TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Webinar

• Multiplatform Data Architectures

  A multiplatform data architecture (MDA) contains data distributed across multiple databases, open source or big data platforms, file systems, clouds, and other data platforms. An MDA is characterized by its large number and diversity of data persistence platforms, as well as its broad range of data structures, types, and containers. Equally important, however, is the MDA’s substantial data management infrastructure, which unifies the MDA’s architecture by integrating, synchronizing, cleansing, mastering, and documenting data across the MDA’s many platforms and beyond. more

• Modern Metadata Management: Boosting BI capacity with automation and machine learning

  Metadata management continues to be a powerful enabler for mission-critical data-driven business activities, including operations, analytics, and compliance. That’s because metadata is the golden thread that stitches together enterprise-wide landscapes, even those that are heavily distributed and heterogeneous, with hybrid mixes of on-premises and cloud systems. more

• New Practices in Data Cataloging

  Are you tired of starting with a blank slate every time you begin a new analytics assignment? That’s what happens when you spend your precious time researching the same data sources as last time and assembling yet another aggregated data set prior to doing what you really need to do. Creating a new analysis can have a positive business impact on your enterprise. more

• Getting Started with Data Integration in the Cloud

  Cloud continues to rise in importance as a platform for many IT systems, including those for data integration. Many organizations have now achieved a maturity level where they are using multiple cloud-based applications and online data sources. These users now need data integration tool platforms that support hybrid data environments so they can unify on-premises and cloud-based data sources and targets. Similarly, users increasingly need data integration processing to run natively on clouds (not just on premises), so that data integration functions and related capabilities are closer to software-as-a-service (SaaS) applications, Web data sources, multiple clouds, and increasingly popular cloud-based databases, data lakes, and data warehouses. more

• 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

• 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

• 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

• 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

• What It Takes to Be Data-Driven: Technologies and Practices for Becoming a Smarter Organization

  Gut instinct alone is not enough to enable decisions that will drive success. Most businesses today believe in the power of BI and analytics to help drive insight and value. TDWI research indicates that the vast majority of organizations are using technology such as visual analytics and BI dashboards to help them gain insight. However, gaining insight and using that insight to make decisions are often two different things. more

• Building a Successful Data Lake in the Cloud

  Data lakes on Hadoop have come on strong in recent years because they help many types of user organizations – from Internet firms to mainstream industries – capture big data at scale and analyze or otherwise process it for business value. more

• Location Analytics for Your Data Lake: Driving New Business Insights and Outcomes

  Location information has been a growth area in recent years in data management, as user organizations of many sizes and industries have realized how location information can inspire new business insights, practices, and outcomes. In response, many users have reworked older enterprise data environments to enrich the data with more location information. At the same time they have begun capturing data from new sources that include location information, especially from sensors, machines, devices, vehicles, and the Internet of Things (IoT). Much of this new data is being managed in data lakes, which in turn are usually deployed atop Hadoop. more