The New Dawn of Data
Big data of today has made all of us rethink our very narrow view of data management, especially where business intelligence and data warehousing are concerned. We’re accustomed to thinking of data as records that are all the same type and form, but that’s not really the case anymore. The limitations of analytical engines in turn limited what we consider data to be. Even in the early days of Hadoop, a lot of projects dealt with data that could fit in a database. Not anymore.
5 Points That Demonstrate How Modern Data Sources Have Morphed
1. BI doesn’t require support from IT.
Five to ten years ago, popular BI tools were reliant on IT. PC Magazine explained, legacy tools that were once popular required specialists to operate, and oftentimes to interpret the data itself, as well as generate intelligence conclusions. This created a barrier between the individuals who were actually seeking business intelligence and those who were tasked with collecting, processing, and interpreting it.
That’s not the case today. Modern BI practices enable end users to connect to any data source and collect, process, and interpret data themselves—with the help of the right solution, of course. Embedding visualizations inside other applications has freed idea from the tedious demands of BI and empowered end users to carry out their investigations independently.
2. Data isn’t just records that are a uniform type and form.
Our view of data management was once pretty narrow. People got used to thinking of data as records that are presented in the same way, of the same type and form. Today, more data exists outside of databases than inside them. This means we have to make considerations for free form text, which can be stored however developers want to store it, plus they don’t have to figure out schema ahead of time.
3. The information and questions people are asking today require data that can’t be found in a legacy structure or database.
Early BI projects almost always involved data that could fit into a database easily—website clicks, for instance. But we started to get spoiled as technology involved and started asking more complicated questions. It wasn’t just about clicks anymore. It became reasons for clicks. What color was the link? Were images involved? Etc. Those aren’t things people coded for using legacy BI tools.
4. The age of “everything is data” descended upon us.
Whether or not you realize it, a large percentage of activities you carry out on a day-to-day basis are generating some kind of data. Shopping on Amazon, using your Fitbit, and even opening your refrigerator, in some cases, hence the expression “everything is data.”
It is now necessary to start creating code that can format more complicated questions. BI specialists have started using tactics like inserting data into columns in HBase rather than relational databases to bypass the discrimination of numbers, characters, data strings and dates. As people come up with new questions, it has become possible to ask more multifaceted business intelligence questions.
5. Working with images became a thing.
Yet another layer of complexity. It’s hard to think of images as data, but they absolutely are, especially with the proliferation of AI and automation. When businesses start getting curious about how certain images influence sales, whatever the product or context, variety, rate of changes, and dealing with information that isn’t as simple as a word all become challenges.
The BI world has responded to this problem by enabling the process of analyzing images programmatically. After all, an image is just a data structure. Even if you have thousands of images, you can write code that extracts characteristics of an image and surface it as a field (whether in batch or by storing it).
What This Means for You
Today, we’ve entered a big data renaissance, using analytics to strip information out of things that aren’t naturally data, and it’s getting more complicated (and awesome) all of the time. So, what does this mean for you? That depends on your BI goals and what kinds of data you’re analyzing.
Want to know more about how modern data sources have changed? Good, because we wrote you an eBook, and you’re going to love it.