By Ike Kavas, Forbes Councils Member, Forbes Technology Council
For the last 2,000 years, we have known that the earth is round. Most notably, it was Aristotle who pronounced the earth was a sphere. Yet this idea was dismissed by many at first, with potentially a few deniers left that still believe the earth is flat.
You might say that there is a similar phenomenon happening in the business world. We have known for some time that semantic data — data with context, relationships and links — is far more insightful and knowledgeable than two-dimensional flat data. However, enterprises of all sizes continue to work with flat data, a system that is outdated and inefficient, and that slows down productivity.
What is flat data? Simply put, it is data that is stored in legacy silos. It can be structured or unstructured data. It is data that provides no context and gives only a narrow view of a broad, intertwined group of statistics, information and relationships.
The opposite of flat data is semantic data, also referred to as contextual data, which provides workers — both digital and human — with a 360-degree view of their data and the many factors and background of where that data came from. Semantic data is the deep understanding of multiple data points and their relationships to one another. Understanding the context of these pieces of data can lead to deeper insights through AI and machine learning. Businesses that want to remain competitive and boost productivity and efficiency need to take their flat data and transform it into contextual data.
For instance, let’s say you work at a bank, where your job is to process mortgage applications. This means sorting through stacks of “flat data” (e.g., bank statements, tax records, utility bills, pay stubs, etc.). Following this sorting, you then must correlate with the bank accounting system to check credit rating scores, conduct a background check and go through myriad other steps that require scores of data — sometimes up to 600 different documents — before the underwriter can determine whether the applicant is approved. All of this data may be linked in your head, but in your digital stack, it has no history, is not connected and has no correlation to any other data.
Today, technology has greatly advanced since the days of the flat world with flat data. In the case of our mortgage documents, the relationships between documents, from both internal and external sources, can now be linked and viewed in a comprehensive and effective way, without the flat data wall blocking and slowing you down. Knowledge graphics that use semantic data can visually represent your findings, offering business leaders a global, well-rounded perspective. This is what we call enriched data.
Accelerating process automation is the result of enriched, semantic data when it is applied and visualized. The result is a view of compiled context that users can access and that systems and digital workers have available in digital form. So, instead of reviewing the bank statements, tax records, etc., as separate “flat” documents, you can see them as one single digital entity. Instead of taking days or weeks to process a mortgage application, you could do it in a few minutes.