From Big Data Analytics to AI: Vodafone and Celonis Mine Data to Improve Business Processes

businessProcessWhat if you could put your company through an MRI scanner to get a detailed picture of how well your processes work, see the bottlenecks, and understand the causes of delays, unnecessary costs, and lost productivity?

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Common Data Analysis Mistakes

datafallaciestoavoid

Source: Geckoboard.com

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The Data Index, March 2021

Data is eating the world and there are numerous indicators of its ubiquitous presence in our lives.

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Graduate Programs in Big Data Analytics/Data Science

Updated list here

Bentley University

M.S. in Marketing Analytics

DePaul University

M.S. in Predictive Analytics

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Data Created Every 60 Seconds

dataevery60seconds

Source: Bernard Marr

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2018 Predictions for AI, Big Data, and Analytics

crystalballA recent Forrester Research report, Predictions 2018: The Honeymoon For AI Is Over, predicts that in 2018 enterprises will finally move beyond the hype to recognize that AI requires hard work—planning, deploying, and governing it correctly.

But Forrester also promises improvements: Better human and machine collaboration due to improved interfaces; enhancing business intelligence and analytics solutions by moving resources to the cloud; new AI capabilities facilitating the redesign of analytics and data management roles and activities and driving the emergence of the insights-as-a-service market.

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Gartner’s Hype Cycle for Big Data

Louis Columbus at Forbes.com surveys key big data forecasts and market size estimates, including Gartner’s recent Hype Cycle for Big Data. The winning technologies in the immediate future? “Column-Store DBMS, Cloud Computing, In-Memory Database Management Systems will be the three most transformational technologies in the next five years.  Gartner goes on to predict that Complex Event Processing, Content Analytics, Context-Enriched Services, Hybrid Cloud Computing, Information Capabilities Framework and Telematics round out the technologies the research firm considers transformational.”

More on the report from Beth Schultz at AllAnalytics:

Gartner’s Hype Cycle is extremely crowded, with nearly 50 technologies represented on it. Many of them are clustered at what the firm calls the peak of inflated expectations, which it says indicates the high level of interest and experimentation in this area. As experimentation increases, many technologies will slide into the “trough of disillusionment,” as MapReduce, text analytics, and in-memory data grids have already done, the report says. This reflects the fact that, even though these technologies have been around for a while, their use as big-data technologies is a newer development.

Interestingly, Gartner says it doesn’t believe big-data will be a hyped term for too long. “Unlike other Hype Cycles, which are published year after year, we believe it is possible that within two to three years, the ability to address new sources and types, and increasing volumes of information will be ‘table stakes’ — part of the cost of entry of playing in the global economy,” the report says. “When the hype goes, so will the Hype Cycle.”

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Who’s Big in Big Data? (Infographic)

Who's Big in Big Data? (Infographic)

Source: Datasift

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The Big Data Debate: Correlation vs. Causation

In the first quarter of 2013, the stock of big data has experienced sudden declines followed by sporadic bouts of enthusiasm. The volatility—a new big data “V”—continues this month and Ted Cuzzillo summed up the recent negative sentiment in “Big data, big hype, big danger” on SmartDataCollective:

“A remarkable thing happened in Big Data last week. One of Big Data’s best friends poked fun at one of its cornerstones: the Three V’s. The well-networked and alert observer Shawn Rogers, vice president of research at Enterprise Management Associates, tweeted his eight V’s: ‘…Vast, Volumes of Vigorously, Verified, Vexingly Variable Verbose yet Valuable Visualized high Velocity Data.’ He was quick to explain to me that this is no comment on Gartner analyst Doug Laney’s three-V definition. Shawn’s just tired of people getting stuck on V’s.”

Indeed, all the people who “got stuck” on Laney’s “definition,” conveniently forgot that he first used the “three-Vs” to describe data management challenges in 2001. Yes, 2001. If big data is a “revolution,” how come its widely-used “definition” is based on a dozen year-old analyst note?

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The Real World of Big Data (Infographic)

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The Real World of Big Data via Wikibon Infographics

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