Graph Databases the “Secret Sauce,” and Other Data Management Trends for 2022
Another year ends and a new year begins, and so too do the countless “2022 trends and forecasts” lists that clog up all our feeds. In the spirit of keeping up with the trend of talking about trends, here at Semantic Web Company, we have also compiled our own list of things to watch for this year, but this time focused on data management trends for 2022.
Having done some extensive reading on analysts’ predictions and industry leaders’ summaries, we have consolidated five trends predicted to make a difference in areas of semantic AI and data management this year.
1. Graph databases as a leading force for 2022
As the article’s title implies, graph databases are predicted by many (see International Data Corporation (IDC), TDWI) to be the secret sauce of 2022. Starting this year, Vice President of Research at IDC Carl Olofson projected that in the next 10 years, the usage of graph databases will increase by 600%.
In this same article reported by analyst Dave Vellante, Olofson summarized how doing a simple reporting or analysis of a supply chain and all its parts is more difficult with a typical relational database:
“You can [find relationships and see how many levels there are of the chain] with relational databases, but it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It’s not supported in the database. And whenever you have to program something, that means you can’t trace it, you can’t define it. You can’t publish it in terms of its functionality and it’s really, really hard to maintain over time.”
In a graph database, users can overcome the common limitations of relational databases because it is designed with the intention of having rich relationship analysis and context mapping. Since they are literally visualized in a web of sorts, they are optimized for tracing connections in the data so that companies can get holistic overviews of all their data, documents, etc. As a result, retrieval of specific resources are much easier both to reuse and analyze.
Knowledge graphs, better explained.
Knowledge graphs, while in popular demand to suit data management trends for 2022, are often described as complex – which can sometimes make them off-putting to the average user. However, data scientists are calling for a growing need to teach the data management community about what they are and how they work, so that more companies are keen on taking them on and reaping the benefits.
What’s a knowledge graph and what’s it good for? For starters, they provide a very intelligent way to:
- Establish rich connections between data points
- Define data objects (through the use of concepts, see an overview of concept tagging here) so they can be easily searched for
- Combine siloed data structures so data can be accessed in one place
- Interpret unstructured text via natural language processing (NLP) to make it actionable
- And the list goes on
While a knowledge graph looks quite complex, it actually talks about the data it’s made up of rather simply. “The information is stored in the way people naturally think and ask questions,” says COO of Enterprise Knowledge Joseph Hilger.
Take this example from the Technical University of Vienna as a guide:
If we start with “Lily” in the bottom left corner, we can visualize her relational path to the other entities via the italicized text.
So, Lily is a Person, she is interested in Da Vinci, who painted the Mona Lisa which is in the Louvre, which is located in Paris, which was lived in by James, who is a friend of Lily.
We have come full circle and we can easily understand it because we have followed the direction of the data points, and have thus tracked the graph’s relations. The same can be said for company data, with tracking a customer’s purchasing history, supply chain operations, HR employee architecture, and so on.
2. Focusing on unstructured data
The clear benefit to all this is that knowledge graphs help flesh out unstructured data. Which is great considering that a data management trend for 2022 is that data managers will continue to prioritize their unstructured data as an asset. In the past, companies have disregarded their unstructured data because it was too cumbersome to process and derive insights from, now people are viewing it as an opportunity to analyze different aspects of their data.
Semantic AI helps us interpret unstructured data because it combines machine learning and NLP techniques with knowledge graphs to enable algorithms to better analyze text by not only processing words, but understanding the underlying concepts and their context. In other words, semantic AI will tell a computer that a document about the car purchasing market is about Jaguar the luxury car brand and not jaguar the jungle animal. Read about these capabilities in SWC Research Director Artem Revenko’s article here >
Unstructured data is not going anywhere, so jumping on a software that can extract relevant terms from hundreds of pages and derive useful information from it is in everyone’s best interest. Check out our free Named Entity Recognition demo to see how something like this could work.
3. Intelligent Document Processing and Content Management
On the topic of analyzing text and making documents actionable, another data management trend for 2022 looks at putting content management at the forefront of data strategies. If people are starting to care about their unstructured data, then it’s only natural that they also care about how their content management systems (CMS) are doing.
Along with the typical problems that text-based content brings such as the language ambiguity stated above, one major drawback to working with it is that it becomes extremely hard to work with content if it is not managed and tagged properly. Searching for specific content is quite burdensome, and that’s why the need for auto classification and document tagging are needed to enhance typical CMS search fields’ performance.
Gartner has positioned Intelligent Document Processing (IDP) as a necessary practice for the coming years for its ability to capture, digest, and reprocess complex documents into workable data. NLP and knowledge graphs are heavily embraced for this capability – see how we have combined this altogether in our Contract Intelligence demo.
In the PoolParty world, better tagging and findability of objects in a CMS are pressing topics – so we are investing much of our time in building the right solutions. Take a look at our PowerTagging integrations for SharePoint, Adobe Experience Manager, and Tridion to see how this is being managed.
4. Data governance
One of the greater advantages to working with semantics as a data management strategy is that it prioritizes the use of active metadata. Simply put, metadata is data that gives information about other data; a novel is described by its genre, author, paperback vs. hardcopy, publishing company, and copyright date, which are all examples of metadata in its various forms.
Taxonomies, concept tags, and knowledge graphs facilitate metadata creation and maintenance well, which is of chief importance to data governance. Rather coveted in data management communities, data governance is a framework of sorts that defines how data should be treated based on internal data standards and policies.
In their projected trends for this year, Dataversity claims that “data security, data auditing, and Data Quality are becoming more complicated. As a consequence, organizations are developing more comprehensive Data Governance strategies.”
Besides helping comply with regulatory and business requirements, data governance helps assess the impact of a change within a data source. Through developing a standardized data model, security and risk professionals can be ahead of potential issues by classifying data according to risk and security needs. Quality metadata management and by extension, data governance, facilitates data lineage, impact analysis and data management to reinforce privacy requirements.
5. Semantic AI for 2022 and beyond
This mostly goes without saying, but all these features and information point to semantic AI and its overall growing popularity in data management communities. Companies are increasingly relying on the semantic web for their needs, especially when it comes to unstructured data and repairing data silos.
Companies like RWS, who have been serving intelligent content management services for over 20 years, are latching onto semantic AI to deliver better customer experiences. Take our OEM integration with RWS’ Tridion, for example, which has recently embedded semantic AI to “deliver intuitive digital experiences” by means of enhancing the content management platform’s search capabilities.
With intelligent concept tagging, natural language processing, and knowledge graphs at the forefront of a data management strategy, we can, as CEO Andreas Blumauer puts it, “use a really large amount of data and make complex queries over it, validate different data sources at the same time and put them together.”
Graph databases, and by extension, semantic AI prove to be high performing methods of collecting, managing, and deriving data – so much so that they won’t just remain a data management trend for 2022 but rather a staple for many years to come.
Want to jump on the bandwagon and get started with graph databases yourself? Take our free, 10 minute Knowledge Graph Assessment to get a detailed report about your company’s readiness to adopt knowledge graphs.