On Demand
As companies seek to gain competitive advantage by utilizing analytics, a change is occurring in terms of the data and infrastructure that supports it. A number of technology factors—including big data, Hadoop, and advances in analytics—are coming together to form the fabric of an evolving analytics ecosystem. Advanced analytics, in particular, are becoming more important as companies embrace big data. This includes techniques such as advanced visualization and machine learning that can be particularly beneficial in big data discovery and analysis.
Fern Halper, Ph.D.
Sponsored by
SAS
The complexity of data warehouse environments has increased dramatically in recent years with the arrival of data warehouse appliances, columnar database management systems, NoSQL databases, Hadoop, and tools for multiple forms of advanced analytics or real-time operation. The new vendor and open source platforms come in response to users’ growing demands for platforms optimized for various forms of big data, analytics, real-time operation, and the workloads that go with them.
Philip Russom, Ph.D.
Sponsored by
Actian, Cloudera, Datawatch, Dell EMC, Hewlett Packard Enterprise, MapR
Find out how your organization can achieve new advantages through the visualization and analysis of real-time data and event streams. Today, leading firms in industries such as financial services, healthcare, energy, telecom, manufacturing, government, and more are capturing insights from data and event streams and delivering real-time analytics for both human and automated decisions.
David Stodder
Sponsored by
Datawatch, IBM
Know your data. With today’s information-driven business projects, no maxim could be more true. Yet many organizations lack fundamental knowledge
about their data—and the situation is getting tougher as “big data” sources grow in size and variety and manual documentation efforts can’t keep pace.
Good data knowledge is critical to defining business objects such as customers and products within and across data sources. Clear understanding of data
assets, data relationships, and how sources map to target schema can be a vital business accelerator. Poor understanding leads to higher costs,
embarrassing mistakes, regulatory errors, and data quality problems that damage daily decision making.
David Stodder
Sponsored by
Dell EMC
There’s a fair amount of confusion about how best to collect, integrate, and preprocess data for the purposes of advanced analytics. Many business intelligence and data warehouse professionals think it’s the same as the traditional ETL practices they have applied to their report-oriented data warehouses for years. And some database administrators think it’s just a matter of dumping large volumes of data into a highly scalable repository.
Philip Russom, Ph.D.
Sponsored by
Liaison Technologies
Key BI industry growth areas are focused on big data, advanced analytics, cloud computing, and supporting mobile workers. When they are marketing and writing about using these technologies, vendors, the press, and analyst organizations usually focus on building new and leading-edge systems and applications.
Colin White
Sponsored by
TDWI and IBM Content
The cloud services model offers much in the way of potential benefits to businesses in terms of efficiency and cost savings. It’s no wonder that many enterprise applications have moved to public, private, or hybrid clouds. Although business intelligence applications have been slower to move to the cloud—usually because of data security concerns—this is starting to change.
Fern Halper, Ph.D.
Sponsored by
Tableau Software