#wikibon

“But Gartner’s rather sombre outlook doesn’t mean that Hadoop has failed to live up to the hype. On the contrary, the figures are simply a reflection of what it takes for technologies as complex as Hadoop to mature to a point where they can be easily accepted by mainstream users, argues Wikibon’s Big Data and Analytics analyst George Gilbert.”

It’s almost as though technology adoption can be plotted on a graph, probably a curving graph, with upward-trending areas where everyone’s bullish and downward-trending areas where they worry the wheels are coming off.

Big Data Vendor Analysis

Looking at the recent update from Wikibon Big Data Vendor Revenue and Market Forecast by Jeff Kelly, the Big Data market reached $18.6 billion sales (hardware,software, and services) versus 2013, and grew 58% versus 2012.

This breaks down to 40% services, 38% hardware, and 22% was software. The low amount of software is due partly to open source like Hadoop.

The main drivers of growth for the…

It's been a long time since 2010 when I first read the economist magazine special report for Managing Information

 That was the first time I got really interested in the "new field" of Big Data and until now I have tried to watched all the news and new directions very closely. This is why I believed at the time that Big Data seemed to be something more than a new buzz. Nowadays, only for the first quarter of 2013, I've read four interested reports related with Big Data. Some of them are more technical and some are more focused on business strategy.

 Starting with McKinsley Quartely which was published on March 2013, it provided some useful conclusions about how a company can make a start with big data.We can summarize McKinsley's advice in three simple steps i) Collection and integration extraordinary volumes of new data ii) Apply advanced analytics models for optimise operation and  protect outcomes and iii) Creating tools to translate the output of models into tangible business actions. I totally agree with the third step. More or less, it is easy to collect and build a big data set  and apply data mining or machine learning algorithms on it. These actions are useless if you can not focus on translating these results to every day business practice. Data scientists should keep in mind to provide easy methods or tools for their customers to "consume" the data and understand new patterns in order to improve their company's efficiency. 

In Gather 2013 BI Magic Quadrant report, you can find the analysis of 22 vendors active in BI domain. I focus on two companies which, according to Gather, are the winners in "ability to execute" and "completeness of vision" categories. Microsoft wins in category "ability to execute". The company has three tools PowerPivot,  SQL Server Analysis Services and Power View.

A lot of people are familiar with MS Excel and Microsoft used the Excel's popularity with the Power Pivot. It is rather easy for anyone to analyse a Big Data set using Power Pivot. Microsoft called this "self service business intelligence". Another important aspect for the company is the Hadoop Distribution by Microsoft which is available on Windows Azure Platform and it is called HDInsight. IBM leads in category "completeness of vision". IBM is one of the first companies which offered BI solutions to their costumers and already holds a large range of products in its portfolio. It is worth mentioning the InfoSphere Streams and the new products like Cognos Insight and Cognos Express. 

I recommended you to read this report in order to have a clear view of BI ecosystem of tools and solutions today. There you can find companies like QlinkView which provide new capabilities that enable users to build interactive visualizations for discovering new insights.

The third report is the Big Data Vendor Revenue and Market Forecast 2012-2017  from Wikibon. You can find a table of more than 60 vendors Wikibon tracked and/or modelled during the 2012 for the Big Data revenue report. The total Big Data revenue for the 2012 is $11,448M which was divided in three categories i) 40% of the total revenue refers to Big Data Hardware Revenue ii) 21% is for Big Data Software Revenue and iii) 39% is for Big Data Services Revenue. The Big Data market forecast yearly revenue for the 2017 is $47.8B. Wikibon states that these increases were because of a number of certain factors.

"An increased awareness of the benefits of Big Data as it is applied to industries beyond the Web, most notably financial services, pharmaceuticals, and retail;

The maturation of Big Data software such as Hadoop, NoSQL data stores, in-memory analytic engines, and massively parallel processing analytic databases;

Increasingly sophisticated professional services and practices that assist enterprises in practically applying Big Data hardware and software to business use cases;

Increased investment in Big Data infrastructure by massive Web properties – most notable Google, Facebook, and Amazon – and government agencies for intelligence and counter-terrorism purposes."  

Source:http://wikibon.org