What are the top data analytics platforms of 2015?

Published in World Economic Forum in February 2015.

The past few years has seen an explosion in the number of platforms available for big data analytical tasks.

The open source Hadoop framework is free to use, but very technical to set up and not specialized towards any particular job or industry. To use it in your business, you need a “platform” to operate it from.

These platforms are commercial offerings (you pay an ongoing service charge), most of which take the Hadoop framework and build on it, to provide analytical services of practical use to businesses and organizations.

So here is a rundown, in no particular order, of ten of the best and most widely used of these services. Like any commercial product in a competitive market, each has its advantages and disadvantages, and you need to make sure you are picking the right tool for the job.

Cloudera CDH

Cloudera was formed by former employees of Google, Yahoo, Facebook and Oracle and offers open source as well as commercial Hadoop-based big data solutions with the label Cloudera Distribution including Hadoop, known as CDH. Their distributions make use of their Impala analytics engine which has also been adopted and included in packages offered by competitors such as Amazon and MapR.

Hortonworks Data Platform

Unlike every other big analytics platform, HDP is entirely comprised of open source code, with all of its elements built through the Apache Software Foundation. They make their money offering services and support getting it running and providing the results you are after.

Microsoft HDInsight

Microsoft’s flagship analytical offering, HDInsight is based on Hortonworks Data Platform, but tailored to work with their own Azure cloud services and SQL Server database management system. A big advantage for businesses is that it integrates with Excel, meaning even staff with only basic IT skills can dip their toes into big data analytics.

IBM Big Data Platform

IBM offers a range of products and services designed to make complex big data analysis more accessible to businesses. They offer their own Hadoop distribution known as InfoSphere BigInsights.

Splunk Enterprise

This platform is specifically geared to businesses that generate a lot of their own data through their own machinery. Their stated goal is “machine data to operational intelligence”. Internet of Things is key to their strategy, and among other products they provide the analytics behind the Nest wifi-enabled smart thermostat. Their analytics also drives Dominos Pizza’s US coupon campaigns.

Amazon Web Services

Although everyone thinks of them as an online store, Amazon also make money by selling the magic that makes their business run so smoothly to other companies. The business model was based on big data from the start – using personal information to offer a personalized shopping experience. Amazon Web Services includes its Elastic Cloud Compute and Elastic MapReduce services to offer large-scale data storage and analysis in the cloud.

Pivotal Big Data Suite

Pivotal’s big data package is comprised of their own Hadoop distribution, Pivotal HD and their analytics platform Pivotal Analytics. Their business model allows consumers to store an unlimited amount of data and pay a subscription fee which varies according to how much they analyze. The company is strongly invested in the “data lake” philosophy, of a unified, object-based storage repository for all of an organization’s data.


Another database management system, again available in both an open source, free edition and a paid-for proprietary version. This product is geared towards users looking to get involved with the Internet of Things. They offer three levels of service for paid users, with more users given access to the helpdesk, and quicker email support response times, for higher tier customers.


MapR offer their own distribution of Hadoop, notably different from others as it replaces the commonly-used Hadoop File System with its alternative MapR Data Platform, which it claims offers better performance and ease of use.

Kognitio Analytical Platform

Like many of the other systems here, this takes data from your Hadoop or cloud-based storage network and gives the users access to a range of advanced analytical functions. Kognitio is used by BT to help set their call charges and by loyalty program Nectar for its customer analytics.

As always, I hope this was useful? Please let me know if you have any views or comments on the topic. E.g. are there other platforms you would include? Any practical tips on picking the right one for you?

This article is published in collaboration with Linkedin. Publication does not imply endorsement of views by the World Economic Forum.

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Author: Bernard Marr is a globally recognized expert in strategy, performance management, analytics, KPIs and big data.

Image: Visitors stand in front of QR-codes information panels during a ceremony to open an information showroom in central Moscow April 29, 2014. REUTERS/Maxim Shemetov 

How will the sharing economy reshape our spending?

Published in World Economic Forum in February 2015

When we talk about technology, frequently we’re focused on how it makes our lives easier—but we often look past the way it can completely reshape the economy. When I was in China last month, I spoke to a group of business students about these ideas. As I explored in my last post, we discussed how the drop in oil prices will act as a massive tax cut—today, I want to talk about how new technologies can reshape spending.

According to the International Energy Agency, daily demand for oil has increased by about 600,000 barrels over the past year. But thanks in part to new technologies like hydraulic fracturing, we’ve seen an increase in supply of over 2 million barrels a day. We’re now in the midst of price discovery, and while it’s hard to say where oil will settle, I think it’s likely to be around $75 to $85 a barrel.

The oligopoly is confused. And one reason why is that they did not anticipate the rapid growth in new oil extraction technologies. Fracking represents just a fraction of the 90 million barrels of oil produced each day, but it has a profound impact on the industry as a whole.

It’s a good example of how quickly new technology can destabilize an industry—even one as entrenched and established as oil—and even be missed by many of those within the business.

These changes took place over a number of years, and are happening at the margins. But you can also see technology precipitate incredibly rapid changes, like what we’ve seen with the rise of the “sharing economy.” Best exemplified by home-rental services such as Airbnb or taxi services like Uber (and a Chinese equivalent called Kuadi), they represent a completely different mindset for the use of capital, especially among young people.

We tend to think of these services in terms of the way they affect convenience—how they change behaviors. But as we saw with oil, new technologies can have a profound effect on prices, markets, and the way we spend our money.

For generations, young people all around the world have focused on acquiring two key pieces of property: a home and a car. These purchases are partly status-driven, partly practical. And they’re not identical, of course: Cars tend to depreciate, while homes are seen as an investment. But both purchases require large amounts of capital or credit—money that could be used elsewhere.

With the advent of technologies like Uber and Airbnb, these long-accepted financial decisions may start to change. Why bother with the big upfront investment, the hassles of maintenance and parking, or the liability of owning a car, if you can have one available within minutes with one tap of your phone.

As more and more people use sharing services for transportation, for example, personal vehicles will become less important, both financially and in terms of status. People may decide—and I think this would be the right decision for many —to take the cash they would spend on a vehicle and direct it instead towards smart investments.

Think about the scale of this change—Uber was founded just five years ago. In another five years, car-sharing technologies could be replacing car ownership at a meaningful scale. That has significant implications for the global economy, simply by changing the way capital flows through it.

New technology can also have some unpleasant effects. For example, increasing automation is putting significant downward pressure on employment. Take driverless cars, for example: While it’s true that they will eliminate congestion and accidents, over time, they will also eliminate jobs for people like taxi and truck drivers.

The countries that will get ahead will be the ones that train enough workers to do the skilled jobs—designing and maintaining sophisticated machines, or writing the code that helps them run.

This article is published in collaboration with Linkedin. Publication does not imply endorsement of views by the World Economic Forum.

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Author: Larry Fink is the Chairman and CEO at BlackRock.

Image: An illustration picture shows the logo of car-sharing service app Uber on a smartphone next to the picture of an official German taxi sign in Frankfurt. REUTERS/Kai Pfaffenbach
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