On Demand
The Internet of Things (IoT) is hot and getting hotter. Consumers use it for health monitoring and “smart” home devices, such as thermostats and appliances. On the business front, a piece of equipment—or any business asset, really—can be tagged, monitored, and analyzed. This might include a sensor-enabled pressure valve on a piece of drilling equipment, a tagged piece of construction material, food moving to market, or a chip placed in an employee badge, not to mention smart cities, smart power grids, and more.
Fern Halper, Ph.D.
Content Provided by
IBM, Teradata, Tibco Spotfire
Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever as they support operationalized analytics and wring business value from machine data and other new forms of big data. In the age of big data analytics, it’s important to modernize a DW environment to keep it competitive and aligned with business goals.
Philip Russom, Ph.D.
Sponsored by
SAP
Cloud computing is a major trend that offers advantages in terms of flexibility, dynamic scalability, and agility. Even so, there’s been a lot of marketing hype. The reality is that, until recently, cloud has been slow to take off for business intelligence (BI) and analytics. Organizations have been concerned about security, performance, functionality, and other critical issues. TDWI Research is now seeing a significant shift as more organizations show willingness to experiment with BI and analytics in the cloud and are moving into deployment.
Fern Halper, Ph.D., David Stodder
Sponsored by
Cloudera, IBM, Informatica Corporation, SAP, SAS, Snowflake, Tableau Software
Big data presents significant business opportunities, when leveraged properly. And yet, big data also presents significant business and technology risks, when it is poorly governed or managed.
Philip Russom, Ph.D.
Sponsored by
Talend
One of the strongest trends in information technology (IT) today is self service, which puts the power of creating data-driven solutions in the hands of the business user. This way, IT organizations are offloaded; they needn’t create unique datasets, reports, and analyses per user, which frees up IT’s time for other tasks. Furthermore, a broad range of end-users – mostly mildly technical business people – needn’t wait for help from IT, thereby giving them greater agility and creativity, while reducing the time to value and allowing them to apply their business expertise to a well-targeted solution. Therefore, self service is a win-win situation – but only if key pieces of technology are in place.
Philip Russom, Ph.D.
Sponsored by
Bit Stew Systems
One of the hottest trends today is self-service data preparation. Following the path of front-end tools for self-service business intelligence (BI) and visual analytics, self-service data preparation is aimed at providing nontechnical business users with the ability to explore data and choose data sets to fit their BI and analytics requirements. The goal is to reduce IT hand-holding—an ambitious one considering that, according to TDWI research, in most organizations IT manages nearly all data preparation steps, which can include data ingestion and collection, data transformation, data quality improvement, and data integration. Self-service data preparation thus represents a significant and potentially destabilizing change for IT and the way that IT and business work together.
David Stodder
Sponsored by
SAP and Intel
As the data warehouse environment (DWE) continues to evolve, one of its strongest trends is the diversification of data platforms. A rigorously structured relational data warehouse is still at the heart of the DWE, but it is being joined more and more by other platform types, including data platforms based on columns, appliances, graph, streaming data, and open source.
Philip Russom, Ph.D.
Sponsored by
SAP