Turning Data into Knowledge: Three Key Enablers for a Better Data Strategy in 2021

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Click to learn more about author Kendall Clark.

The mandate for enterprise IT to deliver business value has never been stronger. Today tech professionals are key participants in helping drive enterprise business strategy. Yet, to be truly effective, they are quickly learning that agility is the key enabler.

Hampered by data strategies that leave teams flat-footed when the market shifts or new questions arise — and with the enterprise data landscape becoming increasingly hybrid, varied, and changing — the year 2021 will see big shifts in how enterprises pivot from a reactive to a responsive Data Strategy. I want to highlight three key trends.

Semantic Graphs Drive the New Data Integration Landscape

Relational data was never designed to support complex business processes with changing requirements, particularly with the incredible data variety we see today. Most data integration techniques are artifacts of where Data Management was 25 years ago, not least since, truly, relational systems were never intended to represent large-scale information systems. Semantic graph — one of the fasted growing new technologies — is the natural way to represent data that is natively stored in other structures and will become increasingly popular in the new year as organizations look to connect data from structured, semi-structured, and unstructured sources to gain a full picture of connected enterprise data. 

Knowledge Becomes “Machine-Understandable”

The reality of digital transformation is that most “data-driven” efforts are doomed to fail, primarily because machines are not humans! Human decision-making is based on contextual intelligence, and in order to successfully automate, machines need to know what we know. One technology that is helping address this need is an enterprise knowledge graph (EKG), a modern data integration approach that discovers hidden facts and relationships through inferences that would otherwise be unavailable in large, complex organizations. EKGs make knowledge not only machine-readable but also machine-understandable by capturing real-world context from disparate data sources on a specific topic, person, project, etc.

Query-Answering Weaves the Successful Data Fabric

Data fabrics are starting to get a lot of attention for their ability to stitch together existing Data Management systems, enriching all connected applications. They are considered the next step forward in the maturation of the Data Management space. Data lakes once held the promise of centralizing an enterprise’s data assets but failed to make the data usable beyond storing it all in one place. Given the variety in the data landscape, the data warehouse is, in fact, even less capable than data lakes since they only admit structured data, to begin with, leaving the semi-structured and unstructured data silos completely disconnected. Data catalogs have emerged to provide an inventory of the bewildering diversity of enterprise data landscapes, only to be faced with the next great challenge: how to make the underlying data usable and reusable at enterprise scale? In short, by creating a data fabric.

Combining graph, inference, and virtualization, EKGs are operating at the heart of the data fabric and will become even more important in 2021. Unifying data based on its meaning, not location, organizations can create a body of knowledge to support the business and answer complex queries across data silos while powering an impressive range of use cases and supporting the full breadth of today’s increasingly complex, connected enterprise.

Turning Data into Knowledge to Support the Business

While data continues to rule the world, organizations still find themselves struggling to leverage their data to create a true competitive advantage. In 2021, organizations will look to make data relatable to the actual business context because what matters more than the data itself is its business meaning. As your enterprise evolves and more data, sources, and use cases emerge, knowledge graphs continuously absorb the new with no loss in manageability and accessibility. The mesh of meaningful relationships grows to fully represent the current expanse of what your enterprise knows. This is how your enterprise accumulates and then operationalizes knowledge for unique competitive advantage.

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