Analytics Gets More Accurate, More Accessible - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

IoT
IoT
Data Management // Big Data Analytics
News
11/15/2012
05:10 PM
Connect Directly
LinkedIn
Twitter
RSS
E-Mail
50%
50%

Analytics Gets More Accurate, More Accessible

Advanced analytics' predictive capabilities combined with big data are driving a new age of experimentation.

InformationWeek Green - Nov. 19, 2012 InformationWeek Green
Download the entire Nov. 19, 2012, issue of InformationWeek, distributed in an all-digital format as part of our Green Initiative
(registration required.)

Advanced Analytics Five years ago, companies were standardizing on one or a couple of business intelligence products. Broad interest in advanced analytics, especially the predictive kind, was just emerging.

Today, companies of all sizes and industries are experimenting with and using analytics, and veteran users are going for new levels of sophistication, according to our new InformationWeek Analytics, Business Intelligence and Information Management Survey. Companies are embracing analytics to optimize operations, identify risks and spot new business opportunities.

Advanced analytics is all about statistical analysis and predictive modeling -- being able to see what's coming and take action before it's too late, rather than just reacting to what has already happened. That latter practice, derisively known as "rearview-mirror reporting," is associated with conventional BI.

The more data companies use, the more accurate their predictions become. But the big data movement isn't just about using more data. It's also about taking advantage of new data types, such as social media conversations, clickstreams and log files, sensor information and other real-time feeds. Experienced practitioners are taking cutting-edge approaches, including in-database analytics, text mining and sentiment analysis.

Get our full report, "2013 Analytics & Info Management Trends," free with registration.

In it you'll find more data from our survey of nearly 550 business technology professionals and more detail on our user examples.
Get This And All Our Reports

In each of the past six years, respondents to our analytics and BI survey have rated their interest in 10 leading-edge technologies, and advanced analytics has always been the No. 1 choice. Advanced data visualization is No. 2 this year, up from being ranked third in 2009 (see chart at right). Last year we added "big data analysis" to the list of cutting-edge pursuits, and this year it ranked No. 4 along with collaborative BI.

We also see clear evidence that companies are investing in software, people and advanced techniques. For starters, this year we added "in-database analysis for predictive or statistical modeling" to our list of leading-edge technologies, and respondents rated their interest higher than for more-established categories such as mobile BI and cloud-based BI.

With in-database analysis, statistical and predictive algorithms are rewritten to operate inside databases that run on massively parallel processing (MPP) platforms. In-database analysis is faster than the old approach to data mining, where analysts moved data sets from data warehouses into specialized analytic servers to create and test predictive models. Data movement delays plagued the old approach, and the analytic servers were underpowered. As data sets have grown, time and power constraints limit work to small data samples rather than all available information, limiting the accuracy of the resulting models.

Businesses that have embraced in-database approaches say they can develop models in less time for more precisely targeted segments, whether they're trying to predict customer behavior, product performance, business risks or other variables. What's more, MPP power lets them crunch through massive data sets, so they can use all available data and deliver far more accurate models.

To read the rest of the article,
Download the Nov. 19, 2012, issue of InformationWeek

We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.
Comment  | 
Print  | 
More Insights
Slideshows
10 Ways to Transition Traditional IT Talent to Cloud Talent
Lisa Morgan, Freelance Writer,  11/23/2020
News
What Comes Next for the COVID-19 Computing Consortium
Joao-Pierre S. Ruth, Senior Writer,  11/24/2020
News
Top 10 Data and Analytics Trends for 2021
Jessica Davis, Senior Editor, Enterprise Apps,  11/13/2020
White Papers
Register for InformationWeek Newsletters
Video
Current Issue
Why Chatbots Are So Popular Right Now
In this IT Trend Report, you will learn more about why chatbots are gaining traction within businesses, particularly while a pandemic is impacting the world.
Slideshows
Flash Poll