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Big Data
Thought Leaders in Big Data: Interview with Jim Swift, CEO of Cortera (Part 3)
Sramana Mitra: I get what you are doing. Would you like to discuss a use case that is outside of sales? You said you are providing information to finance and marketing.
Jim Swift: In the risk space there are several different applications. A lot of our customers are using data in different ways. One in particular is a 130-employee boutique manufacturing company that is a customer of one of our customers. They are a good example of what tends to happen in the risk world. There is a blind spot that has always existed with the traditional ways to look at companies. >>>
Thought Leaders in Big Data: Interview with Sandy Steier, CEO of 1010data (Part 1)
Sandy Steier is the chief executive officer of 1010Data, the leading provider of cloud-based big data analytics. Sandy counts more than 25 years of experience in the industry and is known as one of the innovators behind the adoption of advanced analytics in the financial sector. In this interview Sandy gives us insights into 1010data’s problem solving capabilities, especially in the financial and retail sector, and he discuses his opinion of the current state of big data analytics.
Sramana Mitra: Sandy, let’s start with some context about 1010data. What do you guys do, who are your target customers and what scale are you operating at? >>>
Thought Leaders in Big Data: Interview with Jim Swift, CEO of Cortera (Part 2)
Sramana Mitra: Let’s double click down into this particular use case. What happens? You take the customer database of this logistics provider, and you do a characterization of each of their leads and then you do clustering?
Jim Swift: We have as much data on companies as anyone I have ever seen, if not more. We have insight into things like purchase behaviors. We know [there is] about approximately $1.7 trillion in spending by U.S. companies on an annual basis. We put it into 45 categories, and we roll it up into different levels. This helps us get a more granular view into what companies are doing. We also bring in other behavioral data about hiring, news events, and all kinds of other things as well as the traditional demographic data, public record information, and others. We append all that information to the client’s customer base. So, they send us a file with their customers, we append this information and then use modeling techniques and define what equations, clusters and relationships best describe their customer base, using the information we have on them. Then we take that and project it against all the companies we know about that aren’t their customers. We come back with various confidence levels and ways to score the prospects they should go after. They then upload [these projections] into their sales force automation system, and they proceed to make the calls. >>>
Thought Leaders in Big Data: Interview with Jim Swift, CEO of Cortera (Part 1)
Jim Swift is the chief executive officer of Cortera, a company that provides B2B solutions such as purchase behavior data and business monitoring solutions. Jim studied at the Rochester Institute of Technology and has many years of experience in database marketing. In this interview, Jim talks in detail about how Cortera provides companies with useful information to minimize risk and gives step-by-step insight for entrepreneurs on how to proceed when entering this space.
Sramana Mitra: Jim, tell us about Cortera. What do you do? What kind of customers do you have? What kind of technology do you use, and what kind of problems do you solve? >>>
Thought Leaders in Big Data: Interview with Seth Redmore, VP of Product Management at Lexalytics (Part 5)
Sramana Mitra: What else is interesting in your story?
Seth Redmore: The problems – what doesn’t work well. I personally think that is the most fun part. The classic part of text analysis is humor and sarcasm. It is very hard to tell. For example, someone says, “I love the Apple store.” If that is all they say in the Tweet, then they like the Apple store. But if the previous Tweet was, “I spent three hours waiting in line and the genius was an idiot,” that changes the context of that Tweet. >>>
Thought Leaders in Big Data: Interview with Seth Redmore, VP of Product Management at Lexalytics (Part 4)
Sramana Mitra: What big data applications do you see yourselves being stuck in to? Certain big data applications or big data genres have a certain text analytics component to them.
Seth Redmore: So you have a text. That is the first bit. But there is stuff that can be turned into text. That is what is interesting. Clients are asking, “How do I do media monitoring, and how do I take what people are saying on Twitter and be able to figure out if that has some sort of predictive implications for my lines of business.” That is one of the things that people are working on right now. >>>
Thought Leaders in Big Data: Interview with Sasha Gilenson, CEO of Evolven (Part 7)
Sramana Mitra: I think the bottom line is they are not architecturally in a place to handle a dynamic environment.
Sasha Gilenson: A truly dynamic environment, yes. Let’s say there is an urgent issue with a particular application. The system administrator runs and accesses the machine to fix the issue; because it is so critical, it needs to be fixed immediately. So these tools essentially say, “Someone bypassed my model and I don’t recognize this change, so what I will do is reverse the change back to my model.” That is the way they think. Instead of helping to assess this change and maybe to distribute it to the rest of the machines, what they will do is delete this change. That is how they operate. >>>
Thought Leaders in Big Data: Interview with Sasha Gilenson, CEO of Evolven (Part 6)
Sramana Mitra: At the same time, that raised quite a bit of funding in that area. Is that correct?
Sasha Gilenson: Yes. Recently Puppet [Labs], an infrastructure as a code company, which automates infrastructure measurement popped up. They just raised $50 million from VMware. There are many other companies in this space. With the increasing pace of change and the growing complexity of the environments, IT has no other way to cope with it than to automate. This is why this space is hot. The automation in this space is still second generation. Puppet is still involved in a lot of expertise and work from IT itself, so there is a long way for improvement. >>>
