On Demand
Self-reliant and less dependent on IT, business executives and departmental/LOB managers and users are deploying the latest tools, services, and applications for business intelligence, analytics, and data discovery. Although IT is hardly disappearing from the picture, IT needs to adjust how it manages access to data sources and governs BI and analytics. Enterprise BI/DW systems also need to accommodate how users customize their BI and analytics as they see fit based on their roles. As BI and analytics tools become easier to use and more flexible, the trend toward business users directing their own BI and analytics experiences will accelerate.
David Stodder
Sponsored by
Actian, Datawatch, Looker, SAS, Tableau Software, Treasure Data
Predictive analytics has finally hit the mainstream as organizations realize its value and how it can help them become more competitive. The technology has also become easier to use. In fact, a current trend in predictive analytics is improving ease of use so that analysts supporting functions such as sales, marketing, and finance can use more sophisticated software.
Fern Halper, Ph.D.
Sponsored by
Actuate - now OpenText
Integrating and transforming data for business decision making has always been a complex and resource intensive task. The industry move toward the use of cloud, mobile and big data technologies makes this task even more difficult given the heterogeneous nature of the many systems involved. This heterogeneity coupled with the need for companies to make faster and often close to real-time decisions requires organizations to modernize their data integration frameworks to integrate data not only at the database and file system level, but also at the application and business process level.
Colin White
Sponsored by
TDWI and IBM Content
Significant changes are afoot in data warehouse environments (DWEs). This is because organizations are evolving their DWEs so they can leverage big data for business value, practice advanced forms of analytics for new insights, scale up to larger user communities, enable self-service data exploration, and operate the business more competitively based on real-time and near-real-time data.
Philip Russom, Ph.D.
Sponsored by
TDWI and IBM Content
Defining the term data warehouse is getting more difficult. Many of the recent technology innovations in software and hardware have enabled a new generation of data warehouse architectures. In-memory processing coupled with today’s faster hardware gives new data warehouse architectures greater speed and scale.
Philip Russom, Ph.D.
Sponsored by
SAP
Data scientists are in high demand and companies are frantically looking for ways of overcoming the costs and shortage of experienced data science talent. To help solve this problem, both established and new start-up vendors are introducing products that claim to enable business users to do advanced and predictive analytics without the assistance of a data scientist.
Colin White
Sponsored by
Tableau Software
Agile development methods—as applied in data disciplines—have experienced strong adoption by users in recent years, and for good reason. As more organizations “run the business” based purely on data (and compete and innovate), data management professionals are under increasing pressure to deploy data solutions into business use sooner, produce multiple solutions, and align data solutions with quickly evolving business goals. Hence, delivery speed, development productivity, and business alignment are the leading priorities (and benefits) for agile data management.
Philip Russom, Ph.D.
Sponsored by
Dell EMC