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Interview: Joe Otto, Alpine on Selecting the Right Big Data Vendor

We discuss Big Data vendor landscape, key relevant questions, Alpine's competitive differentiation, important qualities sought in data scientists, and more.

Joe OttoJoe Otto is President and CEO of Alpine Data Labs. In his role, Mr. Otto is responsible for Alpine’s strategic direction, growth initiatives and overall execution. Prior to joining Alpine, Joe served for five years as the Senior Vice President of Sales and Services for Greenplum, where he established Greenplum’s commercial footprint and developed it into a global business.

Joe’s 30 year technology career includes significant leadership roles at Sun Microsystems, Cisco Systems, and EMC, as well as executive leadership positions in early stage startups in Artificial Intelligence, Networking and CRM. Throughout his career Joe has developed high performance sales and marketing teams, driving innovation to deliver business transformation and extreme growth. Joe is a graduate of The Ohio State University with a Bachelors degree in Electrical and Computer Engineering.

First part of interview.

Here is second part of my interview with him:

Anmol Rajpurohit: Q5. Based on your expertise as a Sales and Marketing leader, what are the key factors impacting the purchasing decision of Big Data solutions by C-level executives of large firms? According to you, what should be the key questions they should ask their Big Data vendor, in order to ensure successful pursuit of their business goals?

Big Data vendor solutionsJoe Otto: Many C-level executives in the market for a “Big Data solution” today find themselves fairly confused by the messages they hear from technology vendors. There's a lot of hype and many vendors are throwing around the term "Big Data" in an attempt to hop on the bandwagon.

There are two key criteria to consider to make sure you've acquired the right technology to succeed with Big Data. First, Hadoop and Big Data are not the same.

In fact, most enterprise environments are heterogeneous consisting of a combination of traditional data sources, MPP databases AND Hadoop. Look for solutions that don’t force you to change but rather, application that embrace your data environment and focus on driving more value from it, quickly.

The second criteria to keep in mind is that Data Science is NOT dead. Data scientists remain the foundation of the predictive enterprise and companies who win with data shouldn't compromise on their data science values. Partner with a vendor who can help you scale Data Science across your company so that the work is made available broadly across the organization.

Visualization is a great tool to understand what's in your data, but visualization tools can't replace serious math.

AR: Q6. How does Alpine distinguish itself from the increasing competition, particularly from other successful startups such as Skytree, Revolution Analytics and Rapid-I?

CompetitionJO: Most of the competitive alternatives are desktop-based or point solutions without any collaborative capability. Alpine Chorus is a modern, web application whose main tenant is collaboration. On top of collaboration and search it provides modeling and machine learning under the same roof. Also, we are focused on “No-Data Movement” deployment. Meaning, that regardless if a company’s data is in Hadoop or MPP Database, Alpine Chorus sends instructions out without ever moving data. This technology, called In-Cluster Analytics allows for massive savings (from storage to management) and unbound scalability.

Since the data hasn’t been moved to a dedicated analytical server, Alpine Chorus can scale across your entire set of Hadoop Clusters for example. It is different from competing startups because it is web, collaborative and has In-Cluster technology. It is also different because it has an End-to-End approach so users can work from data transformation to modeling and analysis. Chorus also provides built-in search capabilities. Just like they do with Google, users can search for all types of information, from people, to datasets, to data projects and others.

AR: Q7. What key qualities do you look for when interviewing for Data Science related positions for your company?

JO: It’s as important for our data scientists to not just be skilled as data scientists, but to also be adept consultants and coaches for our customers. They must be able to expertly Data Sciencemanipulate databases, collaborate with team members and successfully communicate analyses to those outside of world of data. It’s important for our data scientists to be good communicators so that they could effectively impart the value of predictive analytics. It’s not enough to just convey the numbers and percentages, but the story needs to be told n the context of the business benefit so that all stakeholders buy into the effort. In addition, time management, planning and the ability to be agile are skills that our data scientists possess.

Data JujitsuAR: Q8. What are your favorite books or blogs on Data Science?

JO: There are so many good books and blogs on the subject but a few to note — The Data Jujitsu; Big Data: A Revolution that Will Transform How We Live, Work and Think; Lean Analytics: Use Data to Build a Better Startup Faster; Secrets of Analytical Leaders: Insights from Information Insiders; Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die; or Keeping with the Quants by Tom Davenport.


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