“Top performing organizations benefit from a digital-ready, data-literate and data-driven workforce.” 

Gartner identified seven specific behavioral traits that are exhibited by data-driven enterprises.

Literate: A data-literate workforce is actively pursued and organizational and individual literacy is regularly measured and taught. Job expectations include data literacy requirements, and they are actively recruited.

Intentional: There is a high degree of intentionality in how leading organizations manage, measure and monetize their D&A assets.

Accountable: An empowered chief data officer (CDO) exists and leadership promotes data-driven behaviors and is accountable for implementing change. 

Analytical: Decisions are based on evidence versus gut or experience. 

Innovative: Continuous experimentation to find new value opportunities using D&A is the norm, with failure cast as learning.

Communal: Data and insight sharing is enthusiastically democratized and supported throughout the organization.

Empathetic: Decisions and initiatives consider greater context/ecosystem, challenge norms and thoughtfully consider human factors. 

“Organizations should start by discussing these attributes and identify the biggest impediments to achieving goals. Identify what must change in order to use data and analytics more effectively in the enterprise.”

“Graphs represent relationships between entities in a way that cannot be captured in tabular data.”

“Graphs capture explicit and implicit context and relationships in a single flexible model.”

“Graph analytics can extend the potential value of the data discovery capabilities in modern business intelligence and analytics platforms.”

Data & analytics leaders should explore their graphs for multiple features including centrality, community, shortest path, similarity, connectedness and graphlets.

“Algorithms and analytics are not just useful in themselves for analyzing a graph. They can actually be used as features in machine learning (ML) models to boost and improve model accuracy.” 

“Graph ML enhances existing predictive models and provides entirely new solutions.”

Organizations should dedicate data scientists’ time for exploring graph frameworks and libraries.

“Self-service analytics empowers regular business users to access data and use it to drive decision-making.”

“However, a lack of guardrails around self-service leads to chaos.”

Gartner research showed that poor data quality can cost companies more than 6.25% of annual revenue globally.

“Without tight collaboration and buy-in from both business & IT, it is extremely difficult for D&A leaders to govern self-service analytics.”

IT can create consistency and build trust in data by delivering curated, high-quality datasets for use by self-service analysts.

“Teach the business what they should and shouldn’t do with self-service by providing training on how to use business intelligence (BI) tools, conduct appropriate analyses and protect your data in accordance with your governance policies.” 

“Monitor activities within your BI platform to ensure proper use and demonstrate the value of self-service as adoption increases.”