RESEARCH & RESOURCES

Featured Webinars

  • Achieving Business Value Using Hybrid Analytics

    As companies progress in their analytics efforts, they often look to leverage a hybrid cloud analytics model—one where data from both on-premises and cloud sources is analyzed seamlessly. This approach makes sense especially when analyzing data from diverse sources using more advanced analytics such as machine learning and predictive analytics. Data that is generated both in the cloud and on-premises often needs to be analyzed together. June 20, 2018 Register

  • Practical Predictive Analytics – Results of New TDWI Best Practices Research

    Predictive analytics is now part of the analytics fabric of organizations. TDWI research indicates that it is in the early mainstream phase of adoption. Yet, even as organizations continue to adopt predictive analytics and machine learning, many are struggling to make it stick. Challenges include lack of skills, executive and organizational support, and data infrastructure issues. June 21, 2018 Register

  • Modern Data Architectures to Support Modern Analytics

    Many organizations today are scrambling to meet the needs of new data types and analytics. TDWI research shows that companies are often analyzing data from multiple sources, including structured data, unstructured data, real-time streaming data, location data, and transactional data. They are making use of new techniques such as text analytics and machine learning, and they are moving towards self-service analytics. The traditional data warehouse or data mart is often limited in its ability to support these modern analytics in a fast and friendly way. July 12, 2018 Register

Upcoming Webinars

  • Achieving Business Value Using Hybrid Analytics

    As companies progress in their analytics efforts, they often look to leverage a hybrid cloud analytics model—one where data from both on-premises and cloud sources is analyzed seamlessly. This approach makes sense especially when analyzing data from diverse sources using more advanced analytics such as machine learning and predictive analytics. Data that is generated both in the cloud and on-premises often needs to be analyzed together. June 20, 2018 Register

  • Practical Predictive Analytics – Results of New TDWI Best Practices Research

    Predictive analytics is now part of the analytics fabric of organizations. TDWI research indicates that it is in the early mainstream phase of adoption. Yet, even as organizations continue to adopt predictive analytics and machine learning, many are struggling to make it stick. Challenges include lack of skills, executive and organizational support, and data infrastructure issues. June 21, 2018 Register

  • Modern Data Architectures to Support Modern Analytics

    Many organizations today are scrambling to meet the needs of new data types and analytics. TDWI research shows that companies are often analyzing data from multiple sources, including structured data, unstructured data, real-time streaming data, location data, and transactional data. They are making use of new techniques such as text analytics and machine learning, and they are moving towards self-service analytics. The traditional data warehouse or data mart is often limited in its ability to support these modern analytics in a fast and friendly way. July 12, 2018 Register

  • 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? July 24, 2018 Register

  • Achieving High-Value Analytics with Data Virtualization

    Analytics projects are critical to business success, and as a result, they are growing in size, number, complexity, and perhaps most important, in their data requirements. TDWI finds that data scientists, business analysts, and other personnel need to view and access data that resides in multiple sources, both on premises and in the cloud, to draw insights from data relationships and discover important patterns and trends. July 31, 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

Improving Data Preparation for Business Analytics

Data preparation is a hot topic today because modern technologies and practices are finally giving users and IT an alternative to traditionally slow, manual, and tedious steps for getting data ready for business intelligence (BI) and analytics. Data preparation covers a range of processes that begin during the ingestion of raw, structured, and unstructured data. Processes are then needed to improve data quality and completeness, standardize how it is defined for communities of users and applications, and perform transformation steps to make the data suitable for BI and analytics.

David Stodder


Accelerating Analytic Insights via the Hybrid Cloud

More often, organizations are looking to the cloud for analytics. The cloud can provide flexibility, elasticity, and convenience. Organizations are using the cloud for a range of business use cases from reporting and sandboxes to production and IoT analytics, and much more. Cloud analytic services offerings are evolving too and becoming more popular – especially with business customers. As a Service (aaS) offerings can target specific subject areas such as churn-detection-as-a-service or fraud-detection-as-a-service. These can help to jump start improved business outcomes much faster than in-house efforts.

Fern Halper, Ph.D.


Data Preparation for the Rest of Us!

Data preparation for analytics used to reside solely within the IT teams with savvy technical resources. With businesses leaning towards self-service analytics, business analysts and data scientists need data prepared their way on their schedule, not based on IT availability, to drive business forward. Data preparation does not replace traditional data integration or ETL but is complementary to existing business intelligence solutions and allows the business user to easily access the integrated data and combine it with other sets of data thereby realizing the ROI on your BI and analytics investment beyond what your IT teams can deliver.

Claudia Imhoff, Ph.D.


Agile, Fast, and Flexible: Five BI and Data Management Strategies for Meeting New Business Challenges

A signature quality of leading companies is their ability to generate data-driven insights quickly so that they can proactively shift strategies to take advantage of new opportunities. They use data to learn sooner how customer preferences are changing, how to adjust when markets are shifting, and how they can reduce inefficiencies in operations so that resources are deployed the right way.

David Stodder


Getting to the Next Level with Visual Analytics and Governed Data Discovery

Dull reports and static bar charts are old news: Business users today are excited about modern visual analytics, data discovery, and intuitive business intelligence. Tools, applications, and cloud-based services are making it easier for users to derive powerful, actionable insights from a widening array of data. Users across organizations may finally have an alternative to limited spreadsheets and BI reports – and to waiting in IT’s backlog for developers to give them what they need.

David Stodder


Delivering Business Value Faster with Visual Data Discovery

Hidden inside data are insights that could change the game for your business – that is, if your decision makers can discover and apply them in time to make a difference! Nothing is more frustrating to business users than having to wait out long IT development cycles for business intelligence (BI) tools and data warehousing systems just to gain access to the data they need right now. Fortunately, with the advent of visual analytics and discovery tools, the journey to data insight is getting easier and faster. Cloud computing is accelerating time to business value even further by giving organizations the option of bypassing the delays and difficulties of on-premises deployment.

Fern Halper, Ph.D., David Stodder


When Worlds Collide: Using the Data Lake to Connect Old and New Technologies

Legacy information technology environments usually consist of aging components, typically acquired over time to address specific business needs. While these systems met past needs, emerging opportunities and business pressures have motivated organizations to consider innovative data management technologies.

David Loshin


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