We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week in Sydney, Australia. Below is a collection of the key announcements and insights coming out of the conference. You can read the highlights from Day 1 here.
On Day 2 from the conference, we are highlighting how to create metrics that matter, best practices for trusted data sharing and the role of graph analytics and machine learning in AI.
Presented by Andrew White, Distinguished VP Analyst, Gartner
No single set of metrics works for every organization. Business and IT leaders need a model to help them build the metrics portfolio that best matches their competitive environments, business strategies and digital maturity. In this session, Andrew White, Distinguished VP Analyst at Gartner, explained how data and analytic leaders can bolster their organization's performance measurement capabilities and communicate the business value that good metrics create.
“Analytics help create more impactful metrics by bolstering your organization's ability to measure, classify, and decide.”
“To make metrics that matter, create positive outcomes by focusing on three key areas of business value: resource allocation, conversation optimization and comparative benchmarking.”
Resource Allocation: “Analytics — in particular a precise system of measurement — enables organizations to allocate just enough headcount, budget or inventory to balance the cost/quality trade-off.”
Conversion Optimization: “Analytics — specifically the capability to classify with granularity — enables organizations to personalize their business processes and, therefore, improve conversion ratios.”
“True comparative benchmarking is difficult and requires a trusted data aggregator to collect data at a low level of grain and calculate the metrics consistently across all peers.”
Presented by Lydia Clougherty Jones, Senior Director Analyst at Gartner
Sharing data is a must for revenue growth, cost optimization, improved risk mitigation and accelerating digital business. In this session, Lydia Clougherty Jones, Senior Director Analyst at Gartner, explained the business imperative of data sharing to help D&A leaders modernize and align data sharing with stakeholder priorities, enterprise goals and organization benefit.
“Mandated enterprise data sharing is closer than you think.”
“Global data strategies highlight data sharing as a key priority to generating public and private value.”
“Data sharing is a business-facing key performance indicator (KPI) of achieving effective stakeholder engagement and providing enterprise value.”
“Embed data sharing in every relationship.”
“Embrace the chaos within augmented data ecosystems outside of your organization’s control to find known and unknown relationships in combinations of diverse data.”
“Organizations often unnecessarily require too much trust, or not enough, across data ecosystems, disrupting the risk/reward calculus of data sharing for business value.”
“Trusted data sharing means the optimal, not perfect, level of trust across data sharing ecosystems. Apply “situational trust,” not perfect trust, to achieve maximum value and benefit from data sharing.”
“While occasionally the right amount of trust could also be perfect levels of trust, business leaders must resist the emotional pull toward over-investing in perfect trust, which ironically can create enhanced risk given emerging D&A liability theories.”
“The journey of eschewing perfect trust, and instead establishing the right trust to match the situation at hand, enables new business opportunities for data reuse and resharing, accelerating data and analytics value while mitigating risk.”
Presented by Erick Brethenoux, Distinguished VP Analyst, Gartner
As data volumes and variety grow, organizations seek new ways to use it to inform and drive business results, but the types and composition of problems become more varied, requiring different technologies and approaches such as graph analytics.
In this session, Erick Brethenoux, Distinguished VP Analyst at Gartner, explained the concept of graph analytics and how to use it to find hidden insights in data and enhance decision making.
“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.
Gartner, Inc. (NYSE: IT) delivers actionable, objective insight that drives smarter decisions and stronger performance on an organization’s mission-critical priorities. To learn more, visit gartner.com.
Susan Moore
Gartner
susan.moore@gartner.com
