Data Mining

Where's Big Data leading your business?

The topic of big data continues to pulsate with vigor in the market, as demonstrated by the wide variety of data innovations emerging daily and the talented professionals successfully pursuing the creation and use of big data solutions. So what trends might we see emerge in the Big Data ecosystem?[read more]

How can Big Data deal with social risk?

Corporations are faced with literally millions of potential threat sources given how the social aspect of today’s online world has empowered practically every individual with immediate reach and influence to broadcast their disappointment, displeasure or disgust with a brand.[read more]

A tale of two BIs

All modern Information Technologies which are capable of improving the enterprise competitiveness fall in the scope of BI, such as ERP, CRM, Reporting tools, Data Computing, Statistical Analysis, Data Mining, OLAP, and ETL, etc. They can be divided into two categories: Desktop BI and Solution BI.[read more]

data variety / shutterstock

Data variety stands out from the three Vs of big data from the report of the big data survey conducted by NewVantage Partners in 2012. One of the survey results shows companies focusing more on data variety instead of data volume both now and in the next three years.[read more]

Big Data journey / shutterstock

While much around big data remains hype, many companies are in the fledging stages of drawing value from their big data corpus, and given an army of discussions and opinions around the topic, it’s still hard to find a clear roadmap to arrive at the Big Promise.[read more]

Facebook & big data analytics

Facebook has massive data analytics capabilities and it has a lot of data - big data. It has our personal details, our likes, our updates, our pictures and videos. However, my big question here is: are they overstepping the mark by exploiting this (even very personal) data?[read more]

Hadoop toolbox

Hadoop and Big Data have almost become synonymous. But Hadoop is not just Hadoop now. Over time it has evolved into a big herd of various tools, each meant to serve a different purpose. But glued together they give you a powerpacked combo. Here's my short intro to some very useful tools.[read more]

Predictive analytics for retail (Image Source)

Here's a true business success story that sheds bright light on the awesomeness of big data. It substantiates the notion that you should store every last iota of data – because you don’t know every pattern [read: opportunity] that might be found or explored.[read more]

Automated data mining?

Can data mining be automated? To find an answer, we need to analyze the different phases of data mining and estimate which one can be automated. For this purpose, I have chosen the CRISP-DM methodology (I guess any other data mining process would lead to similar conclusions).[read more]

big data security

Today's security systems still have long ways to go before being fully integrated in true Big Data sense. Security professionals need to be able to get increasing value from the data they already collect and analyze, on top of the data they still are not getting. Here are some key considerations.[read more]

decision management: democratizing data (shutterstock)

Democratizing data cannot just mean helping more knowledge workers have more fun with their query and visualization tools. It has to mean democratizing data-driven decision making throughout the organization, and that will take a new generation of decision-making systems.[read more]

big data sources / shutterstock

Why is big data valuable? Because it’s big? Not really. The value of any dataset is determined by the quality of information you can extract from it. The key to value in big data is the detail. In other words, the value of big data is in the small stuff.[read more]

Big Data education opportunities

Students, from enrollment to grades to the clubs they join, generate as much data as any of us. Schools are already working towards using this data to improve education, the way they handle different student demographics, and even the way they research fields like literature.[read more]

data scientists in natural habitat

Am I overpaid if I can deliver the leads with a higher margin and lower price? At the end of the year, my revenue after cost is far above the $133k mentioned by ZDNet, yet I don't feel overpaid, and my clients don't feel that our service is expensive - if they did, they would stop working with us.[read more]

Big data/shutterstock

Lots of data does not necessarily equate to “Big Data." To my way of thinking, the single most important capability to implement in any large scale data platform that is going to support sophisticated analytics is the ability to quickly construct, high quality random samples.[read more]