Organizations need Business Intelligence (BI) to make highly-informed decisions and more recently this has included the use of AI/ML.

However, without access to the majority of your data, you make ill-informed decisions and draw inaccurate conclusions because you’re basing them on half the story.

Most existing solutions involve creating yet another silo which is a fundamental part of the problem. We already have enough data silos. Fortunately, there’s a new way - Data Virtualization. Data Virtualization allows you to query data without having to understand how to deal with the silos in which it resides.

There are some legacy approaches to Data Virtualization, but they can only work with structured data as a source. Existing tools may help to gain access to a variety of data sources but not unstructured data. This means most of the decisions are being made on data from databases and data warehouse that have nice, structured data. At the same time, Gartner estimates that 80% of an organization's data is unstructured data (mostly logs which require learning new query languages).

So it follows that organizations are making decisions based on analyzing only a fraction of their available data.

But is all data equal and what is in all this unstructured data?

I like to think of it like this:

Structured data tells us about the things we know we need to look at. For example, how many of each product we have, how many we sold, who we sold them to etc.

Unstructured data often tells us about the things we wouldn’t usually know about. For example, errors that indicate a poor user experience and that ultimately deterred someone from purchasing. These things simply don’t exist in SQL data, because if someone didn’t purchase, there is no transaction.

Knowing how many of something you sold is great but also knowing why some customers didn’t buy is better.

In large organizations, this data is often being collected by different people. IT Operations may have lots of log data and performance monitoring data with a wealth of information that could inform BI, but Ops staff operate in a different silo and are dealing with unstructured data.

The key is in being able to access both structured and unstructured data, on the same platform. While this level of access was the ultimate goal of Big Data, to date data silos have acted as a big barrier.

One of my customers has a great example of combining unstructured data with structured data to give an informed decision. They have a BI tool they use to analyze their structured data and draw conclusions about their current customers and retention.

At the same time, they had a logging solution that was capturing twitter machine data and calculating a sentiment score for each tweet. This allowed them to figure out their Net Promoter Score (NPS) over time. Once they were able to combine NPS data with sales figures, it gave a whole new dimension to their understanding of why sales fluctuated. It explained some sales behaviors that they previously had no idea how to account for.

The point is that each of these data sources offers another dimension to your story, all the time building a clearer picture of your complete business and moving towards showing you the whole story. So it follows that the more dimensions of data you can add to your story, the more complete the picture.

They could not have done this without Gemini Enterprise because Gemini Enterprise provides a new level of Data Availability for successful BI and AI. Using a cloud-native infrastructure Gemini simplifies the management complexities that come with making data available.

Gemini does this by using Data Virtualization to connect to ALL of your data sources, structured and unstructured, and allowing you to query them in situ. No more data silos!

Learn more by checking out our homepage or take Gemini for a Test Drive at: