RESEARCH & RESOURCES

Featured Webinars

  • 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. February 27, 2018 Register

  • 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. March 6, 2018 Register

  • Delivering Trusted Data for a 360-Degree View: New Strategies for Governance and Master Data Management

    Nothing can ruin analytics faster than bad data. If users lose trust in the data, they will lose confidence in analytics and be unable to put insights into action. Organizations are facing new challenges with the rising demand of self-service analytics plus users’ growing interest in big and varied data sources. It’s a competitive advantage today for organizations if they can integrate and blend data to gain 360-degree views of customers and other subjects of interest. The tough question is: With democratization, new data, governance requirements, and business pressures to realize value, how do you ensure essential trust in the data? March 7, 2018 Register

Upcoming Webinars

  • 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. February 27, 2018 Register

  • 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. March 6, 2018 Register

  • Delivering Trusted Data for a 360-Degree View: New Strategies for Governance and Master Data Management

    Nothing can ruin analytics faster than bad data. If users lose trust in the data, they will lose confidence in analytics and be unable to put insights into action. Organizations are facing new challenges with the rising demand of self-service analytics plus users’ growing interest in big and varied data sources. It’s a competitive advantage today for organizations if they can integrate and blend data to gain 360-degree views of customers and other subjects of interest. The tough question is: With democratization, new data, governance requirements, and business pressures to realize value, how do you ensure essential trust in the data? March 7, 2018 Register

  • 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. March 8, 2018 Register

  • Modernizing Your Data Pipeline for Better Analytics

    Organizations are excited about analyzing ever-increasing amounts of disparate data, and they are often looking at advanced analytics to do so. Underpinning this move toward better insight and action is an evolving data infrastructure that is multiplatform in nature. TDWI sees increasing interest in the cloud, streaming platforms, and data lakes to support diverse data types for analysis. These platforms and others are coming together in modern data architectures that enable analytics and drive analytics success. March 13, 2018 Register

  • Applying the Power of AI to Data Catalogs: How artificial intelligence can make it easier for users to share knowledge and collaborate

    Data catalogs are increasingly critical as more users engage in self-service analytics and business intelligence and seek access to a wider variety of data types and sources. Many organizations fear that allowing users more self-service access and analysis will result in data chaos, especially if those sources exist outside IT-managed platforms such as an enterprise data warehouse. Data catalogs help organizations combat data chaos by enabling them to manage and govern distributed data assets more effectively. With an effective data catalog, organizations curate data so that users can trust sources and gain insights faster. March 20, 2018 Register

  • Building Trusted Data Through Deep Profiling and Analysis with Machine Learning

    Data environments are becoming increasingly complex. Many organizations are employing what TDWI terms a multiplatform data architecture to manage new and disparate data sources. This might include the data warehouse along with other platforms, such as a data lake or a streaming platform. The cloud might be an important part of this architecture as well. This data is often used to gain insights as organizations become more analytically sophisticated. In fact, analytics is the top driver for modern data architectures. March 28, 2018 Register

  • 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. March 29, 2018 Register

TDWI Webinars on Big Data, Business Intelligence, Data Warehousing & Analytics

TDWI Webinars deliver unbiased information on pertinent issues in the big data, business intelligence, data warehousing, and analytics industry. Each live Webinar is roughly one hour in length and includes an interactive question-and-answer session following the presentation.


On Demand

Making Predictive Analytics Work – 5 Keys to Successful Model Deployment and Management

Organizations are excited about predictive analytics and machine learning for a number of reasons. Companies want to better understand customer behavior. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing.

Fern Halper, Ph.D.


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.

Nicholas Kelly


Enabling Data Science to Be Data Science: Strategies for Increasing Self-Service Data Science

Data science offers great potential for what it can contribute to business strategy and operations—that is, if data scientists are actually able to do data science rather than spend most of their time on data management and preparation. TDWI finds that most data science projects spend the majority of time on these areas rather than on development of analytics, models, and algorithms. To increase business value, organizations need solutions that will flip this ratio.

David Stodder


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.

Fern Halper, Ph.D., David Stodder


Evolution of the Data Lake—Implementing Real-Time Change Data in Hadoop

A ten-fold increase in worldwide data by 2025 is one of many predictions about big data. With such growth rates in data, the “data lake” is a very popular concept today. Everybody touts their platform capabilities for the data lake, and it is all about Apache Hadoop. With its proven cost-effective, highly scalable, and reliable means of storing vast data sets on cost-effective commodity hardware regardless of format, it seems to be the ideal analytics repository. However, the power of discovery that comes with the lack of a schema also creates a barrier for integrating well-understood transaction data that is more comfortably stored in a relational database. Rapidly changing data can quickly turn a data lake into a data swamp.

Krish Krishnan


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.

David Loshin


Ask the Expert on the Roles and Construct of a Thriving Analytic Team
TDWI Members Only

Most organizations believe they will achieve better analytic results if they populate a deeper bench of experienced data scientists and machine learning practitioners. But this is akin to building a home exclusively with highly skilled framers, brick layers and cabinet makers. You’ll end up with a solid structure and great workmanship, but not a true functional home.

Keith McCormick


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