Many businesses have yet to fully capitalize on their investments in big data and analytics technologies.
That’s one of the main takeaways from a new report from the Economist Intelligence Unit and ZS Associates. Report authors surveyed businesses across the country to gauge their success with analytics technologies and many of the responses were surprising.
Analytics tools are now seen as almost a no-brainer where the value seems fairly obvious. To be sure, when implemented and used wisely, the tools can be game changers for enterprises. But the report suggests that few are reaping the full promised benefits. Take a listen to this interview with ZS associate principal Dan Wetherill to hear more about why some businesses are struggling.
The opportunity to celebrate 50 years of publication at Computer Weekly proved also the occasion for some musings on the big picture of British computing over the years, when CW’s Brian McKenna joined Ed Burns and Jack Vaughan for the Talking Data podcast. The discussion turned to the role of new technologies like Hadoop in London’s frenetic financial services industry. It included a consideration of the influence that the vaunted Bletchley Park development center had on the course of U.K technology.
While adoption of Spark continues to grow, this year’s Spark Summit highlighted some of the ways in which the big data processing engine is still a work in progress. In particular, it’s stream analytics processing engine continues to struggle with a few hiccups that can limit its utility. That said, presenters still by and large believe Spark is the best option for streaming analytics, as other tools are even further behind in maturity.
The Summit also featured comments from Doug Cutting, the originator of Hadoop, on where the open source data processing industry is headed and what might be next. Take a listen to this edition of Talking Data to hear more about these topics and more from the 2016 Spark Summit.
The development of AI had been stalled out for years, but all of a sudden there’s been a huge surge in interest. Why now? The answer is big data. It turns out big data was the missing knowledge bank that was needed to make machines truly intelligent. But now that enterprises have stashed huge volumes of data, they are starting to unleash learning algorithms on it, creating large-scale learning opportunities.
Kafka and Spark Streaming are arising as central parts of new real time data architecture. In this podcast, the Talking Data crew hooks up with Jay Kreps and Doug Cutting, of Confluent and Cloudera, respectively, who outline some of big data streaming’s pertinent facts.
The recent PBS film The Human Face of Big Data stirred plenty of reaction of social media and in blogs. In this edition of Talking Data, we take a look at what the show got right and what it might have missed.
The documentary was certainly a high-level overview of big data geared mostly toward a popular audience. With that in mind, it did do a good job of introducing some positive examples of big data and analytics. But while the show was not uncritical, particularly around the areas of privacy and security, it missed some important opportunities to discuss the potential downside of big data, mainly as it relates to distributing the benefits of technology throughout society.
Take a listen to this podcast to hear more about how people are reacting to the documentary.
Batch processing came, went and returned. Now it may be leaving again, MapR’s Jack Norris tells the Talking Data podcaster Jack Vaughan in our latest episode. According to Jack Norris, senior vice president of data and applications, we will see more convergence in real time and batch architecture as Apache Spark joins Hadoop, and event streaming is matched with big data storage in the world of big data. Norris spoke about this and other pressing data topics in the podcast.
In this episode of the Talking Data podcast, Ed Burns and I discuss use cases for machine learning. Vibrant application areas include insurance risk analysis, credit scoring, recommendation engines and digital ad placement. While machine learning does seem to undergird a lot of modern big data analytics work, implementations still remain largely the province of the advanced data scientist. Machine learning methods have deep roots in statistics and artificial intelligence, and how quickly these methods can go mainstream remains a matter of conjecture. Check out podcast, and stay tuned.
There’s not doubt that IoT analytics has become one of the most hyped technologies in 2016, but behind the hype, there may be a glimmer of promise. In this edition of Talking Data, we try to look beyond all the excitement to see signs that the promise of IoT analytics is for real. We assess various ideas, such as analytics at the edge, smart cities and data privacy and security, and how they are likely to play out when businesses start to analyze IoT data.
In the year ahead, businesses are looking for new ways to analyze data and new tools to help them, according to Goutham Belliappa, an analyst with consulting firm Capgemini.
In particular, Belliappa is looking at ways businesses plan to monetize their data, analyze data from Internet of things-connected devices and make better use of cognitive computing. Take a listen to this podcast to learn more about why these trends are expected to be hot in 2016.