RESEARCH & RESOURCES

Featured Webinars

  • 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. January 23, 2018 Register

  • 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. January 25, 2018 Register

  • 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. February 8, 2018 Register

Upcoming Webinars

  • 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. January 23, 2018 Register

  • 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. January 25, 2018 Register

  • 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. February 8, 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

Engaging the Business: Agile, Collaborative Approaches to Data Usability

The term “data-driven” has become an accepted principle for modern organizations, but to drive modern, agile businesses, each data consumer’s view of enterprise data must both align with individual data quality and usability criteria and remain consistent with other data users in the organization. While traditional data quality/data preparation tools were intended to ensure accuracy and trust, the conventional wisdom centered on a technical, IT-centric usage model.

David Loshin


Your Data Warehouse Is Not Alone: Now It Has Friends in the Extended Analytics Architecture

Analytics are today’s business weapon of choice. Changing business environments and competitive pressures have driven companies to seek a new edge from innovative technologies such as Hadoop, specialized data stores, and the cloud. This expanding and constantly evolving set of data sources means the enterprise data warehouse can no longer be the singular physical location for all large-scale information management.

Claudia Imhoff, Ph.D.


Leveraging Data Governance to Align and Operationalize Business Policies

There is growing awareness that for practitioners to effectively manage data as an asset to the business, that data must not be simply collected and moved between systems, but must be validated to ensure the level of trust that the data is fit for its various downstream purposes. This demands conformance to business rules that accurately reflect meeting the needs of defined business policies. Understanding business policies, transforming them into data rules, and implementing those rules is the process of data governance. Organizations whose understanding enables their ability to effectively govern their data are gaining business advantage as they leverage data quality, metadata, and data governance tools to translate business policies into consistent, useful data.

David Loshin


Adaptable, Intuitive, and Faster: Key Trends Shaping the Future of Business Intelligence

Business intelligence (BI) sits at the center of many organizations’ efforts to enable data-driven decisions and actions through their enterprises. But BI is changing, both for organizations just getting started with BI and those that have invested in developing an enterprise standard. We have entered the age of BI “democratization”: tools and applications are becoming easier to use, more visual, and more adaptable to the requirements of a greater variety of users. Across business functions, users are excited by the potential of the new BI and visual analytics technologies and are clamoring for the opportunity to move beyond the limits of spreadsheets and canned reporting. However, a balance must be struck because no organization wants BI democratization to devolve into BI chaos.

David Stodder


Agile Approaches to Data Warehouse Modernization

The conventional approach to data warehousing may satisfy conventional reporting and straightforward analytical needs. Yet outside of the enterprise data warehouse, the information world has rapidly evolved and changed – there are new data sources, streaming different kinds of data, all coming at faster speeds. While we trust our existing data warehouse platforms to meet existing business needs, how can we integrate new technologies to address new business challenges without disrupting the consumers who rely on the trust and security of the established reporting and analysis platforms and applications?

David Loshin


Predictive Analytics Meets the IoT: Harnessing the Opportunity, Overcoming the Challenges

The Internet of Things (IoT)—a network of physical objects accessed through the Internet—has big implications for both business and consumers. From data center environmental sensors to remote asset tracking, the IoT can help improve business processes and create new value. TDWI research indicates growing excitement around the IoT and machine data.

Fern Halper, Ph.D.


IoT Analytics- Analytics at the Edge

The Internet of Things - a network of connected physical objects that can send and receive data over the Internet—is a hot market topic. It’s about connecting devices, sensors, electronics and more. It’s exciting, innovative, and important. The network itself is a big trend, but the analytics that can be performed over this data will be where the value lies.

Fern Halper, Ph.D.


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