Top Trends in Analytics and Big Data ahead of Strata 2014 Santa Clara
Our survey ahead of Strata 2014 Conference revealed the top 3 trends in Analytics and Big Data for 2014: Analytics for the masses, Apache Spark, and Real Time Analytics. Read the interesting comments and details and get a KDnuggets discount for Strata.
As a media partner for Strata 2014 Conference, KDnuggets received and raffled to our readers a free, 2-day conference pass to Strata Conference, Making Data Work, Feb 11-13, 2014, Santa Clara, CA. Although the KDnuggets free pass was taken, you can still save 20% on registration by using code KDNG.
The winner was randomly selected among almost 50 submissions. All submissions also gave their most important trend in Analytics and Big Data in 2014.
There was a great diversity among answers, but 3 most popular trends have clearly emerged:
- "Analytics for the masses" - more apps, platforms, and companies aiming for business users and consumers, not data scientists
- Apache Spark
- Real-time Analytics
Here is the word cloud of the top terms, after removing the most common words "Data" and "Analytics":
Here are some of the more interesting trend observations:
continued "democratization" of the data - many apps, platforms & companies to help business users & consumers have access to meaningful insights gleaned from their own data as well as public data.
In 2014, some of the hype around predictive analytics will subside, and investors and customers will start to realize that predictions have the most long-term value when they are transparent and actionable.
A) accessibility of analytics and big data to the masses. First time in history that you don't need an advanced degree to be relatively successful with advanced analytics - just down load R or Python and learn random forest,
B) the other trend is Hadoop on Amazon - while Hadoop is not new, neither is Hadoop on Amazon (EMR) for that matter, EMR makes the storing, aggregation and structuring of enormous volumes of data possible to anyone for peanuts, as penny and a half per node per hour come on that is cheap!
In 2014, (automated) software has started eating data science. From analytics platforms to ML in the cloud, there are more tools every day that automate away labor-intensive aspects of the data scientists' work. However, the need for people who know how to use these tools continues to increase.
the hype around the big data analytics will settle down (already started) and more and more organizations will have to develop clear business cases for Big Data Analytics before they take the leap.
For me it's the use of small organizations and non-profits like ourselves using Analytics and Big Data to find ways to do more with less. My goal is to use analytics to increase operational awareness to front line staff as well as bridge the healthcare industry gap between data and operations. Risk mitigation with the use of big data and data science techniques in a highly regulated world such as ours is invaluable not just in in the field but in auditable processes and documentation.
1. In the Python and R we are going to see a fusion take place. Python for scrubbing, machine learning, and repetitive tasks, and R for visualization and statistical analysis. But we don't have a tool that joins them together. I believe we are going to see that bridge built in open source through dual fusioned IDEs that can handle both worlds more seamlessly.
2. Simpler machine learning techniques that will be developed by start ups. They will create interfaces to do some of the simpler modeling for clients to embrace.
O'Reilly Strata Conference, Making Data Work
Feb 11-13, 2014. Santa Clara, CA, USA
The O'Reilly Strata Conference brings together the brightest minds in data science and big data: decision makers using data to drive business strategy, as well as practitioners who collect, analyze, and manipulate it. Attend the conference and tap into the collective intelligence of over 150 of the leading data experts, network with thousands of your peers, and hear about the latest (and emerging) data tools, technologies, and best practices.
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