What are the benefits and challenges of using cloud-based data integration for BI?

Back in the early days of cloud-based data (early 2000s), there were problems. It was a fledgling technology and many things we take for granted these days (security, connectivity, and so on) were still being developed and standardized. Nearly 20 years on, and things are looking very good for cloud-based data analytics. Sure, there are still aspects of security which require special implementation, but for the most part, cloud-based data analytics is pretty seamless. Most of the tools I've seen in recent years have just about the same capabilities and with small exception seem pretty okay. Unless you're a super-secret Area 51-type org, cloud-based data is a great way to go.

Cloud-based data integration offers several benefits for organizations looking to integrate data from multiple sources for BI. Scalability, cost-effectiveness, flexibility, reduced complexity, and faster time-to-value are some of the benefits of cloud-based data integration. However, there are also challenges that organizations need to be aware of, including security, data governance, connectivity, integration complexity, and vendor lock-in. By understanding the benefits and challenges of cloud-based data integration, organizations can make informed decisions about whether cloud-based data integration is the right solution for their BI needs.

I would guess the majority of current-day cloud-based data implementations acceptably handle security, compliance, encryption, authentication, and authorization for the vast majority of organizations. Sure, there are exceptions, and there are cloud-based tools for those exceptions. While some tools may focus on data quality a bit more than others, I've found that validation, cleansing and standardization are usually better addressed by the people setting up the implementations (and not relying quite so much on automated tools). And, again with small exception, speed and latency doesn't seem to be much of an issue for most organizations.

Data update latency/timing has been my biggest challenge. When combining 5+ data sets my challenge has been trying to manage auto refreshes and making sure that each data source has been updated. If an error occurs during the update, tracking that error down has been a challenge too.

Because of how modern cloud-based data tools work these days, I'll take ELT over ETL any day. Not only does ELT take advantage of what is likely more processing power in the cloud, but it eliminates that extra layer your organization has to administer. Data virtualization is a great technology, but the reality of real-time data integration is that it's not needed nearly as much as it's talked about. A good cloud-based data analytics implementation should easily be able to handle the needs of most organizations (including a lot of large ones).

While I agree with John Poppin that data volumes should not be a major consideration from a technical standpoint, they remain absolutely critical from a cost standpoint. Indeed, many popular ELT offerings can quickly become prohibitive when datasets reach millions of rows. This hardly qualifies as Big Data (billions or trillions of rows), yet charges can easily reach hundreds of thousands of dollars with some solutions that shall not be named.

Where does your data originate? Where does it live? Where is the data going (what reporting software does your organization use)? Look for a cloud-based data solution which ties easily to your data source. (If you're a Salesforce house, look for something which has a native Salesforce connector. The same thing would go for any other data store.) I would say data volume is not a huge consideration these days for most organizations as most cloud-based data solutions can handle fantastic amounts of data. However, integration is definitely a big consideration. Make sure the data can flow easily without a lot of upkeep and administration.

Choosing the best cloud-based data integration platform for your organization requires careful consideration of several factors, including scalability, integration capabilities, ease of use, connectivity, security, data quality, cost, support, compatibility, and vendor reputation. By carefully evaluating these factors, organizations can select a platform that meets their data integration needs and provides the best value for their investment. It is also important to pilot the chosen platform with a small data set to ensure it meets the organization's expectations before fully committing to the platform.