On Demand
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
Many end-user organizations are currently commencing or expanding solutions for big data and big data analytics. These organizations want to understand how to approach big data and where they stand relative to other companies, especially their competitors. In late October 2013, TDWI launched its Big Data Maturity Model Assessment Tool, which can help to guide IT and business professionals on their big data journey. The assessment looks at companies across five dimensions that impact maturity, including organization, infrastructure, data management, analytics, and governance.
Fern Halper, Ph.D., Krish Krishnan
Content Provided by
TDWI, IBM, Cloudera, MarkLogic, Pentaho
Predictive analytics is quickly becoming a decisive advantage for achieving desired business outcomes, including higher customer profitability, stickier websites, more relevant products and services, and more efficient and effective operations and finances. Predictive analytics involves methods and technologies to help organizations spot patterns and trends in data, test large numbers of variables, develop and score models, and mine data for unexpected insights. Sources for predictive analytics are expanding to include machine data and semi-structured and unstructured data, making it important to include text analytics and mining in technology portfolios.
Fern Halper, Ph.D.
Sponsored by
Birst, Actuate - now OpenText, Alteryx, Pentaho, SAP, Tableau Software
More and more, companies are looking to a variety of data types and new forms of analysis in order to remain competitive. Geospatial data is emerging as an important source of information, both in traditional and big data analytics. Companies are using geospatial data and geospatial analytics in applications ranging from marketing to operations. The analytics are moving past mapping to more sophisticated use cases.
Fern Halper, Ph.D.
Sponsored by
Alteryx, Information Builders, Tableau Software
Organizations today are seeking to drive deep analysis, detect patterns, and find anomalies across terabytes or petabytes of raw big data. Whether you’re trying to discover the root cause of the latest customer churn or the hidden costs that are eroding the bottom line, you need analytic tools and techniques that work well with unstructured and multi-structured data in its original raw form.
Apache Hadoop is maturing as a loosely coupled stack for inexpensive batch storage, where you don't need to know data formats or schemas to store and process the data.
Philip Russom, Ph.D.
Sponsored by
Splunk