“What you Need to Know About Hortonworks, Splunk, and 28 Other Big Data Companies” -Dave Feinleib, Forbes

“What you Need to Know About Hortonworks, Splunk, and 28 Other Big Data Companies” -Dave Feinleib, Forbes

Here is a great article in Forbes about some general information about 30 of the top Big Data companies that Dave Feinleib, a contributor to Forbes, uncovered when he spoke with all of them.

 

 

A month ago I posted The Big Data Landscape. I got 100s of emails from vendors, customers, PR people, and consultants. In addition to talking with customers, I set a goal of talking with 30 Big Data companies in 30 days. Here’s who I talked to.

Alteryx Atigeo Autonomy Bloomreach Calabrio Chart.io Cirro Cloudera Cloudyn CollectiveI Connate Datameer Digital Reasoning Hortonworks Informatica Kognitio Lexalytics Metamarkets Microstrategy Opera Paraccel Pentaho Pervasive, PredPol QlikView Qubole Recommind SAP SAS Splunk Sumo Logic Terracotta Tibco and Visual.ly. (There are more than 30 here; a few of these are scheduled for the next two weeks, but I didn’t want to leave them out.)

Here’s what I learned.

1. Customers: the haves and have nots. The haves are making use of Big Data to get more efficient, generate new customers, or enter new businesses. Some of the have nots aren’t collecting the same granularity of data, or don’t have a way to take lots of different data sources and use them together.

The haves are expanding access to data analytics across the organization. The have nots are not.

2. Big Data as a core competency. The haves consider leveraging Big Data a core competency and source of competitive advantage. One has a corporate-wide (not just project-specific) ROI measure that ties the benefits gained from Big Data to EPS.

Some have visibility into what’s going on this instant, but can’t easily run trend analysis. Others can run reports but have no real-time visibility. The have nots haven’t even internalized they have a problem.

3. All steak, no sizzle. Vendors are fond of talking about exabytes and petabytes, cloud versus on premise, and why this technology beats that technology. Stories of business success and industry transformation exist but aren’t typically thought of and talked about in that way. Many customers are still buying and assembling technology for which ready-made, well-supported Big Data Apps are available .

4. There’s lots of noise. This is a common complaint from startups in the space, especially business intelligence and analytics startups. Ironically, it’s also a complaint from vendors that have been around for a while. It is possible to stand out, but it requires a distinguishing characteristic.

5. There’s plenty of ‘white space.’ Despite all the noise, there’s still lots of ‘white space,’ especially for Big Data Apps. Here are some examples:

Visualization
– Some products run only on Windows. There’s no SaaS version and no Mac version.

Marketing and Sales analytics
– A lot of marketing and sales people just want basic reports for Salesforce.com. Those that are heavily invested in online and SaaS want reports that leverage data across multiple cloud services, including Facebook, Twitter, Google Adwords, Salesforce, Zendesk, Marketo, and others.

Consumer behavior
– Companies are fond of talking about the advantages of energy monitoring and smart meters. There’s a lot of money at stake in aggregate, but most consumers aren’t changing their energy consumption habits to save a dollar or two a month.

New data/information services. Micro Bloombergs – one company takes real-time market data feeds and generates unique hourly and daily reports for Wall Street traders using open source software and SaaS reporting tools.

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