When it comes to business intelligence integration for the needs of growing businesses, or BI integration, there are some challenges. For instance, business owners generally have access only to fragmented and heterogeneous data sources, a lack of system consistency, and data silos. Meanwhile, their choices among data integration strategies can be overwhelming.
In this article, we share a BI consulting insight to help you understand what is suitable for your specific data sets and operational conditions. We also spell out the pros and cons to help you make better decisions.
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Export, Transform, Load (ETL)
ETL is the most popular and commonly applied BI integration method. Its primary focus is enterprise-wide data synchronization. This synchronization makes the data usable and accessible. This is crucial if the business is preparing for BI implementation. In the long run, ETL makes extracting valuable insights with BI tools easier.
During the “extract, transform, load” process, all existing data from multiple unrelated sources is gathered and then moved to staging. This is where cleansing, aggregation, and reconciliation happen, transforming data into a comprehensible ready-to-use relational format. Later it arrives at its final data destination—a new database or a repository.
- The technology is well-developed and researched. Moreover, there are plenty of tools and specialists available.
- ETL works best for consolidating smaller amounts of data that require minimum transformations and also have a determined purpose, such as business analysis.
- ETL is easier to operate in terms of GDPR and similar regulations. That is, you can remove and protect sensitive information before loading it into the final database.
- Only the data you marked as necessary stays in the “load” phase.
- ETL is predictable. However, it demands maintenance. What’s more, it can have high hardware requirements for data transformation. On the other hand, modern ELT is capable of performing the same operations in the cloud.
- It is time-consuming and requires planning. Traditional ETL works better when it’s based on batches. But it’s not ideal when it is used on demand or in real time.
- The information is not available until the transformation is fully complete, which might take more time depending on data size.
- Data that doesn’t make the cut into the “transform” step remains unavailable.
Export, Load, Transform (ELT)
The difference between ETL and its closest relative, ELT, is the order of the steps. In other words, the target database works on the data transformation after it’s loaded, not before. Also, there is no staging step. The loading starts immediately after the data-extraction step.
However, ELT is a good fit if the volume of data is enormous and unstructured, or the source and the target databases are similar. In the ELT process, everything is moved to the destination, usually by way of a high-speed cloud-based server with almost unlimited storage. In this way, you won’t lose a single detail of information. When you need to look up a specific item, ELT quickly transforms it for you on demand.
- ELT is a suitable solution for so-called data lakes, huge data pools whose purpose the business has yet to discover.
- This method offers both speed and flexibility. That is, the data loads immediately for users to analyze on demand, thereby supporting real-time analytics.
- It is less time-consuming than ETL, since data loads instantly, raw and unstructured.
- ELT requires little maintenance since data is always available. Moreover, the hardware requirements are not demanding or specific. ELT processes everything using existing resources in a cloud environment.
- ELT is less developed than ETL. While ELT requires less installing and configuring efforts, the technology is not very elegant.
- ELT could violate compliance regulations since it requires loading of unfiltered data. Also, you have to consider the cloud server’s location if it’s outside your country.
- ELT does not work well with relational databases, even though it can in certain data warehousing solutions.
Other Data Integration Techniques
Enterprise Information Integration (EII)
EII is a complementary approach to both ETL/ELT. For example, EII enables multiple stand-alone applications to access the same data in a single database.
Also, it merges data from a few different sources like web services, social media, or other data streams on demand. Suitable for huge enterprises with complex architectures, EII generates a unified current view for monitoring requested parameters that are picked up from different applications.
EII speeds up the process of retrieving data from multiple sources, and it fixes inconsistencies in formats and semantics. Therefore, it allows businesses to use real-time data in reporting and analysis. However, EII doesn’t come without disadvantages. For example, the data doesn’t undergo any standardization, leaving room for data type mismatches.
Enterprise Application Integration (EAI)
EAI connects a set of applications used in an enterprise, allowing them to communicate and exchange data using a unified interface. It is useful when there is a huge volume of operational transactions running in real time, as in fulfillment and supply chain management.
EAI offers great flexibility and improves connectivity. Thus, it can respond to growing customer expectations and new opportunities in a timely manner. This is because the applications are free to talk to each other even without knowing the location and format of data.
Unfortunately, due to the heavier load, EAI is prone to performance bottlenecks. But the resulting overall efficiency boost is often worth it.
Enterprise Data Replication (EDR)
EDR is basically a process of moving a dataset from one database or storage to another one with a similar environment. The main difference from other ways of integrating data is that it neither performs nor requires any data transformation. At the same time, the replica is continuously updated and synchronized with the source. Depending on the business needs, this may be in real time or on schedule.
With EDR done right, your data always stays available in case of a hardware meltdown. Remote teams can access information faster and more comfortably. The biggest challenge EDR faces is latency or service interruptions during data transfer. These are often caused by the distance between the source and the “mirror” database.
The Bottom Line of BI Integration
Each data integration strategy carries its own opportunities and restrictions. To reap the rewards of successful BI integration, you need to set your goals regarding your data first.
Making the right choice depends on how your business operates now, and what are you going to do with the information you have. For example, you might use it to implement a BI solution, increase the overall productivity of the enterprise, or ensure business process continuity.
To learn more about technologies that are important for your business, be sure to check out our blog.