We’re in the midst of the data revolution, yet not many people are aware of how much power they have at their fingertips with the information that is coming in today. With semantic information processing, a relatively new area within the IT industry, it is now possible for a platform to view data intelligence as more than just a string of characters, but rather understanding data entities as a real world "thing."

When your software can recognise "Joe" as a person or "Chicago" as a city, or even a "chip" as a type of food made from potatoes (and not a computer chip for example), then the type of feedback you are getting from your data insights goes from interesting and probabilistic relevancy to possibly profound and definitely insightful.

Such processing ability becomes the ready-to-use tool that today’s thinkers, strategisers, and designers need, without having to reach out to a data scientist for help in interpreting and applying the information held in the data.

The people who could benefit the most from data management solutions, which can pull together data points and tell a story, are often those who are relying on traditional Business Intelligence (BI) tools. These BI tools, although helpful, are not capable of connecting and relating the dots in data management and interpretation compared to emerging technologies.

In fact, many small- to mid-sized organisations are just catching up on the benefits of standard BI, even though the next generation of workflow and data management is already available. Using a BI tool, in order to get any results that are in any way comparable to semantic information processing, a user would have to configure every data point option into the BI tool, which is an impossible task. This means that business owners, marketers, business strategists, and other professionals who rely on data intelligence to do their jobs are missing many key data relationships.

Data Needs to Be Personable

Piles of data are just that—piles. With semantic processing and efficient data management, it is possible to connect the dots from all data resources that are available today.

This does two things. First, it eliminates wasted, superfluous data that can make gaining a clear picture of a "subject" very difficult. Second, it helps to generate more useful information that can be readily applied to an organisation’s planning efforts and decision-making processes. On the other hand, configuring reports and dashboards based on your preconceived notion of what you may need, by necessity, limits insight.

For example, today's marketers are scrambling to create a better experience for customers, known as the omni-channel experience. The businesses that are thriving are the ones that are able to anticipate an individual's movements along the customer journey and supply their needs every step of the way. The only way they can do this is if they are sure that they have a relevant, single customer view with which to work.

With standard BI, a company may see that a customer enters their information online and may receive data about their online shopping habits. But what if this customer then goes into the brick-and-mortar store to make actual purchases?

With traditional "string" data views, the business' marketing team may have put efforts into driving online purchases when the customer never buys online. With "thing" data interpretation, inputs from different resources such as social media, app log-in info, or device usage, create a fuller, and more accurate picture. Both transactional and behavioural data insights are factored into relationship views of the "thing," the customer in this case, as a real, thinking, dynamic entity identifier—not just a string.

Today’s companies believe that the most significant obstacle that they face for achieving omni-channel success is related data linkage. That’s why the data interpretation services that are available today are so intrinsic to many businesses' ability to remain competitive.

A full data management platform such as Latize Ulysses allows business users to understand the relationships themselves, without having to wait for their data scientists or outside analytic services. When data is mapped from raw data sources to a semantic graph, information becomes instantly accessible, visible, and usable for anyone. That’s efficiency, and it's what today's data-hungry organisations have been waiting for.