6 Steps To Manage Big Data

Here are 6 tips by +Diane Berry from +Coveo and +Coveo's engineers to handle the biggest challenge of the 3 Vs of Big Data - Variety:

1. Understand Your Business Goals Beforehand
2. Don't try to Move the Data
3. Replicate and Duplicate Data? Don't Do It
4. Use Indexing Technology to Navigate a Complex Data Environment
5. Present Information Based on User Actions
6. Spin Data Into Insights

Full article on +InformationWeek by Jeff Bertolucci can be accessed here.

What's Your Big Data Worth?

Business leaders wanting to value their data are facing significant obstacles from outdated accounting practices. A positive development, however, is the fact that data has been added as an asset class during the +World Economic Forum  Summit in Dubai.

And, some Gartner clients are now asking not only how to become information-driven but also how to generate revenues from the data assets they own.

Also, according to John Lewis from Nielsen, a common mistake was "falling in love" with own data and disregarding external sources, such as industry or consumer data.

Full article on +InformationWeek by Ellis Booker can be accessed here.

What A Data-Driven Culture Buys You

A survey by the +Economist Intelligence Unit of 530 C-Level executives found a very strong correlation between company's financial performance and how it views data collection, analysis and sharing.

Companies that considered themselves ahead of peers financially also paid strong attention to the way they collected, analyzed and used data. The reverse link was also very strong.

Elissa Fink from +Tableau Software, the company that sponsored the survey, speculated that the reason for the results can be very simple "If you're on top of your data, you're probably more aware of your financial performance".

Full article on +InformationWeek by Ellis Booker can be accessed here.

9 Big Data Challenges Banks Face

Some banks have started dusting off projects that were put on hold when the financial crisis hit. However, most of these are dealing with a specific problem, such as liquidity risk reporting and not trying to transform the enterprise systems at once.

These are the major challenges as identified by executives from +SAP:

1. The Big Data myth
2. Dirty data
3. Aged data infrastructures
4. The need for real-time data
5. The need to get new products to market quickly
6. Inefficient data management
7. Seeing the value of data
8. The need to shift IT from cost to profit center
9. Fear of failure

Full article on +American Banker can be accessed here.

Big data is the key to unlocking big gains in energy productivity

Big Data will allow energy productivity gains, called for by President +Barack Obama, to be achieved more easily. And, that applies as much to the new, already efficient buildings, as to existing ones where the biggest impact would be felt if they're made more energy efficient.

At present, US and Canada, use almost double the amount of energy per capita, compared to Germany and Japan - 83,561 and 86,101 kWh respectively, vs 46,702kWh (Germany) and 45, 477kWh (Japan).

WebMeter is a device that can monitor data from up to 36 circuits in a building's circuit board. And, it's main difference is cost - 1/10 compared to other monitoring technologies.

Full article by Tyler Hamilton can be accessed here.

Big Data Pumps Life into Water Investment

A fundamental question remain unanswered in relation to water: At what costs is it economical for cities to bring additional water resources online? In other industries, e.g. oil, this question is easy to answer. Water industry, however, is treated quite differently by politicians, Wall St and other policymakers.

Large amounts of data are available in relation to water - volumes, expenditure, energy costs. These, when analyzed, would allow for creation of an universal index that can be used to evaluate attractiveness, or otherwise, of water investment.

And, this is what Water Cost Index developed by +IBM  and Waterfund is looking to achieve.

Full article can be accessed here on SustainableCitiesCollective blog.

Big Data's Big Blind Spot

(from Forbes)

Data can help envision the future, but it isn't the future. On the other hand, what makes us human most likely is the unpredictability of our behaviour. And, this cannot be handled easily by Big Data. Because our behaviours will lead to Big Swings that are not easily handled by Big Data.

Or to put another way, "Black Swans" will always be there at some point in time.

You can read the full article by Rick Segal from +gyro on Forbes here.

Unleash The Brawn Of Big Data With Small Steps

(Photo credit: Eoin McNamee, from Forbes)

A big gap exists between the possibilities of Big Data and its use in making better business decisions. Studies show that although ROI of 241% can be generated by using Big Data, only 11% of marketers use it in their decision making activities.

And, one of the reasons for this disconnect is that Big Data can be daunting. One solution is to start with baby steps and progress from there. This is what Caesar's Entertainment and Rapid Racking, a UK-based manufacturer have done.

Read the full article by +Sandra Zoratti on Forbes here.

The Retailer's Guide to Big Data

Thanks to consumers’ increasing use of mobile devices and social media, the volume and variety of data points continue to mushroom at a dizzying pace. What are the challenges and opportunities arou...

Mario's insight:

This infographics has it all - what makes Big Data big, challenges and goals in using it and how to develop a game plan.
See it on Scoop.it, via Big Data for Organisational Leaders

9 Open Source Big Data Technologies to Watch

1. Apache Hadoop - framework for data-intensive applications

2. R - statistical language

3. Cascading - software abstraction layer for Hadoop

4. Scribe - server for aggregating log data

5. ElasticSearch - open source search server

6. Apache HBase - non-relational columnar database

7. Cassandra - NoSQL data store

8. MongoDB - another NoSQL data store

9. CouchDB - yet another NoSQL data store

View the full slideshow by +Thor Olavsrud on CIO.com here.