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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  • 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

  • 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

  • Seven Strategies for Achieving Big Data Analytics Maturity

    Big data analytics is full of potential – but also fraught with pitfalls, obstacles, and a fog of hype surrounding the technologies. To be successful, organizations need to know where to begin with big data analytics and how to sustain progress so that they can achieve objectives. With key strategic initiatives hinging on success with big data analytics – including developing competitive innovations in customer intelligence and engagement, fraud detection, security, and product development – organizations need a roadmap for how to move ahead. more

  • Making Data Preparation Faster, Easier, and Smarter

    Business users, business analysts, and data scientists have diverse data needs and specialties, but they all have one thing in common: they are tired of long, complicated, and tedious data preparation. Unfortunately, data preparation is getting even more difficult as users doing analytics and data discovery reach out to larger volumes of different types of data. more

  • BI, Analytics, and the Cloud: Strategies for Business Agility

    Cloud computing is a major trend that offers advantages in terms of flexibility, dynamic scalability, and agility. Even so, there’s been a lot of marketing hype. The reality is that, until recently, cloud has been slow to take off for business intelligence (BI) and analytics. Organizations have been concerned about security, performance, functionality, and other critical issues. TDWI Research is now seeing a significant shift as more organizations show willingness to experiment with BI and analytics in the cloud and are moving into deployment. more

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    Upcoming TDWI Events

    Conferences, Leadership Summits, Seminars, and Bootcamps

    • Accelerate TDWI Boston Accelerate TDWI Accelerate Boston

      April 3-5, 2017
      REGISTRATION CLOSES MAR 31

      ACCELERATE brings together the brightest minds in data to share their expertise and insight on the future of data science and analytics. From sessions on core data science skills, to learning how to use new big data tools such as R, Python, and Spark, to talks on the latest trends in machine learning, predictive analytics and artificial intelligence, attendees will learn from industry experts, receive valuable training, and network and share ideas with their data peers in an exciting and collaborative environment.

    • Conference TDWI Chicago Conference

      May 7-12, 2017
      EARLY BIRD DEADLINE APR 7

      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.