The Importance Of Finding Semantic Relations In Your Data

The Importance Of Finding Semantic Relations In Your Data

Finding relationships and patterns between different entities has been a prominent part of business operations for as long as we know. Even before technology, businesses needed to determine how different aspects of their operation were correlated to optimize profits. Back then, it was more of a guess and check to see how things worked out. If they guessed wrong, they would go back to the drawing board and find different patterns to drive their actions.

Luckily, that is not the case today, and people are no longer operating on paper and in notebooks. Company data exists in various different digital databases and data sources, leading to the ability for them to be connected and all data within to be analyzed and meaning to be extracted.

Gaining actionable insights from information that was once invisible is the new norm for businesses worldwide. They just need to take advantage of the AI-powered technology that is out there and available on the market today.

Correlations: What Is Relevant To What?

Semantic relations are meaningful correlations between different concepts and entities. Rather than just showing the correlation between multiple subjects, semantic relations focus on the meaning and the context of the business situation.

Who is the best person for this project? Who is the best colleague to ask about this subject? Is a certain person a useful resource for this specific topic? Managers and employees find themselves asking these questions all the time but many times don't necessarily know where to go.

As an example, by connecting data from support tickets to marketing documents to sales pitches to video demos, machine learning can identify the people being sought after from the above questions—all by collecting data on who authored different documents, solved certain support cases and produced different sales collateral. No more guessing and checking.

Problem And Solution Patterns In Research And Development

The use cases for finding semantic relations in your data go far and wide. This same type of machine learning can be used to identify the best partners for R&D. Every business or product upgrade is launched because they have a solution to an existing problem. Recognizing what that problem is and then constructing the road map to developing your solution takes a lot of work, and finding hidden patterns plays a prominent role in this process.

Semantic relations can help companies figure out what types of R&D partners are true experts in their field while also pinpointing relationships between research targets in a way that allows users to truly understand any or all correlations between subjects.

Understanding the meaning of all of your data leads to a more effective and highly focused R&D process. Once one has a documented solution to a problem, they can leverage it wherever the solution is needed (internally, externally, direct, partners, etc.).

The True Value Summarized And Where To Go Next

Many management teams and people generally think they have all of the information, but today, the saying that "you only know what you know" has never been truer. Especially in large corporations, data and information are being created daily on a gargantuan level. There simply isn't a way to take it all in and use it to drive business decisions without deploying artificial intelligence as a sidekick. AI, as a smart partner, works around the clock collecting and connecting the continuous storm of data being created from internal and external sources.

What relations and connections are you interested in seeing? To begin the journey toward a semantic relations strategy through AI, a business needs to define its use case. This entails really homing in on the problems that impact your business and establishing where they are coming from and why they need fixing. Having internal meetings with leadership and department heads is the best way to crack down on which areas need transforming and how building semantic relations can help. Be sure to include everyone who may have a voice or valuable input on the landscape of your operations.

A very important next step is defining success criteria. Defining success criteria allows you to narrow down your choices of the correct AI solution to deploy. As mentioned, finding semantic relations entails connecting data from various structured and unstructured data sources. Which solutions can seamlessly connect the data sources you are interested in? If your use case involves connecting Microsoft Teams chats with internal documents and data from Salesforce, the correct solution will come with out-of-the-box connectors for those data sources.

Eventually, when testing the solution, it is critical to use real company data and not makeshift data. Using company data can give organizations the best idea of what the deployment experience will look like and can allow them to ensure semantic relations are being formed. Any shortcomings can be addressed much more easily when testing with company data as well.

Whether you need to find the best resource within your corporation or insights for the R&D process, the capacity to see correlations and patterns in hidden data gives your business more potential in every corner of your operations. In addition, it puts less burden on your employees and equips them with revolutionary tools that will change the way they work and approach their projects.