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  • How to Design a Data Lake with Business Impact in Mind

    A quarter of organizations surveyed by TDWI in 2017 say they already have a data lake in production, while another quarter say their lake will be in production within 12 months. Although data lakes are still rather new, user organizations have adopted them briskly. Why has the data lake gotten so popular, so fast? more

  • Data Architecture for IoT Communications and Analytics

    The Internet of Things (IoT) is an architectural paradigm combining an exploding number of different types of connected sensors and devices continuously generating and broadcasting data. The data can be processed to create integrated analytics models that can enhance and optimize new business initiatives. more

  • Modernizing Data Analytics: Moving Beyond Hadoop

    As an open source platform that simplified the ability to develop distributed and parallel applications, Hadoop lowered the barrier to entry for many smaller organizations interested in big data analytics. Some people have gone as far to suggest that Hadoop be used to replace their existing data warehouse. more

  • The Automation and Optimization of Advanced Analytics based on Machine Learning

    However, embracing machine learning successfully is challenged by ML’s serious data requirements. In development, designing an analytic model depends on very large volumes of diverse data. In production, an analytic model created via machine learning again needs voluminous data, so it can learn and improve over time. In turn, managing big data for machine learning demands a substantial data management infrastructure and tool portfolio. more

  • Ask the Expert: Demystifying Semantics and Ontologies
    TDWI Members Only

    We hear more and more about semantics these days, but what does it mean? What is an ontology and how does it relate to a data model? Do semantics and ontologies have a role to play in data architecture and data modeling? more

  • Six Strategies for Balancing Risk with Data Value

    Managing data for value is a business-oriented focus on the potential of data. It complements the all-too-common obsession with data’s technical requirements. Data value recognizes that data is a valuable business asset and should be leveraged accordingly. If you are managing data for value, your asset portfolio of data should be protected, grown, and governed. more

  • Ask the Expert about the Role of Data Visualization on Data Validation
    TDWI Members Only

    Data visualization has become a standard part of the business intelligence fair. It is now expected that a business intelligence team include a rich set of graphics in the tooling used across the business. In the real-time world, we are faced with the challenge of handling data streams directly from operational tools. This real-time data when presented visually tend to immediately skew to highlight outliers and exceptions in the data. more

  • Modern Data Warehouse Integration: Bringing Data Together in the Cloud

    As more organizations leverage hosted data warehouse environments and cloud-based reporting and analytics services, the challenges of data integration become more acute. In the past, data integration was straightforward: most of the data that flowed into the data warehouse originated well within the corporate firewall. Today, however, there is an increasingly varying mix of data sources, including on-premises data systems, cloud-based databases, externally-produced third-party data, as well as data sourced from software-as-a-service (SaaS) environments. The diversity of these sources contributes to growing complexity in bringing the data together; different data refresh rates, streaming cadences, and timing differences confound conventional staging and bulk load processes, leading to increased operational efforts at best, and inconsistent results at worst. more

  • Extending Your Data Warehouse Environment with Hadoop: Bringing Enterprise and External Data Together

    Surveys run by TDWI show that roughly a fifth of mature data warehouse environments now include Hadoop in production. Hadoop is becoming entrenched in warehousing because it can improve many components of the data warehouse architecture—from data ingestion to analytics processing to archiving—all at scale with a reasonable price. more

  • Ask the Expert on The UX Guide to Analytics
    TDWI Members Only

    Enterprise analytics spans a wide array of categories but they all have one thing in common, they require human interaction to realize value. However, much of that value is often left on the table. Factors such as user interviews, persona design, stakeholder buy in, wireframing, iteration, adoption and feedback are underutilized and greatly increase the risk of user disengagement and stakeholder frustration. Analytics managers and dashboard creators can miss the opportunity to leverage user motivations to drive success. more

  • Up to the Minute: The Need for Rapid Adoption of Streaming Data

    As Internet of Things (IoT) technologies become more common and web data grows in volume, there is growing evidence that the ability to analyze continuous data is not only valuable but necessary. In fact, those with the ability to capture and analyze massive numbers of independent continuous data streams will have a powerful capability that will help them to power operational intelligence and predictive analytics. A growing number of applications increasingly rely on fast analysis, but tomorrow’s world will be even more dependent on up-to-the-minute consumption of data streams. more

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