Thanks to digitization and Big Data, unorganized and impossible-to-search-through manila folders and filing cabinets should be ancient history. Yet, this information revolution has produced a double-edged sword – the scope and scale of digital information has made it more difficult to comb through data to find real, valuable insights about customers.
When Big Data grows too big, it's unable to fulfill its potential as "the ultimate research assistant," as some have suggested. To attempt to control all this data, businesses have built two different management models – relational databases and knowledge graphs.
Businesses need to better understand the context around, and the relationships between, points of information. Historically, they've relied on relational databases, the filing cabinet of the database world, but knowledge graphs are actually better suited to make these connections. In Part I, we explored how knowledge graphs help companies understand their customers on a deeper level, and provide them with a comfortable customer journey. Now we’ll take a look at how knowledge graphs provide visibility into Big Data.
Relational Databases: No 'Silver Bullet'
It's true that relational databases make Big Data seem smaller and more manageable. That's why they were originally conceived as a "silver bullet" solution that would help businesses become more customer focused. The problem is that relational databases silo information, which prevents “big picture” perspectives from coming into focus.
Put another way, if you're searching for information, a relational database will likely point you to the right answer – but it isn't very adept at providing unexpected insight, which is an important use of Big Data. Additionally, these databases are rigid and slow to change – extracting meaningful information from them can feel like trying to turn around an aircraft carrier. It's a process that requires time, preparation and some expertise – rare commodities for many businesses.
Knowledge Graphs Unlock Customer Data
Knowledge graphs, unlike relational databases, are welcoming to data exploration, because they are built on the connections between information. They allow searchers to ask broad questions, and can provide insights they didn't even intend to learn.
To see knowledge graphs in practice, look at how Google has transformed its search results. Have you ever Googled someone, and then noticed, off to the right side, a picture of the person along with related search results? Your curiosity is piqued, so you click on a related person. Then, when you dive into that person's past, you realize they attended your alma mater. The next thing you know, you're looking at your football team's roster from 15 years ago. You can get lost in all that information!
But, it's the best kind of "getting lost," because you're able to root through the "collective human wisdom" that Google has catalogued. It may seem overwhelming to replace relational databases with knowledge graphs, and to break down the data silos in your own organization, but just think, if Google can execute knowledge graphs on the largest scale possible, your business can manage its own data in the same way. It's all possible by building and understanding the relationships between data points.
Once you have this information in hand, you'll be better able to reach the on-the-go, connected consumer, because you'll have a better understanding of their needs. Your competitors are probably already using data this way, and there's no time to waste for you to do the same.
Learn more about using trusted data and insights in context across your enterprise.