Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
Recently Published
There are good reasons to step back and assess where we are as an organization in our adoption of agile (or any other process model). Executives should concern themselves with these questions:
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We've spent a lot of money and political capital on agile. Where are we in terms of our capability? Are we still at a beginner level organizationally, or are we fairly mature now?
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What percentage of our teams are at an advanced level of practice?
Back in March, I discussed some of the important developments involving the incorporation of social computing techniques (i.e., blogs, wikis, social networks) with enterprise software (see "Facebook for the Enterprise: The New Business
Last month (see "Corporate Use of Text Analysis and Mining Grows," 25 May 2010), I wrote that organizations are showing more interest in using text analysis and mining tools to support their BI efforts.
The Proven Value of Solution Architecture: Six Sigma for Projects
Much has been argued about the value of architecture, but actual proof is generally lacking. As for solution architecture, recent quantitative research has confirmed that this approach not only helps reduce throughput time and budget for IT projects but also leads to a reduction of variance in time and budget, indicating that bringing solution architecture to a project can be viewed as a quality improvement process in line with Six Sigma thinking.
There have been a number of recent developments pertaining to the use of MapReduce1 and its open source equivalent, Hadoop.2 These developments are important because they should help spur greater use of MapReduce and Hadoop by traditional enterprises looking to take advantage of their big data assets by implementing applications that can rapidly process vast amounts of dat