up and become unmanageable to handle, let alone be utilized in BI.
Data integrity helps bring some sanity to the chaos.
It employs a process known as ETL (Extract, Transform, Load) to aggregate data from different data sources and remodels it into a format suitable for data warehousing. This, in turn, makes data access and retrieval for BI a breeze.
Data Integrity Simplifies Business Intelligence
Data integrity also scales BI.
How? Data comes in varying formats and from different sources so it may have inconsistencies and errors imperceptible to a regular user. Fortunately, DI platforms often feature tools to clean and purge these issues, making the data more accurate and reliable for business intelligence processes.
Even better, some DI systems use ELT instead of ETL. This means that, after extraction, data is directly loaded into BI warehouses first before being transformed into the required format.
This enables them to extract and load data from multiple data sources into BI warehouses in real-time. Consequently, enterprises can analyze real-time data and respond promptly to time-sensitive business issues. Add in that DI platforms display data in a single unified view, and BI processes become incredibly easy.
Even in businesses, the data collected comes from different sources. By unifying data from the different sources a company may have and creating one unified view, data integrity breaks down the walls separating data from different sources. This creates simpler access and allows seamless collaboration between staff from different departments.
For example, whenever there’s a revenue dip, sales and marketing teams and management may collaborate on their data to identify what’s not working. Previously, this would be challenging and time-consuming as it would mean manually collecting and combining data from these departments before analyzing it for similar data points.
Reduction of Human Errors
There are so many ways human errors might negatively impact data integrity. For instance, employees involved in data collection might forget data from individual yet important sources, diminishing its wholeness. Alternatively, they may accidentally add or delete some information, interfering with accuracy and consistency.
Data integration prevents such instances and other human-related errors. It automates integration from multiple data sources, reducing human capital involvement in integration processes and reducing errors.
Data Integration Approaches in Business Intelligence
There’s no one-size-fits-all approach to data integration. Just like data is varied, so is data integration, meaning there’s no one-size-fits-all approach. However, there are five main approaches an organization can take:
1. Manual Data Integration
Manual data integration is the most basic of data integration approaches. As the name implies, a data manager manually completes the entire integration process. They write the code, aggregate disparate data sources, and clean up the data before storing it for queries.
It significantly cuts data integration, deployment, and maintenance costs and allows enterprises more freedom over integration processes. They, however, have to give up scalability and data accessibility, not to mention that there’s increased room for human error. Manual data integration is best applicable for enterprises with a few data sources.
2. Middleware Data Integration
Middleware software acts as a bridge between different applications, other software, tools, and databases. In data integration, a middleware connects data sources with a database. It also cleans and validates the data before storage.
This allows users to move data from heterogeneous data sources into a single store where it can be easily accessed to run queries and other BI processes. It also enables the integration of new and legacy data systems since middleware can format and update data into usable formats.
On the downside, only professional developers can implement and maintain middleware data integration systems. This increases payroll costs and reduces access to data for the rest of the team. Middleware data integration is best for organizations looking to integrate legacy and modernized business systems.
3. Application-Based Data Integration
The software doesn’t act as the middleman in an application-based data integration approach. It instead does all the work, from locating data sources to cleaning and integrating data into determined storage systems.
Thus, only one piece of software does all the heavy lifting, significantly simplifying the integration process. This has the ripple effect of allowing departments to share data and also reduces integration costs seamlessly.
On the other hand, implementing a software system that can locate, validate, and integrate data across different departments might prove tricky and expensive. And even if you achieve it, your organization will need a data manager for maintenance.
4. Uniform Access Integration
In uniform access integration, data from heterogeneous sources is presented uniformly but simultaneously stored in its original location. This lowers storage costs since there’s no need for a centralized data storage solution.
It also simplifies data access by providing a unified data view and allowing every department to access its data using the usual steps.
The catch? Leaving data at its source creates data integrity risks. Additionally, some data sources aren’t powerful enough to facilitate frequent data retrieval for BI. An example of uniform access integration is the data federation technique.
5. Common Storage Integration
This approach refines uniform storage integration. It works similarly, but instead of leaving data at the source, it creates and stores a copy in a central repository. This promotes cleaner data, seamless data access across departments, and, more importantly, simplifies data retrieval for BI analytics.
Common storage integration is the way to go for organizations looking to use data for sophisticated data analytics processes like BI.
Data Integration Techniques
Now that you know the different approaches your organization can take, here are common integration techniques that businesses employ today:
Data warehousing is the most common data integration technique in business intelligence. This, however, comes as no surprise since data warehouses are often created solely to support BI activities.
In that light, data warehousing involves collecting and integrating data from disparate sources and storing them in data warehouses for simplified data management and BI analytics.
Data federation is a far more advanced integration technique quickly gaining traction. Here’s how it works- a data federation software aggregates data from multiple sources, from cloud databases to warehouse extensions, into one single format and stores it in a unified, virtual database.
It essentially uses the typical data integration framework, but instead of creating a copy of the data, it integrates it virtually, thus eliminating the need for a separate repository. This also creates a single source of data for BI analytics, promotes fast data access, and minimizes data loss risks.
In data propagation, integrated source data from a central access warehouse is often transferred to data marts after the data integration process is complete. Any changes to data in the central access warehouse are continually propagated to the data marts, ensuring data uniformity. Automatic updates to the data in data marts also simplify data management as it means data managers no longer need to move volumes of data back and forth manually.
Other common DI techniques include:
- Data orchestration
- Data consolidation
- Hybrid data integration
Implementing Data Integration to Enhance Business Intelligence
There are two ways to go about data integration in business intelligence. An enterprise can develop a custom data integration BI framework. This allows enterprises to customize their framework exactly as they need, but the process is expensive and time-consuming.
The other option is to invest in ready data integration software for BI frameworks on the market. These solutions often come with a full suite of advanced analytics (from statistical to predictive) and may even include automated reporting tools. Here are some important factors to keep in mind while selecting an enterprise-ready data integration solution for BI:
- Built-in connectors to extract data from different sources, transform it into relevant formats and load it into the destination system
- Customization options
Employ DI, Simplify BI
Business intelligence has no doubt transformed the corporate landscape. By using data, enterprise executives can make more strategic and informed decisions, while small and medium-sized businesses have a competitive advantage.
But to achieve this, they must integrate the massive data volumes they accumulate daily. Data integration lets them achieve this feat.