Leveraging Knowledge Graph Technology to Fuel Advanced Analytics

Since the early 1990s, organizations have been collecting, storing, analyzing and reconfiguring a plethora of information, only to find 20 years later that they are still struggling to maximize their big data investments. In fact, an Accenture Research study found that many firms continue to struggle to harness the power of data, with less than one-third of companies (32%) stating they were able to realize tangible and measurable value from it. What’s worse, only slightly more than one in four (27%) said they could produce highly actionable insights and recommendations from their data and analytics projects.

From optimizing retail shelf space to delivering better patient care and improving strategic decision-making to reduce insurance fraud, the challenge remains clear for organizations of all sizes; they must have the ability to connect data and deliver analytics solutions to help solve urgent business problems.

Why Settle for Historical Descriptive Analytics When You Can Have Advanced?

Unlike descriptive analytics, advanced analytics is the autonomous or semiautonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions or generate recommendations. Advanced analytic techniques include data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing and neural networks.

Although descriptive analytics answer historical questions such as “What happened?” and are used for reporting, data scientists (and data engineers) apply advanced analytics to answer questions including “What will happen?” and “What action(s) should we take?” Advanced analytics come in different flavors, from diagnostics and forecasting to predictive and prescriptive modeling. In fact, artificial intelligence (AI) is also considered a form of advanced analytics used in real-time applications such as recommendations.

How Technology is Harnessing the Complexities of Achieving Advanced Analytics

Because of the inability to deal with the challenges associated with accelerating data analytics competency to gain a competitive advantage, there are a growing number of vendors that are helping organizations quickly address increasingly complex questions with high fidelity. These solutions focus on data management and advanced analytics based on a specific domain, such as healthcare, insurance or retail; function(s) such as supply chain, R&D or marketing; and/or competency across industries, such as strategic decision-making or customer churn. These data analytics platforms often include a data lake (or warehouse/lake house), a wide assortment of data, and BI and data science capabilities.

However, owing to the size and complexity of data multiply, these same solution providers are increasingly turning to semantic knowledge graphs to improve their advanced analytics capabilities. In fact, by 2025, graph technologies will be used in 80% of data and analytics innovations, up from just 10% in 2021, according to Rita Sallam, distinguished VP and Gartner Fellow in the Data and Analytics team. 

Unlike semantic knowledge graphs, conventional graph and/or relational data architectures lack the access, context and inferencing required to meet the grueling demands for innovating and monetizing advanced analytic solutions. This is because they are constrained by architectural limitations for highly scalable, discovery-style analysis in relation to business problems. Unlike the rigidity of relational or conventional graph database structures, a flexible semantic data layer in knowledge graphs enables users to endlessly link and network the complex relationships contained within their data platform and other sources without changing the underlying data, thus enriching the semantic meaning of the data.

Semantic search allows users to search their data platform by meaning, scanning the knowledge graph to uncover all layers of connections across the search terms, ensuring no results are left out. Using a concise syntax and powerful, standards-based graph query language, SPARQL, individuals can answer complex analytic questions from the data platform. This empowers data and analytics teams to act quickly.

Enterprise Knowledge Graphs enable organizations to add a semantic layer on top of their existing data analytics platform to develop and monetize advanced analytic solutions. However, organizations need to include the following attributes to enable scaling advanced analytics:

1. Graph-based virtualization to access large amounts of data across formats, domains and sources and the ability to incorporate new data sources/sets as needed – without the need to copy or move the data, which saves on infrastructure costs and analytics development time. 
2. Rich semantic layer that sits between the analytics consumption and data platform layer and represents explicit and inferred relationships from data, including context of data and metadata and any data interrelationship.
3. Data model development and reuse to create a semantic data model with business concepts, reuse and expand the data models to address new use cases, and write federated queries without requiring proprietary language.
4. Intuitive search and discovery of data insights to search semantically with context and graphically visualize data relationships to uncover hidden patterns or unexpected insights in relationships and extend this workflow into popular BI tools such as Tableau and PowerBI.
5. Design for performing large graph-based work at scale, including processing massive semantic or Resource Description Framework (RDF) triples – a standard originally designed as a data model for metadata and used as a general method for description and exchange of graph data – without centrally storing data and query performance, thereby improving data and analytics work productivity and time to market. 

Ensuring the enterprise knowledge graph platforms encompass this functionality, organizations can easily connect, contextualize and discover new insights from any data source and structure so teams can deliver better, faster and lower-cost analytic solutions to their customers.

By leveraging an enterprise knowledge graph platform, enterprises can tap into innovations such as explainable AI, data virtualization, reusable semantic data modeling and scalable complex query performance to help speed analytics insight and reduce data operations cost. Finally, they can start to reason about the underlying data and use it for complex advanced decision-making instead of being forced to look in the rearview mirror using antiquated descriptive analytical approaches.