Data collection and comparison has become a major driver of business technology over the last decade. Between archival of old documents and tracking of ongoing business data in real time, accumulating and using information to better manage the company has been a major motivator for most enterprise software for during that time, even if it isn’t its main purpose. The last few years have been moving even faster in this direction, too, as artificial intelligence and machine learning do a better and better job of providing insights and analytics that fuel data-driven decision making at all levels of your business. Of course, those applications for your data are only possible if you have a way to access and organize it, which is one of the major hurdles to getting the most out of these new tools. If your data is in a silo where no one can reach it or see how it all relates, you aren’t able to gain those insights. So what’s the solution?
Knowledge Graphing and Business Data
The first step to using data is knowing how to get it into a knowledge graph. Graphing information is a good way to quickly organize it and to show relationships between otherwise hard to reconcile events, but it’s not enough to simply organize things visually or to do it by a couple sets of matching base characteristics. If you delve deeply into a knowledge graph guide, you’ll see the underlying database structure and method of calling up visualizations of relationships between data points or even entire data sets becomes integral to the graph’s usefulness and ability to provide the ground for robust insights. Moreover, knowledge graphing can move beyond some of the limitations that traditional graph organization is prone to, like the tendency of the data to outgrow the algorithm for sorting it. By its nature, a knowledge graph allows for more flexibility and ability to traverse individual segments of data or to redefine the comparisons and relationships being queried. It is, essentially, a constantly shifting method of organizing knowledge about a characteristic of your business or a phenomenon it deals with regularly.
Artificial Intelligence, Data, and Insights
Today’s major AI-driven software uses advanced data tracking and organization to build its base of experience as the algorithm makes decisions, which means the method of organizing that data and making it available for cross-comparison greatly affects the usefulness of the machine’s insights. It doesn’t matter if you’re talking about the identification of marketing trends or the success of training and development programs. It’s as simple as the fact that your algorithm has to be able to first access information and then put it into relationships with other data before being able to make those decisions. The more it has the ability to contextualize your data into a bigger picture, the faster and more accurate its conclusions. As such, the best outcomes for any of your machine learning or artificial intelligence applications will come when you have data organization and retrieval that supports them.
How To Invest in More Effective Data Use
There are a few clear ways to get more out of your data, rather than simply letting it sit in a silo where it’s hard to access and visualize. They all take investment, though, and frequently that investment means both capital and resources in the form of setting up procedures for understanding when you could get more from your tools and when you could use more tools to accomplish your goals. Here are some great ways to do more with your data:
- Invest in the database and archival tools needed to fully contextualize information in your archives and data from software that provides analytics
- Look for meta-analytical tools that can make use of the contextualized information with both manual queries and automated insights
- Plan for future enterprise software investments to be interoperable with your existing archives and analytics
- Look for more opportunities to gain insights in areas you are not currently supporting with analytics and feedback
The more you do with your data, the more you can do. It’s really that simple.