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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Onsite Education

  • TDWI Data Quality Fundamentals

    Most organizations have persistent and long-standing data quality problems—troubles that grow and propagate with the challenges of data redundancy, purchased applications and databases, legacy databases, multiple data providers and consumers, missing documentation, and uncertainty in defining data quality. more

  • TDWI Master Data Management Fundamentals

    Top-performing businesses need high-quality, low-redundancy reference data. You can’t manage a supply chain with disparate and unreliable product and customer data, service your customers effectively with inconsistent customer views, or confidently report to stockholders when financial data is in disarray. more

  • TDWI Data Integration Techniques: ETL and Alternatives for Data Consolidation

    Data integration is becoming increasingly complex as new expectations and technologies change the face of data warehousing and business intelligence. Design of data integration systems was comparatively straightforward when extract, transform, and load (ETL) was the only option. more

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  • 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

  • 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

  • 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

  • Marketing Analytics Meets Artificial Intelligence

    The world of marketing and the world of advanced analytics have been winding towards each other for years. TDWI research indicates that marketing is often one of the first areas in an organization that makes use of advanced analytics. Marketers understand the value that analytics can provide to understand customers and the customer journey. Marketing analytics provides insight gathered from data analysis that can make marketing more efficient and effective. 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

  • Streaming Analytics for Real-Time Action – Best Practices for Getting Started

    More often, organizations are realizing that analyzing data in motion- i.e., data that arrives continuously as a sequence of instances- can provide substantial business value. This data comes from sensors, social media feeds, traffic feeds, and much more. TDWI has seen growing interest in event stream processing as well as the real-time, continuous analysis of streaming data. more

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    Upcoming TDWI Events

    Conferences, Executive Summits, Seminars, and Bootcamps

    • Conference TDWI Las Vegas Conference

      February 12-17, 2017

      TDWI Las Vegas addresses our greatest data challenge head-on: harnessing the power of data and analytics to extract high-value insights that enable faster, smarter business decisions. Analytics requires a team with skills across a spectrum of disciplines. At TDWI Las Vegas, you will learn these vital skills from architecture, data management, and data preparation to data analysis, visualization, data storytelling, and more.

    • Accelerate TDWI Boston Accelerate

      April 3-5, 2017

      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.