2021 Gartner Data and Analytics Summit Recap
Gartner’s 2021 Data & Analytics Summit delivered a variety of discussions focused on building a successful strategy, incorporating the latest trends in data and analytics and providing valuable insights for teams and leadership. Some key trends shared throughout the event included data fabric, decisions support, and graph. We’ll discuss some of the major takeaways from these topics below.
Data fabric is a data management design that enables more flexible pipelines for data integration. A data fabric allows you to link data sources throughout an organization with real-time standard integrations. It is complex and made up of multiple technological elements and tools, including knowledge graphs, semantics, and ML/AI, which can support automated data access and sharing. With a rise in complex data management landscapes, data fabric frameworks can be a beneficial investment, especially for combatting a shortage of skilled data management and engineering teams. One presentation during Gartner’s Summit explained how from a business perspective, this investment enables employees that may be less familiar with technical processes to more easily interact with the data, and from a data management and IT perspective, a data fabric means automated optimization, improving productivity and costs.
Many sessions throughout the conference covered elements of modern data architecture, discussing how your data lake should sit with your data warehouse for both reporting and analytics. Our Insights to Action team believes that reporting and analytics have different uses and needs, and that both reporting and analytics require their own architecture and strategy considerations. Data lakes can be used for analytics exploration, while a data warehouse is used for optimization or consumption on a broad level — and using a data hub architecture can better connect data sources and users. While a data lake and a data warehouse serve similar purposes, they are best used in combination with other patterns to work together for data sharing. Therefore, creating a cohesive strategy with data hubs, data lakes, and data warehouses can better support use cases. This enables platform evolution as reporting and analytics business needs shift.
Decision support and intelligence was another key topic of the 2021 Gartner Data and Analytics Summit.
At Clarkston, we call ourselves the Insights to Action team because the focus should always be on using analytics to guide better decision making and taking action that makes substantial changes. One way to make impactful change is through decision automation — for example, embedding analytics in an operational process. This requires thorough testing, monitoring, and change management processes in order to grow stakeholders’ trust in the solution and incorporate more decision automation moving forward. However, decision models are not designed to be used in every solution, as balancing human and machine decisions depends on the risk and accountability factors of different use cases.
Business rules and pre-defined logic are both necessary and are often used in combination with ML or optimization in complex use cases, such as making important business decisions. Decision automation can enable human teams to focus on more complex, risky decisions, which results in greater speed, lower costs, and more standardization due to automation of routine tasks. The Summit covered the two main elements of selecting processes for decision automation: the complexity of calculations and the number of conditions involved. This means that there is a spectrum, where different tools may fit different needs for different organizations. By involving teams in continuous development, monitoring, and improvement, decision automation can be successfully implemented.
Many 2021 Gartner Data and Analytics Summit discussions covered graph database analytics, and how it is accelerating in use for more complex data science. Graph technology is an alternative data storage method that defines data by nodes and a relationship, which enables you to more easily identify bottlenecks or quality issues. Graph helps uncover interesting relationships in your data, whether between people, products, business partners, etc. Using the right data architecture can enable these relationships to become useful data inputs for improving models and solving certain challenges. Digital twins and process mining are also some related efforts that can make a big impact on continuous monitoring and updating to increase productivity and reduce operating costs.
There are many ways that graphs can work with data science to produce benefits, such as data exploration with graph visualizations, graph analytics and feature engineering, and mining graphs for business rules. Graph technology can either be used alone or integrated with data storage or analytics and data science platforms. However, one key takeaway is that traditional data storage approaches often fall short in using relationship insights from graph. Therefore, it is important to help data teams understand graph and how it interacts with objectives and use cases, such as product recommendations, cybersecurity, customer segmentation, and more.
These topics covered in the 2021 Gartner Data and Analytics Summit are complex but important as trends in data and analytics that can benefit an enterprise. Clarkston’s data and analytics team supports a broad set of services, from data strategy and modern data architecture to governance, master data management, business intelligence, data engineering and data operations, all the way through machine learning, AI, and predictive analytics.
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Contributions by Courtney Loughran