10 hot big data startups to watch in 2013

What will be the most promising startups in the Big Data field in the year 2013? Just like last year, we did a lot of research and analyses to compile our hotlist of 45 companies we think that could change the market in 2013 either through awesome product innovations or great funding rounds or take-overs. Our criteria for this hotlist were as follows:

  • Main area of business should be related to big data challenges – i.e. aggregation, storage, retrieval, analysis or visualization of large, heterogeneous or real-time data.
  • To satisfy the concept of being a startup, the company should be no older than 5 years old and not be majority owned by another company.

So, here’s the list of the top acts on the Big Data stage:

logo_10gen10gen is the Company behind MongoDB. Thus 10gen has been right in the Epicenter of Big Data. MongoDB has become synonymous with scheme free data base technology. The heap of unstructred documents that wait to be indexed is growing exponentially and will continue to rise until most document generating processes are automated (and therefor only mapping structured data form some other source). 10gen received $42M of funding in 2012 among others by the intelligence community’s VC In-Q-Tel and Sequoia Capital.

bityotaWhile MongoDB is a well known name in the NoSQL movement, you may not have heard of BitYota. This 2011 founded company that only left stealth mode in November 2012 promises to simplify Big Data storage with its Warehouse-as-a-Service approach that could be a very interesting offer for small and midsize companies with a high data analytics need. The BY management teams has a lot of experience by working the Big Data shift at companies such as Yahoo!, Oracle, Twitter and Salesforce. They received a surprising $12M in 2012 by the likes of Andreessen Horowitz, Globespan, Crosslink and others.

clearstorydataBig Data analytics, integration and exploration will be a huge topic in 2013. ClearStory Data, the company co-founded by digital serial entrepreneur Sharmila Shahani-Mulligan, has drawn a lot of attention with a $9M A round in December 2012 by KPCB, Andreessen Horowitz and Google Ventures. ClearStory’s main promise of integrating all the different and heterogeneous data sources within and around companies should be a very attractive segment of the Big Data business in the coming years. We’re eagerly awaiting the launch of this company.

climatecorpAnd now for something completely different. Climate. Insurances have always been looking into the past, modelling risks and losses – usually based on aggregated data. Climate Corporation calculates micrometeorological predictions and promises to thus to be able to offer weather related insurances far more effective. We certainly will see more such technological approaces, bridging from one “Big Data Field” to another – like Climete Corp does with weather forcast and insurances. $42M funding in 2011 and another $50M in 2012 – weather data seems to be a very promising business.

We already had this one on our last year’s list. Then in stealth mode, now – one year and $10M later – Continuuity have disclosed more of their business model. And we’re excited. When the Web started in the 90s, everyone got excited about the fantastic possibilities that html, cgi and the like would offer. But setting up a website was an expert task – just to keep the links consitent ment continuusly updating every page; this did not change until the easy-to-use content management systems where programmed, that we are all using today. With Big Data, its the same: we recognise, how great everything is in theory, but there are only few apps and the recurring tasks to maintain the environment are hardly aggregated into management tools. Continuuity builds a layer of standard APIs that translate into Hadoop and its periphery, so companies can concentrate on developing their applications instead of keeping their data running.

dataguiseOkay, this company is no longer a start-up age-wise. But it is representative of many other Big Data companies that will address a more and more important topic when it comes to the modern data environment: security. Dataguise has received $3.25M of funding in 2011 for its approach of protecting all the valuable information buried in your Hadoop clusters. Other companies on our shortlist in this field are Threat Metrix and Risk I/O.

ERNOn our hotlist, we had a lot of Big Data start-ups focusing on finance or retail. One of our favorites, ERN offers an integrated payment and loyalty solution. The founding team of this British startup hails from companies like MasterCard, Telefónica o2 or Barclay Card, so they should have good insight into the needs of this market. Up to now, they have received $2M funding. But especially with the focus on mobile transactions, we believe this market holds a lot more than that.

nuodbDatabase technology is at the core of the current Big Data revolution. But with all the talk about NoSQL, you shouldn’t say good-bye to SQL prematurely. 2013 could also be the year of the great SQL comeback. One of the people who could make this happen is NuoDB’s Jim Starkey. He developed one of the very first professional databases, Interbase, and invented the binary large object or: BLOB. Now he co-founded NuoDB and received $20M of funding in 2012 to re-invent SQL.

parstreamHere’s another non-US Big Data start-up: Germany’s Parstream. Big Data does not always mean unstructred data. Check-out-transactions, sensor data, financial record or holiday bookings are just examples of data that comes usually well structured and is kept in flat tables. However these tables can become very very large – billions, even trillions of records, millions of columns. Parstream offers highly robust data base analytics in real time with extremely low latency. No matter how big your tables are – each cell is to be addressed in milliseconds standard SQL-Statements. This makes Parstream an interesting alternative to Google’s BigQuery for applications like web analytics, smart meetering, fraud detection etc. In 2012, they received $5.6M of funding.

zoomdataOf course, as in 2012, data viz will still be one of the most fascinating Big Data topics. Zooming into data is what we are used to do with data mining tools – to quickly cut any kind of cross section and drag-and-drop the results into well formated reports. However this was only working on static dumps. Zoomdata offers seamless data access on data streamed from any kind of input source in real time with state-of-the-art visualisation that users can swipe together from the menu in real time. Still at seed stage with $1.1M of funding, we’re looking forward to hearing from this company.

10 Points Why Market Research has to Change

(This is the transcript of a key-note speech by Benedikt and Joerg 2010 on the Tag der Marktforschung, the summit of the German Market Researchers’ Professional Association BVM – [1])

Market research as an offspring of industrial society is legitimized by the Grand Narrative of modernism. But this narrative does no longer describe reality in the 21st century – and particularly not for market research. The theatre of market research has left the Euclidian space of modernism and has moved on into the databases, networks and social communities. It is time for institutionalized market research to strike tents and follow reality.

“Official culture still strives to force the new media to do the work of the old media. But the horseless carriage did not do the work of the horse; it abolished the horse and did what the horse could never do.” H. Marshall McLuhan

1. Universally available knowledge

In facing the unimaginable abundance of structured information available anytime and everywhere via the Internet, the idea of genuine knowledge progress appears naïve. When literally the whole knowledge of the world is only one click away, research tends to become database search, meta analysis, aggregating existing research or data mining, i.e. algorithm-based analysis of large datasets.

2. Perpetual beta

What can be found in Wikipedia today is not necessarily the same that could be found a few weeks ago. Knowledge is in permanent flow. But this opposes the classic procedure in market research: raising a question, starting fieldwork, finding answers and finally publishing a paper or report. In software development, final versions have been long given way to releasing versions; what gets published is still a beta version, an intermediate result that gets completed and perfected in the process of being used. Market research will have to publish its studies likewise to be further evolving while being used.

3. Users replacing institutes

The ideal market researcher of yore, like an ethnologist, would enter the strange world of the consumers and would come back with plenty of information that could be spread like a treasure in front of the employer. Preconditions had been large panels, expensive technologies, and enormous amount of special knowledge. Only institutes were able to conduct the costly research. Today, however “common” Internet users can conduct online surveys with the usual number of observed cases.

4. Companies losing their clear boundaries

“Force and violence are justified in this [oiconomic] sphere because they are the only means to master necessity.” Hannah Arendt

In her main opus “The Human Condition” Hannah Arendt describes the Oikos, the realm of economy, as the place where the struggle for life takes place, where no moral is known, except survival. The Polis in opposition to this represents the principle of purposeless cohabitation in dignity: the public space where the Oikos with its necessities has no business.

When large corporations become relevant if not the only effective communication channels, they tend to take the role of public infrastructure. At the same time, by being responsible only to their shareholders, they withdraw from social or ethical discussions. As a result, their decisions could hardly be based on ethic principles. With research ethics, the crucial question is: Can our traditional ways of democratic control, based on values that we regard important, still be asserted – and if not, how we could change this.

5. From target groups to communities

The traditional concept of target groups implies that there are criteria, objective and externally observable, which map observed human behavior sufficiently plausible to future behavior or other behavioral observations. Psychological motives however are largely not interesting.

Contemporary styles of living are strongly aligning, making the people appear increasingly similar one to each other (a worker, a craftsman or a teacher all buy their furniture at the same large retailer); however, with the Internet there are more possibilities than ever to find like-minded people even for most remote niche interests.

Often these new communities are regarded as substitute for target groups. But communities are something completely different from target groups, characterized by their members sharing something real and subjective in common: be it common interest or common fate. Thus, objective criteria also become questionable in market research.

6. The end of the survey

Google, Facebook and similar platforms collect incredible bulks of data. By growth of magnitudes quantity reverts to quality: they don’t just become bigger, but become completely new kinds of objects (e.g. petabyte memory and teraflop databases).

Seen from the users’ perspective Google is a useful search engine. From a different perspective, Google is a database of desire, meaning, context, human ideas and concepts. Given these databases, data collection is no longer the problem: rather to get the meaning behind the numbers.

7. Correlations are the new causalities

“Effects are perceived, whereas causes are conceived. Effects always precede causes in the actual development order.” H. Marshall McLuhan

In marketing it is often not important if there is really some causality to be conceived. Some correlation suffices to imply a certain probability. When performance marketing watches how people get from one website to the other, without being able to explain why: then it is good enough to know, that it is just the case.

8. The end of models

“Only for the dilettante, man and job match.” Egon Friedell

There is, as sketched before, an option for market research without theory. To take profit from new data sources and tools, the future market researcher has to act like a hacker, utilizing interfaces, data and IT-infrastructures in a creative way to achieve something that was not originally intended by this technology. The era of the professional expert is over.

9. Objectivity is only a nostalgic remembrance

Objectivity is a historical construct, formed in the 19th century and being kept in the constellation of mass society, mass market and mass media for a remarkably long time. With social networks in the Internet you do not encounter objects or specimens, but human beings that are able to self-confidently answer the gaze.

10. We need new research ethics

The category of the “consumer” shows the connection between aiming to explore our fellows and the wish to manipulate them. Reciprocity, the idea to give the researched population something back, has not been part of traditional market research’s view of the world.

In the Internet on the other hand, reciprocity and participation are expected. This has pivotal implications for our research ethics, if we want to secure the future cooperation of the women and men, which would not want to get mass-surveyed and mass-informed either. Likewise, some existing ethical concepts have become obsolete, such as the anonymity of the participants: who takes part in a project as a partner, does not need and not want to stay anonymous.

“Handle so, dass du die Menschheit sowohl in deiner Person, als in der Person eines jeden anderen jederzeit zugleich als Zweck, niemals bloß als Mittel brauchst.” Immanuel Kant