Every organization faces unique challenges in becoming data-and analytics-driven with its people, business processes, innovation, and competitive market. So why would an off-the-shelf, one-size-fits-all modern data architecture trend be the answer? It's not. This single plan addresses those challenges and helps you create the modern data architecture your organization needs, align it with its current business strategy, and incrementally unlock scalable business value. We follow a four-step process that translates business goals, leverages an analytics capabilities framework, and applies modern data architecture principles for a logical data architecture that allows for prioritization, planning, and developing people's proficiency. Next, we determine which architecture trends and modern data stacks can deliver data management and analytics capabilities most effectively in your organization. Data leaders and architects with this knowledge can demystify and communicate how emerging technologies and architecture trends from cloud-native to data mesh and data fabrics matter in your organization.

Knowledge graphs are a valuable tool that organizations can use to manage the vast amounts of data they collect, store, and analyze. Enterprise knowledge graphs’ representation of an organization’s content and data creates a model that integrates structured and unstructured data. Knowledge graphs have semantic and intelligent qualities to make them “smart.” Attend this workshop to learn what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your data. This is a very interactive workshop, so be prepared to learn about knowledge graphs and actually build one.

An introduction, overview, and update for professionals who interact with data privacy functions and want to understand privacy and data security better. Explore the regulatory environment and the ways in which businesses are responding (and failing to respond). Consider why privacy is important and what's changed to make it even more relevant today. How have evolving tech and consumer preferences affected information practices and data security? Learn simple models to harden yourself and your coworkers to privacy and security threats. Join privacy professional Jockisch in this interactive workshop to understand how to better protect the sensitive data in your business.

In an information economy, data is the currency of business. Digital upstarts are disrupting every industry, making it imperative that organizations have a strong data strategy that turns data into insights and profitable activity. Organizations need data to streamline operations and reduce costs, to improve decisions and plans, and to grow revenues and profits. A data strategy is an enterprise-wide plan to harness data and analytics to achieve business goals. At a high level, it is a blueprint for creating a data-driven organization; at a low level, it is a set of blueprints for designing a data architecture to acquire, transform, and deliver data to business users and applications. Eckerson explains the keys to developing an actionable data strategy, how to build an executable roadmap, and creating a data strategy that aligns with business needs, based on an organization’s unique circumstances, culture, and data maturity.

AI and the internet are transforming our understanding of how the future happens, enabling us to acknowledge the chaotic unknowability of our everyday world.

Back when we humans were the only ones writing programs, data looked like the oil fueling those programs. But now that machines are programming themselves, data looks more like the engine than its fuel. This is changing how we think about the world from which data arises, and that data is now shaping as never before. We’ve accepted that the intelligence of machine intelligence resides in its data, not just its algorithms—particularly in the countless, complex, contingent, and multidimensional interrelationships of data. But where does the intelligence of data come from? It comes from the world that the data reflects. That's why machine learning models can be so complex, we can't always understand them. The world is the ultimate black box. Weinberger looks at the implications of this for people who work with data.

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Collecting, querying, and manipulating data are important, but analyzing it well provides a competitive advantage.

Organizations employ many data analysts embedded in various departments and business units. These data analysts cost organizations millions of dollars in wages annually. Surprisingly, corporate data teams don’t know most of the data analysts in their organization, nor do they have a strategy to align them or optimize their organization’s investment in them. Eckerson presents a comprehensive strategy for empowering data analysts; describes how to make a business case for developing a self-service strategy that optimizes their time and output; and explains how to motivate and retain data analysts (people), how to organize and manage data analysts (organizations), how to govern data analyst output (process), and how to select tools and products that enable them to work as efficiently and effectively as possible (technology).

We’re all looking for insights that can be gleaned from our data. In addition to being data-driven, consider being insights-driven

Collecting more data doesn't always translate into better business outcomes. The best companies are shifting their entire culture to obsess about generating more insights that can be turned into tangible results in profits, revenue, and growth. Ugarte shares strategies for how teams can start to make the shift to being insights-driven and how to turn those insights into profitable decisions. Learn how to determine the ROI of your data and why more insights are the key to increasing the value of data. Look at your decisions as a process instead of one-off and start optimizing this internal process. Connect the dots in how data becomes an insight and then into a winning decision.

A key point about succeeding with digital transformation involves obtaining commercial data services and utilizing their capabilities to the fullest.

Acquiring commercial data services is not as simple as signing a standard contract without a preliminary and thorough investigation and an understanding of how to broker, evaluate, and integrate the data into a corporate or governmental data lake, data warehouse, or data mesh. Huffine calls your attention to the complexities of licensing, negotiations, usage, and ROI. Additionally, he covers environmental, financial, and core data (ZIP codes, GIS layers, etc.) as well as use cases for modeling, dashboards, research, and regulatory activities.

Some data is meant to be shared, but other data requires being secured so it doesn’t get into the wrong hands.

One of the biggest concerns of small, middle-sized, and enterprise companies involves securing data and ensuring integrity when collaborating or integrating with other services. Seasoned executives know that issues of IT security, compliance, regulations, cyber liability insurance, supply chain requirements, incident response, and forensics should all be addressed in the contracts. However, it is easier to identify contractual protections than to obtain them. Agreeing to contractual commitments is a function of both liability to the enterprise and negotiating leverage.

Challenges in licensing compliance have not diminished; in fact, they are more challenging than ever.

Unisphere’s report, “Managing the Software Audit: 2022 Survey on Enterprise Software Licensing and Audits Trends,” surmised that, due to lost revenue as a direct result of COVID-19, major software vendors increased the pace of their software licensing audits to generate additional revenue. Your risk of a software audit is greater now than at any point in time. Corey and Sullivan discuss the significant findings of the survey and explore the stealth audit, the newest tool in the vendor audit toolbox. They explain the difference between vendor policy and contractual obligation and expose how software licensing trolls have weaponized software vendor audits. Don’t be the next victim!

Legacy data architecture needs to be modernized to meet today’s data and analytics needs.

Capital One’s move to the cloud required it to modernize its data operations for this new environment. This meant learning how to balance the flexibility and efficiency of managing data in the cloud in order to generate the most value from its data. Bharathan shares more on the decisions Capital One made throughout this journey around monitoring, access, schema management, resiliency, cost, security, load patterns, and governance. He shares lessons learned—what worked, and what didn’t—and more on the tools Capital One developed to ensure a well-managed, well-governed cloud data platform.

Modern data architectures optimize for quick delivery at scale.

Zielnicki explores how Stitch Fix evolved its large suite of recommender models into a novel model architecture that unifies data from client interactions to deliver a holistic and real-time understanding of their style preferences. Stitch Fix’s Client Time Series Model (CTSM) is a scalable and flexible sequence-based recommender system that models client preferences over time, based on event data from various sources, to provide multi-domain, channel-specific recommendation outputs. The model has enabled Stitch Fix to continuously provide personalized fashion at scale, like no other apparel retailer.

DataOps affects everyone involved in the data ecosystem, which pretty much encompasses all employees, so adopting a strategy for agile data delivery is important.

Industry publications and thought leaders have been touting the benefits of composable design for both business and architecture. For roughly 10 years, Composable Analytics has been ahead of that curve. We were founded on using composable design strategies to get actual projects up and running and providing value for clients. Vidan shares some of the real-world lessons learned over that time and explores some of the more common usage patterns that Composable Analytics has found that help put theory to practice and composable architecture into production.

IDOL Unstructured Data Analytics is an advanced search, knowledge discovery, and analytics platform that uses AI and ML to leverage key insights stored deep within your unstructured data—including text analytics, audio analytics, video analytics, and image analytics. Some use cases include safe city analytics, law enforcement media analysis, open source intelligence, marketing and brand monitoring, brand and reputation protection, and digital assistant chatbot. Ciesliga and Drewry discuss some of the current major attack types and how to detect, protect, and recover. While they focus on recovery, they also highlight common data backup protection options with their advantages and drawbacks.

Tools to collect, organize, store, and analyze data are part of a modern data stack that can transform data.

At Rippling, a workforce management platform, a key part of its product is to enable customers to run automations and analytics on their HR/IT/finance data. Thus, its data stack is critical to making sure customers have the best experience when doing that. Li shares details about Rippling’s data architecture and discusses why the company chose Presto and Apache Pinot to underpin its data infrastructure. Today Presto powers the Rippling data platform and customer-facing scripting language, RQL (Rippling Query Language), to run arbitrary customer queries. Presto helps enable diverse, federated querying at scale.

Putting data first rather than taking an application-centric approach is the mark of a modern data architecture.

Emerging modern business-oriented data architectures such as data hub, data lake house, data fabric, and knowledge graphs put data first. By treating data as a supply chain, from which applications hang, as opposed to traditional application-centric approaches, where data is suborned into silos, Bentley explains how to solve complex data problems simply. Data unity, data security, data governance, data context, and data quality are all ensured throughout the data lifecycle without the complexity of multiple integrations from multiple vendors and components. Through real-world case studies, Bentley highlights the advantages of this approach, lessons learned, and practical advice in implementing these modern architectures that augment your existing data ecosystem without the need for “rip and replace.”

Data silos continue to impede access to needed information. Solutions are on the horizon.

With a 133-year history, Northern Trust has a backbone of IT infrastructure built decades ago, when on-premises solutions dominated the technology landscape. Due to the complexity of global regional regulatory requirements and the limitations of legacy systems, valuable data assets are maintained and isolated only in online transactional processing (OLTP) databases. The company faced challenges in data sharing, management, and governance in supporting enterprise-level analytics projects to meet business needs and growth. A digital modernization initiative took place that had a data mesh ecosystem as a critical component, leveraging cloud services on Azure and other modern technologies.

Enable your data team to get the most value from their time and quickly deliver needed business insights through a next-generation data fabric methodology.

MacWilliams introduces a technology agnostic methodology that solves the common challenges facing data teams and focuses on the processes among technologies—on-board data faster, flex automatically when data changes, create solutions that are manageable across technologies, and provide the foundation to be able to monitor and maintain your data fabric for the future. The methodology integrates with already existing technologies. Starting with the end in mind, MacWilliams covers how to best monitor and maintain your platform, how to maximize data team capacity, how to utilize meta-data to streamline your team’s development, and where to build custom and leverage modern technologies.

Moving to the cloud is now a normal function but presents interesting new challenges.

As companies look to scale, they face new and unique challenges related to data management in the cloud. Data mesh offers a framework and a set of principles that companies can adopt to help them scale a well-managed cloud data ecosystem. Learn how Capital One approached scaling its data ecosystem by federating data governance responsibility to data product owners within their lines of business and hear how companies can operate more efficiently by combining centralized tooling and policy with federated data management responsibility.

New technologies can transform companies’ data journeys.

Data fabrics and data meshes are promising paradigms for helping organizations on their data journeys. Data fabric is a new approach complementing the existing infrastructure and data management technology, accessing the data on demand as it’s needed by the consumers of the data, with centralized metadata and governance. Data mesh accesses the data on demand, providing the metadata and governance capabilities at the edges of the organization, where the data resides, enabling agility and autonomy throughout the organization. While much of the conversation around data fabrics and data mesh has been primarily about which approach or architecture is “better,” Fried discusses how the real value of these concepts isn’t rooted in an “either/or” approach and why they must be viewed as complementary.

Data architectures tend not to be static and new approaches are always welcome.

The concept of data mesh has resonated strongly with both data professionals and the broader engineering community. Loose coupling, enablement of federated development, and data sharing ease the difficulty of data management in both large and small organizations, as well as bringing data systems closer to parity with modern microservice-based systems. Cordo explores how the adoption of an event-based data architecture can enable an organization's sustainable transition to data mesh. This includes an overview of event-based architecture, architectural patterns for event-based data systems, and organizational considerations.

There’s no stopping the introduction of AI-based technologies into the enterprise.

Data science methods provide a means to establish analytic tradecraft, capable of managing a large amount of data, allowing for full characterization of actor behaviors, and providing valuable insights. As data volume increases, AI/ ML plays a significant role in this "high data entropy" space, providing users with the means to combine multi-sourced datasets, with the goal of learning and identifying patterns, and develop actionable insights while assuring they follow the organization’s law and policy boundaries. Rodriquez presents a case study on how the intelligence community (IC) is addressing these challenges by establishing innovative AI/ML governance and data management methodologies, supporting the development of a policy-compliant AI governance ecosystem, predicating strategies to enforce legal and policy considerations, and establishing data controls.

MLOps can streamline ML development, thus increasing operational effectiveness.

Jablonski looks at the journey to defining and implementing an MLOps solution for your organization. Jablonski begins with the metrics necessary for successful model lifecycle measurement, then discusses the technology stack to be deployed and the operational model necessary for success at scale. These three must all be defined and built collectively to ensure alignment between operational needs, technology capabilities, and success metrics. High model performance, elimination of bias, and predictability are all key elements of an MLOps strategy.

Neural networks can be used for many applications.

In 2022, BMW Group, Nvidia, and Microsoft teamed up to create the world's largest synthetic image dataset (800,000 images) for training AI in industrial environments. The dataset, called SORDI, was created using data from the BMW iFactory and rendered in Omniverse. The speakers demonstrate SORDI-next groundbreaking enhancements. Object classes in SORDI will no longer be static but will show signs of use and aging. They detail the benefits of these new features, especially with respect to the training of deep learning networks for industrial object recognition.

Knowledge graph technology expands by employing neural networks.

Probably the most important reason for building knowledge graphs has been to answer this age-old question: “What is going to happen next?” Given the data, relationships, and timelines we know about a customer, patient, product, etc. (“the entity of interest”), how can we confidently predict the most likely next event? Graph neural networks (GNNs) have emerged as a mature AI approach for knowledge graph enrichment. GNNs enhance neural network methods by processing graph data through rounds of message passing. Aasman describes how to use graph embeddings and regular recurrent neural networks to predict events via GNNs and demonstrates creating a GNN in the context of a knowledge graph for building event predictions.

Buying a home is one of the largest financial decisions people can make. Yet, the industry has gone relatively unchanged for decades. Opendoor is changing that by making hundreds of data-informed decisions every day through bespoke applications of AI, ML, and data science. The team uses everything from heavily parametrized, linear econometric models to deep neural networks. Drawing from Stone’s expertise in AI, ML, and data science, his talk focuses on developing a data-first approach to finance, the integration of algorithms and humans, and improving AI and ML from better data.

Technology is evolving, and many are understandably fixated on “the next big thing.” As enterprises increasingly recognize that business outcomes are limited less by technological capability and more by the comfort and confidence in adoption and impact, futurist Mike Bechtel suggests that, to arrive at our preferred tomorrows ahead of schedule, organizations need to focus on the element of trust in emerging technologies.

Geospatial datasets have long been difficult to join and make available in a single place. Businesses needed specialized resources, skill sets, and tools in order to prepare, clean, and transform the data before extracting value. Perhaps for this reason, only 26% of data strategy leaders today, according to Forrester, report that their organizations are utilizing location intelligence to its full potential. How can businesses go from resource-intensive geospatial data processes to fast-and-easy data unification, pattern detection, and operationalized AI/ML? Patel unveils a new geospatial technology that will transform how businesses extract value from location intelligence.

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As data science evolves, so does its power to affect many different functions.

Financial institutions have rich, customer-centric data and are in a strong position when building AI solutions. Like any other sector, the financial industry comprises different functions, and each function is uniquely responsible for catering to its customers. Since the functions differ, AI use cases also differ; hence, this presentation aims to touch on all those individual use cases. Using her broad data science experience across multiple industries, Kaur brings it alive in this presentation by sharing some best practices about data, modeling, and the right resources. As a bonus, she shares her thoughts on where the industry is heading and what the focus looks like in the coming years.

Data analysis is at its most powerful when it can be interpreted across many different types of data and points in time.

The fastest-growing types of data this decade are real-time geospatial and time series data fueled by the proliferation of location-enriched sensor data. Innovators are creating new design patterns to harness the unique characteristics of fast-moving spatio-temporal data and develop new data-driven products. This evolution to high-velocity geo-coded and time series data requires real-time analysis to understand spatial relationships over time. Learn about how the rise of real-time location intelligence and the power of extending IoT data to readings over time and space lead to new capabilities for analysis.

Organizational data literacy is a crucial part of being data-driven.

Prioritizing data quality is step one to becoming data-driven. However, what is step one to improving data quality? In most IT suites, the argument is better processes, better data fields, and better technology. Weinrich disagrees. Organizational data literacy is the most important step to truly achieving great data quality, data-driven decision making, and data management. Those fluent in data as a second language can identify and understand data sources, analyze them to generate insights, and use those insights to make better decisions that increase organizational value. Weinrich focuses on encouraging attendees to stop keeping data as a guarded gem in a warehouse, and to encourage a data-literate culture that highlights data democratization.

To function at a high level, maximizing its data, organizations need data science talent.

As the imperative to become more data-driven continues to permeate every aspect of business, the demand for data science talent (and more broadly, for data-literate employees across the spectrum) has exploded. The resulting talent pipeline challenge has led organizations to pursue both external hiring and internal upskilling. In many cases, organizations end up looking for data science “unicorns,” i.e., data science professionals qualified across the vast range of skills from computer science to machine learning, from cloud computing to data storytelling. Based on extensive research into the data science profession, Hamutcu presents a framework on key roles in data science and associated skills and knowledge.

Analytics cloud options balance the benefits of moving to the cloud with some challenges.

The ability for companies to have access to robust, real-time business intelligence (BI) and analytics functionality is more important now than ever before. Companies increasingly looking to migrate their BI and analytics capabilities to the cloud must now design, develop, and deliver data analytics in a different way. However, with so many cloud analytics options to choose from, how can businesses select the best option to match their unique needs and objectives—and what does the journey to get to the cloud look like?

No one wants to indulge in risky behavior when considering a move to the cloud.

Advantages to moving to the cloud include fast scalability, convenience across locations, and elasticity to handle changing workloads. But the more organizations in a wide variety of industries make the move to the cloud, the more we see them tripping over hidden land mines, such as out-of-control costs. In addition, platform lock-in can steal your options for shifting in the future as requirements change yet again. New regulations might restrict data location, or the requirements of your analytics might make more sense on-prem some places, or maybe you need to be on more than one cloud. What about having a high availability capability somewhere else?

The cloud is not a monolithic entity, but exists in many forms.

Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow, or PyTorch. Whether your data is distributed across multiple data centers and clouds, a successful heterogeneous data platform requires efficient data access. Palmer shares an architecture to overcome data silos, based on open source tools that simplify the adoption of the hybrid and multi-cloud for analytics and AI/ML workloads.

Database migration methodologies align with cloud and database trends.

Learn about the latest trends in database cloud modernizations, including the cloud-native databases offered by AWS and Azure. Agarwal has tips to help you pick the right database for your modern application and analytics needs, including use cases and comparing IaaS and PaaS databases.

DevOps is not yet without its challenges, but they are solvable.

Are you under pressure from your business to build, test, and release software changes faster? Is it unclear how to bring database processes into your DevOps infrastructure? Are you concerned about the risks of making compromises while shortening your release cycles? Are you wondering how to effectively monitor the performance impacts of your database changes? Are you required to replicate fast-moving changes to your hybrid database environments? If the answer is “yes” to any of these questions, then this presentation is for you! Future-proof your databases and achieve continuous database operations!

Manage hundreds of Apache Pinot Kubernetes Clusters inside end users’ cloud infrastructure.

When companies consider using SaaS (Software as a Service) products, they are often held back by challenges such as security considerations and storage compliance regulations. Those concerns often require that the data stays within the same virtual cloud owned by the company. StarTree built a modern data infrastructure based on Kubernetes so companies can keep their data inside their own infrastructure and at the same time get the benefits of using a fully managed Apache Pinot cluster deployed in the customer’s cloud environment. Dong and Mathur discuss the architecture, scalability, and automation aspect of the design and share operational experiences and lessons learned.

Open source databases are becoming more common, but how do they compare with proprietary databases?

Deployed, maintained, and scaled properly, open source databases can be one of the best decisions—both strategically and economically—that organizations make. But open source databases are not without challenges, and you have to know what to look for when evaluating open source alternatives. Will open source database resources and support match what you’ve come to expect from proprietary databases? Is there enough standardization? Can you switch between different databases and integrate them with existing software? Inamdar offers specific strategies for how and where to vet open source databases.

Integrating AI into business operations is accelerating, thanks to cloud computing.

The challenge in embedding AI into businesses involves how to do it in an agile, scalable, and cost-effective way. The organizations that will have a competitive advantage are those that take an end-to-end approach with hybrid cloud AI that includes both AI core infrastructure and AI edge infrastructure for easier management and rapid deployment. In this panel session, Voruganati and Kelleher speak to the latest technological advances and field learnings in scaling AI/ML workloads.

The promise of AI extends beyond business processes, having real impacts in healthcare.

Thalassemia and sickle cell disease are rare blood disorders that can have devastating effects on the patient. Both diseases are inherited abnormalities that impact the ability of hemoglobin to carry oxygen throughout the body. Advances in neural networks, combined with large volumes of rare disease healthcare datasets, now can successfully identify undiagnosed thalassemia patients and predict a sickle cell crisis with a high degree of precision.

Digital innovation is a corporate necessity but with the newest developments in AI-based technologies, how much do C-level executives and board members understand?

Before attempting to initiate digital improvements, your organization must improve its data and technology decision making. Aiken and Cesino provide executive insight on appropriate investments, key talent, and focused implementation. These permit organizations to rapidly realize concrete top- and bottom-line improvements directly attributable to maturing data practices as well as keeping regulators at bay. Balanced people and process investments allow your organization to best use valuable data and new technologies to achieve a sustained competitive advantage.

This panel of experts focuses not only on what has transpired during the conference but what we can expect going forward.

Generative AI, more intelligent machine learning, quantum computing, algorithmic determinations of behavior, and other technological advances are changing how we work and what we work with. This panel considers the implications of technology advances for good and bad.

With all the hype surrounding modern data architectures today, it’s harder than ever to discern what’s authentic from market noise. In order to guide people, Radiant Advisors and Database Trends and Applications designed and conducted a market survey in Q1 2023 that collected data and analyzed what companies are doing. The study focused on the perceptions, planning, and adoption of cloud architecture, the data lake house, data mesh, data fabric, streaming data architectures, and robust multi-modal analytics across industries and company sizes. See how your thoughts from this conference are validated and compared with the insights from this market study.