Looking at a rapidly arriving future where all things increasingly become data to be managed, archived, utilized and – most importantly – made sense of, semantic technology seems to be more and more in the minds and considerations of those in need of efficient data integration.

We have talked about the benefits of the technology in The Knowledge Graph and the Enterprise and Thinking Outside the Table. What we haven’t covered so extensively, though, is the mindset needed for adopting semantic web standards and the solutions created with these standards. As much as any transitioning an enterprise makes to a Linked Data solution is a matter of adopting new technology, it is also a matter of a worldview.

No Dataset Is an Island Anymore

Integrating data and linking information throughout systems requires not only changing legacy technology but also changing the set of assumptions and methods held by an organization’s leaders as well as the people working with the systems.

Tell Mr. Bruce from the accounting department that getting a dump of data no longer serves your purposes and now you want to have access to the data in real-time. That will make you the black sheep of the enterprise in no time.

Of course, this example is taken to extreme as there are many ways to use Mr. Bruce’s data so that the security and the quality of these data are not compromised at any level. But the point is that no dataset is an island. This mindset through which the world, and most importantly its data model, is seen as dynamic structures of “small pieces loosely joined” is the world of semantic technology and its enterprise applications.

Semantic Technology and the Growth Mindset

In the book Mindset: Changing The Way You think To Fulfil Your Potential, Dr. Carol Dweck writes:

[…] [B]eliefs strongly […] affect what we want and whether we succeed in getting it.

She introduces a growth mindset where one believes that we can improve intelligence, ability and performance and a fixed mindset, which refers to the belief that a person’s capabilities are fixed, set to a certain extent. And while it is true that considering and further adopting a semantic technology solution is a matter of decisions related to tooling and technology, it is also true that it is a matter of a growth mindset.

The two concepts of growth and fixed mindsets, put in the context of enterprise data management systems and semantic technology, can correlate to the two attitudes of managing data. The first one is when data systems are flexible and open to new input and the second – when data systems are created as fixed, with a certain amount of pre-determined space for input. Both boil down to how an organization will treat its data (and understanding of the world) – will it act like an island or admit that no organization (or dataset) is an island anymore.

When Semantic Technology Comes to Town, I’m Gonna Jump That Train?

Many enterprises have realized the return of (and the dire need for) investing in a semantic technology solution. Yet there are also many who are being distracted by technology-first fixes.

It is true that Linked Data platforms and data management systems come with their technical challenges such as the challenge of navigating a different set of data silos, lack of high-quality data (especially when it comes to using Open Data), sophisticated query language issues, reconciliation problems and more. But more importantly, they provide answers to some fundamental questions that have more to do with the way an organization sees the world rather than simply technology.

Some of thesе questions are:

• Are we willing to build information architectures with data coming from multiple, heterogeneous external sources?
• Can we handle an ever-evolving data landscape?
• Are we willing to invest in a semantic graph database and utilize its maximum capacity to fully address our specific use case?

The answers to these questions may not be straightforward, although at a first glance we all want interoperable data, integrated systems and a 360-degree view of our information. But in the end, they all compel us to think over what worldview and approach to the data landscape we are willing to embed in our systems.

Fortunately, a good number of organizations worldwide have already walked the semantic technology talk. And we can learn from them.

Semantic Technology Trailblazers To Learn From

Semantic technology powers many platforms and services we interact with on a daily basis. Household names such as the BBC, Fujitsu, Springer Nature, the UK Parliament, the British Museum – to mention just a few organizations dealing with huge amounts of data and content – successfully use semantic technology-based solutions to build and maintain their information architectures.

As a result, these organizations create a huge network of semantically interconnected objects, which can be easily explored, navigated and managed. This helps enterprises with a basic, but mission-critical task: how to manage data coming from different interactions and transactions related to customers, supply chains, core business processes, networks, employees and content.

Building a semantic technology solution, although very specific for every use case, ultimately provides an enterprise with the tools to organize, manage and discover internal data. It is also an enabler of linking data to external data sources – to benefit from the network effect.

What semantic technology does is get data from the Web (as well as social, sensors, internal systems data, text and documents) and turn them into one coherent whole that ultimately serves different departments to connect the dots and brings a unified view to what the organization is doing and where it is going. This, in turn, enables better search, exploration, classification and recommendation across diverse information spaces. From a customer’s perspective, such coherency brings intelligent platforms and enhanced user experiences.

Epilogue:

Connectivity is inevitable. So is the shift of the way enterprises see their place within the digital landscape. That said, to choose the right technology is equally important as to see (and model) the world an enterprise operates in as an ecosystem of interacting entities.

Semantic technology can help us embed dynamics (and we will talk about that in an upcoming post) in our systems and operations and the data both these produce and operate with. It does help to set the wheels for quite a few processes related to data management and governance, given that we are ready to see our datasets as dynamic structures with interrelated entities to be embedded into the enterprise IT infrastructures running business critical systems.