Note: This post was originally published on March 19, 2020, and has been updated with new information.
Information, as they say, is power.
This is especially true for today’s business, where staying competitive requires gaining real insight from data. Not only that, but using that data to make accurate predictions about where your business—and your market—are headed.
Given this fact, it’s worth breaking down the differences between the two primary forms of data insight: business intelligence and advanced analytics.
Business intelligence vs. advanced analytics
In a sentence, business intelligence is data that tells you what has already happened. It answers questions like:
- How many widgets did you sell last quarter in specific regions?
- How are your resources being used internally?
- Is there a capacity for growth in a particular segment of your business?
These answers are certainly valuable for a number of reasons, including the ability to forecast investment of time and resource, measuring success against competitors, and revealing inefficiencies.
Business intelligence is not new, of course, but with the arrival of the cloud it is now possible for enterprises to dig deep into more information—at a cost that is far cheaper than traditional on-premises data storage.
While business intelligence is mainly focused on the past, advanced analytics is all about the future.
Using predictive analytics, organizations can now do things like test theories without risk, anticipate changes in customer behavior, and forecast pricing changes.
The driving force behind all this is the massive amount of data now available to enterprises. Not just structured data from known sources that is the foundation of business intelligence, but streaming data, semi-structured data, and completely unstructured data.
These types of information have traditionally not been of much value to enterprises, especially when it comes to business intelligence, but in volume they can be used to unearth more granular information that drives strategic decisions.
Think of it this way: The more data you have, the more opportunities for employing regression and behavior categorization to help you understand the different forces on your business and, eventually, new revenue streams.
Integrating advanced analytics into your business
Any advanced analytics initiative your enterprise takes on needs to be built upon a solid foundation of business intelligence.
It also needs to include data democratization, which means building out your data landscape in a way that is not top-down. Put another way, your organization needs to move beyond the traditional gatekeeping role IT has for analytics and provide access to a wide range of your teams to run reports.
This freedom to explore and test analytics programs without fighting through a traditional top-down structure not only makes your enterprise more agile, it accelerates how the various segments of your business can put analytics to work making smarter decisions.
Finally, advanced analytics is only as good as the data you have access to, which means it’s critical to use solutions such as data lakes and data warehouses to capture and manage your data. Without these tools, the amount of your data will be so large—and your governance so hard to keep in control—that your advanced analytics projects will likely be rendered ineffective.
To learn more about how you can leverage advanced analytics for your enterprise, read our in-depth guide on how your organization can adopt—and thrive with—advanced analytics capabilities.
Keep up with Redapt
- Enterprise Infrastructure
- Data & Analytics
- Cloud Adoption
- Cloud Native
- Workplace Modernization
- Application Modernization
- Google Cloud Platform (GCP)
- Multi-Cloud Operations
- Tech We Like
- Security & Governance
- Dell EMC
- IoT and Edge
- Business Transformation
- Managed Services
- Microsoft Azure
- Emerging Tech
- Google Resale