- SAP BusinessObjects Cloud: SAP’s Approach to Analytics in the Cloud Enterprise, January 13, 2017
- Combining Big Data and Analytics to Achieve Business Success, November 10, 2016
- TEC 2016 Business Intelligence Buyer’s Guide: Big Data Analytics, August 25, 2016
- 2016 Enterprise Performance Management Market Landscape Report: Driving Business Success with EPM Software, May 27, 2016
- IBM Cognos Analytics—Simple, Powerful, and Practical for a New Generation of Users, March 31, 2016
- SAP BW 7.4—The Role and Value of BW in SAP’s New Enterprise Data Warehouse Vision, January 25, 2016
- 2015 Business Process Management Market Landscape Report: Selecting a BPM Solution for the Modern Business, November 13, 2015
- TEC 2015 Business Intelligence Software Buyer's Guide: Data Discovery and Visualization, October 14, 2015
- IBM Analytics for Higher Education: Increasing Student Retention and Growing Revenue, September 1, 2015 (Co-authored with Raluca Druta)
- Teradata 15—Scalable, Integrated, and Connected, May 29, 2015
- deFacto Performance Management: Enterprise Performance Management Redefined, March 31, 2015
- User Simplicity for Business Agility with Bizagi BPM Suite, February 23, 2015
- TEC 2015 Cloud BI and Analytics Buyer's Guide, December 9, 2014
- Financials in the Cloud: Boosting Efficiency and Control in the Mexican Market, December 2, 2014
- Measuring Marketing Success: Know Each Customer in Context, November 26, 2014 (Co-authored with Raluca Druta)
- Data Visualization: When Data Speaks Business, August 25, 2014
- Data Warehousing in the Big Data Era: Are You BIReady?, August 11, 2014
- A Guide to Microsoft’s BI Ecosystem—Understanding Microsoft’s Approach to BI for the Enterprise, July 4, 2014
- The Teradata Database and the Intelligent Expansion of the Data Warehouse, May 28, 2014
- Extending BI’s Reach: Anticipate Outcomes, Forecast Results, and Respond Proactively, March 3, 2013
- TEC 2014 Mobile BI Buyer's Guide, December 2013
- Product Analysis Report: IBM Cognos and SPSS Solutions, November 2013
- The New BPM: Integrated, Transparent, Collaborative—and Apparent, November 2013
- Spotlight on UNIT4 Business Analytics, April 30, 2013
- One In-memory Solution Fits All: QlikView Stands Out with Unique Approach and Broad Applicability, July 2013
- UNIT4 Agresso Cloud Platform Spotlight, March 20, 2013
- SAP HANA: From Database to Platform, November 20, 2012
- 2012 Business Intelligence and Data Management Buyers Guide, October 31, 2012
- Transforming Asset-intensive Industries with Mobility, October 17, 2012
- Moving beyond the Basics: Key Considerations for Successful Adoption of a Mobile Platform, July 27, 2012
- Mobile BI Market Landscape Report, July 27, 2012
- The IT Manager's Guide to Mobile Apps for Lines of Business, July 9, 2012
- Mobile BI: Features, Challenges, and Opportunities, March 16, 2012
- BI Maturity and Software Selection Perspectives, November 17, 2011
- 2011 Business Intelligence Guide: BI for Everyone, March 23, 2011
In the first part of this series, I described a bit of what machine learning is and its potential to become a mainstream technology in the industry of enterprise software, and serve as the basis for many other advances in the incorporation of other technologies related to artificial intelligence and cognitive computing. I also mentioned briefly how machine language is becoming increasingly important for many companies in the business intelligence and analytics industry. In this post I will discuss further the importance that machine learning already has and can have in the analytics ecosystem, especially from a Big Data perspective. Machine learning in the context of BI and Big Data analytics Just as in the lab, and other areas, one of the reasons why machine learning became extremely important and useful in enterprise software is its potential to deal not just with huge amounts of data and extract knowledge from it—which can somehow be addressed with disciplines such as data