DATA MANAGEMENT

The technologies, architectures, and practices needed to manage data as a critical enterprise asset. It is a broad field, within which there are specialized disciplines.

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  • Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment

    One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs). more

  • Data Management for Big Data, Hadoop, and Data Lakes

    A perfect storm of data management trends is converging. First, organizations across many industries are experiencing the big data phenomenon, which forces them to capture and leverage data from new sources, in structures and velocities that are new to them, in unprecedented volumes. Second, technical users are scrambling to learn new data platforms like Hadoop and their evolving best practices. Third, the data lake arose suddenly in 2016 as the preferred approach to managing very large repositories of raw source data. Fourth, business managers have attained a new level of sophistication in their use big data for business value and organizational advantage. more

  • Between a Rock and a Hard Place: How to Modernize Legacy Middleware for an Evolving, Data-driven World

    In support of daily operations, many organizations depend heavily on systems for enterprise application integration (EAI), enterprise service bus (ESB), and other approaches to middleware. Yet, these infrastructures are today legacy technologies that predate the rise of big data and unstructured data, as well as modern sources and targets for integration, such machines, devices, clouds, social media, and the Internet of Things (IoT). Furthermore, many middleware vendor tools are still optimized for the on-premises ERP-dominated applications world of twenty years ago; others are in legacy mode, with no future upgrades coming. more

  • Database Strategies for Modern BI and Analytics

    The data universe has changed. Big data, cloud computing, and open source have dramatically expanded the number of data warehousing offerings available to today’s businesses. An increasing number of companies are implementing self-service business intelligence (BI) and visual analytics tools to access and make sense of all of the new and diverse sources of data their teams are consuming. Data literacy is changing equally fast as an increasing number of “data consumers” want to interact with data on their own rather than through IT. more

  • Data Lakes: Purposes, Practices, Patterns, and Platforms

    We’re experiencing a time of great change, as data evolves into greater diversity (more data types, sources, schema, and latencies) and as user organizations diversify the ways they use data for business value (via advanced analytics and data integrated across multiple analytic and operational applications). To capture new big data, to scale up burgeoning traditional data, and to leverage both fully, users are modernizing their portfolios of tools, platforms, best practices, and skills. more

  • Creating Value with Unified Governance

    Certainly, every business leader wants to have trusted, secure, consistent and usable information. But data volumes and systems complexity has been increasing for years and most organizations rarely prioritize data governance, so why care now? We’re at the brink of a perfect storm of unprecedented IT megatrends. The convergence of Cloud, Social, Mobile and Big Data foreshadows the upcoming tsunami of data ripe with potential business value. But it will also make the frustrating complexity of your traditional on-premises transactional data management challenges appear amazingly “manageable” in contrast. more

  • Take a Dive into the Data Lake

    Many organizations have a serious interest in data lakes, at the moment, because of the business analytics and new data-driven practices that lakes promise. Yet, these organizations still aren’t quite ready to take a dive into a data lake. Whether they are unable to define standard structures, align and maintain business meanings, or create a governance strategy, these companies struggle to anticipate what truly lies beneath the surface of the data lake. more

  • Achieving Integration Agility, Scale, and Simplicity via Cloud-Based Integration Platform-as-a-Service

    Many firms have mandates to move to clouds, control IT costs, integrate disparate applications, deliver data-driven solutions faster, and provide integration infrastructure for hybrid data ecosystems. more

  • Accelerating the Path to Value with Hybrid Analytics Architecture

    In today’s demanding economic environment, companies that can develop and deploy analytics faster have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to finally unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance issues, and more, organizations should consider a hybrid analytics architecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands. more

  • Emerging Design Patterns for Data Management

    Organizations that seek to be data-driven are experiencing considerable change of late, because data itself, the management of data, and the ways businesses leverage data are all evolving at accelerated rates. These changes sound like problems, but they are actually opportunities for organizations that can embrace new big data, implement new design patterns and platforms for data, scale to greater volumes and processing loads, and react accordingly via analytics for organizational advantage. more

  • Dynamic Metadata: Enabling Modern BI Architecture

    In a highly competitive market, today’s forward-looking organizations are seeking to optimize and modernize their IT investments, specifically in enterprise business intelligence (BI). There’s a strong push to capitalize on newer features such as self-service BI, advanced analytics, and customized visualizations—all of which relinquish the centralized data governance necessary for corporate and regulatory compliance. more

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    Upcoming TDWI Events

    Conferences, Leadership Summits, Seminars, and Bootcamps

    • Conference TDWI Chicago Conference

      May 7-12, 2017
      REGISTRATION DEADLINE MAY 5

      TDWI Chicago addresses our greatest data challenges head-on: Data streaming, enriching your data lake with new information sources, and connecting to spectrum of IoT. You will leave TDWI Chicago’s 6-day in-depth conference with the skills and insights to design, build and analyze your organization’s data.

    • Conference TDWI Anaheim Conference

      August 6-11, 2017
      SUPER EARLY BIRD CLOSES JUNE 16

      All of the data in the world is useless unless you can effectively analyze and present the data to tell a story about what has happened, and what is likely to take place in the future. TDWI Anaheim brings together the experts in the big data and analysis space to share their insights about data analysis, visualization and storytelling in a proven learning environment.