Integration & Governance
July 18, 2014
Research indicates that business and IT professionals spend more than 70 percent of their time finding data, validating it or defending it, rather than focusing on what they find most important: analyzing the data. With too little time spent focusing on data analysis, organizations derive sub-optimal returns from their big data initiatives. For better business outcomes, and to maximize the value from big data, organizations need to invest in an agile data governance program.In a recent survey, respondents indicated that they spend more than 70 percent of their time finding data, validating it or defending it, rather than focusing on what they find most important: analyzing the data.
July 17, 2014
The recent controversy over the ethics of Facebook's attempts to influence moods through tweaks to its newsfeed algorithms is overblown. Essentially, Facebook data scientists conducted one of many real-world experiments that are standard operating procedure with them and with most online businesses these days. This was just a routine real-world experiment in big-data-driven sentiment analysis, content optimization and customer experience management.
July 2, 2014
Consider this quote from the recent “Improving Government Performance in the Era of Big Data: Opportunities and Challenges for Federal Agencies Workshop” at Georgetown University: “The seduction of big data is that it allows you to do things that you could not do in the past.” The question is, should you?
June 25, 2014
Building confidence in the big data and analytic initiatives requires good governance. Data from a variety of sources, and in different formats, heightens the need for strong, proactive governance. Health information management professions area ideal candidates for this critical need.
June 23, 2014
“There will always be more data than we can use or manage,” says Richard Lee, this week’s IBM Big Data and Analytics Hero. “We simply need to understand what range of questions we are attempting to answer and size accordingly, while doing no harm to our ability to answer future questions yet imagined.”
June 13, 2014
Nearly 10 years ago, a little open source software project with a funny name came into being.
June 13, 2014
Big data does not make data quality redundant. As long as there is a need to make sound decisions based on information and insights, there will be a need for data quality and governance. Data quality function will adapt to the changing business needs and play a crucial role in building confidence in big data.
June 12, 2014
Real-world experimentation of a very personal and hyper-analytical nature is what the quantified-self (QS) movement is all about. QS practitioners are playing with approaches that behavioral scientists have traditionally applied to third-party subjects within controlled laboratory experiments. The scientific establishment is beginning to realize the potential of quantified self tools for gaining primary data directly from human subjects in a way that is organic to the biological, behavioral, and psychological phenomena being studied.
June 10, 2014
Thomas Baumann, IT performance architect at Swiss Mobiliar and this week’s IBM Big Data and Analytics Hero, shares that once “once response times of analytical reports were improved by a factor of 100 or more, business departments were very interested to apply big data”
June 6, 2014
A delicate balance of appropriate uses, relevant policies and consumer awareness is needed to achieve an effective privacy strategy for use by the big data and analytics community.