METHODOLOGIES & STRATEGIES

The processes and approaches to deliver business value through data and analytics. Includes a strong focus on leadership strategies and organizational management to maximize data management and analytics impact.

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  • Rethinking Enterprise BI in a Self-Service World: Balancing User Freedom with Enterprise IT Responsibilities

    Both product and tech leaders have always recognized that business intelligence (BI) is most valuable when it is pervasive, contextual, and actionable. A new generation of solutions -- embedded BI – provides unprecedented power to weave reporting and analytics into the fabric of apps and business processes. 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

  • Big Data Management Best Practices for Data Lakes

    Organizations are pursuing data lakes in a fury. Organizations in many industries are attempting to deploydata lakes for a variety of purposes, including the persistence of raw detailed source data, data landing and staging, continuous ingestion, archiving analytic data, broad exploration of data, data prep, the capture of big data, and the augmentation of data warehouse environments. These general design patterns are being applied to industry and departmental domain specific solutions, namely marketing data lakes, sales performance data lakes, healthcare data lakes, and financial fraud data lakes. 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

  • The What, Why, When, and How of Data Warehouse Modernization

    Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever as they support operationalized analytics and wring business value from machine data and other new forms of big data. In the age of big data analytics, it’s important to modernize a DW environment to keep it competitive and aligned with business goals. more

  • Enabling Self-Service Analytics with Intelligent Data Integration

    One of the strongest trends in information technology (IT) today is self service, which puts the power of creating data-driven solutions in the hands of the business user. This way, IT organizations are offloaded; they needn’t create unique datasets, reports, and analyses per user, which frees up IT’s time for other tasks. Furthermore, a broad range of end-users – mostly mildly technical business people – needn’t wait for help from IT, thereby giving them greater agility and creativity, while reducing the time to value and allowing them to apply their business expertise to a well-targeted solution. Therefore, self service is a win-win situation – but only if key pieces of technology are in place. more

  • Business, IT, and Self-Service Data Preparation: Can We Talk?

    One of the hottest trends today is self-service data preparation. Following the path of front-end tools for self-service business intelligence (BI) and visual analytics, self-service data preparation is aimed at providing nontechnical business users with the ability to explore data and choose data sets to fit their BI and analytics requirements. The goal is to reduce IT hand-holding—an ambitious one considering that, according to TDWI research, in most organizations IT manages nearly all data preparation steps, which can include data ingestion and collection, data transformation, data quality improvement, and data integration. Self-service data preparation thus represents a significant and potentially destabilizing change for IT and the way that IT and business work together. more

  • Land O’Lakes: How Free-Form Data Lakes Are Complementing Structured Data Warehouses

    As the data warehouse environment (DWE) continues to evolve, one of its strongest trends is the diversification of data platforms. A rigorously structured relational data warehouse is still at the heart of the DWE, but it is being joined more and more by other platform types, including data platforms based on columns, appliances, graph, streaming data, and open source. more

  • Harnessing the Power of Embedded Analytics for Financial Services

    Firms in financial services and many other industries are under pressure to improve efficiency, productivity, and decision-making power. For both daily operational and strategic decisions, organizations need to draw insights quickly from quality data so that they can understand and act on changes in markets, regulations, operations, customer behavior, fraud patterns, and the competitive landscape. Financial services firms need to be smarter and faster to survive in an industry where business models are changing and old ways of managing risk are out of date. more

  • Data Warehouse Modernization and Analytics for the Digital Enterprise

    More and more, organizations want to base decisions on facts, have complete views of customers, manage operations by the numbers, predict and plan strategically, and compete on analytics. As a foundation for achieving these goals, organizations need a modern infrastructure for data warehousing and business analytics. more

  • Faster BI for the Masses: How Search Can Make Analytics More Accessible

    Business intelligence is critical to getting answers from data, but for many users it is also a huge source of frustration. Since its beginning, the mission of BI has been to make it faster and easier to locate the right data, query it, and return meaningful answers for reporting and analysis. Newer data visualization and discovery tools have improved the user experience, and data warehouses and data lakes have added terabytes to the data within reach. Yet, it still can be a slow and difficult process to get to the most relevant data without help from technical experts. Users often have to wait for their answers and unless the technical experts also have a strong understanding of the business, the answers are usually inadequate—and the process starts all over again. 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.