{"id":4101,"date":"2016-03-14T08:45:56","date_gmt":"2016-03-14T08:45:56","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=4101"},"modified":"2016-03-17T16:09:11","modified_gmt":"2016-03-17T16:09:11","slug":"on-the-internet-of-things-interview-with-colin-mahony","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2016\/03\/on-the-internet-of-things-interview-with-colin-mahony\/","title":{"rendered":"On the Internet of Things. Interview with Colin Mahony"},"content":{"rendered":"

“Frankly, manufacturers are terrified to flood their data centers with these unprecedented volumes of sensor and network data.”–\u00a0Colin Mahony<\/strong><\/p><\/blockquote>\n

I have interviewed Colin Mahony,\u00a0<\/strong>SVP & General Manager, HPE Big Data Platform. Topics of the interview are:\u00a0The challenges of the Internet of Things, the opportunities for Data Analytics, the positioning of HPE Vertica and HPE\u00a0Cloud Strategy.<\/p>\n

RVZ<\/p>\n

Q1.\u00a0Gartner says 6.4 billion connected \u201cthings\u201d will be in use in 2016<\/a>, up 30 percent from 2015. \u00a0How do you see the\u00a0global Internet of Things (IoT) market developing in the next\u00a0years?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>As manufacturers connect more of their \u201cthings,\u201d they have an increased need for analytics to derive insight from massive volumes of sensor or machine data<\/a>. I see these manufacturers, particularly manufacturers of commodity equipment, with a need to provide more value-added services based on their ability to provide higher levels of service and overall customer satisfaction. Data analytics platforms are key to making that happen. Also, we could see entirely new analytical applications emerge, driven by what consumers want to know about their devices and combine that data with, say, their exercise regimens, health vitals, social activities, and even driving behavior, for full personal insight.
\nUltimately, the Internet of Things<\/a> will drive a need for the Analyzer of Things, and that is our mission.<\/p>\n

Q2. What Challenges and Opportunities bring the\u00a0Internet of Things (IoT)?\u00a0<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>Frankly, manufacturers are terrified to flood their data centers with these unprecedented volumes of sensor and network data. The reason? Traditional data warehouses were designed well before the Internet of Things, or, at least before OT (operational technology<\/a>) like medical devices, industrial equipment, cars, and more were connected to the Internet. So, having an analytical platform to provide the scale and performance required to handle these volumes is important, but customers are taking more of a two- or three-tier approach that involves some sort of analytical processing at the edge before data is sent to an analytical data store. Apache Kafka<\/a> is also becoming an important tier in this architecture, serving as a message bus, to collect and push that data from the edge in streams to the appropriate database, CRM system, or analytical platform for, as an example, correlation of fault data over months or even years to predict and prevent part failure and optimize inventory levels.<\/p>\n

Q3. Big Data: In your opinion, what are the current main demands\/needs in the market?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>All organizations want \u2013 and need – to become data-driven organizations. I mean, who wants to make such critical decisions based on half answers and anecdotal data? That said, traditional companies with data stores and systems going back 30-40 years don\u2019t have the same level playing field as the next market disruptor that just received their series B funding and only knows that analytics is the life blood of their business and all their critical decisions.
\nThe good news is that whether you are a 100-year old insurance company or the next Uber or Facebook, you can become a data-driven organization by taking an open platform approach that uses the best tool for the job and can incorporate emerging technologies like Kafka and Spark<\/a> without having to bolt on or buy all of that technology from a single vendor and get locked in. \u00a0Understanding the difference between an open platform with a rich ecosystem and open source software as one very important part of that ecosystem has been a differentiator for our customers.<\/p>\n

Beyond technology, we have customers that establish analytical centers of excellence that actually work with the data consumers \u2013 often business analysts \u2013 that run ad-hoc queries using their preferred data visualization tool to get the insight need for their business unit or department. If the data analysts struggle, then this center of excellence, which happens to report up through IT, collaborates with them to understand and help them get to the analytical insight \u2013 rather than simply halting the queries with no guidance on how to improve.<\/p>\n

Q4. How do you\u00a0embed\u00a0<\/em>analytics and why is it useful?\u00a0<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>OEM software<\/a> vendors, particularly, see the value of embedding analytics in their commercial software products or software as a service<\/a> (SaaS) offerings. \u00a0They profit by creating analytic data management features or entirely new applications that put customers on a faster path to better, data-driven decision making. Offering such analytics capabilities enables them to not only keep a larger share of their customer\u2019s budget, but at the same time greatly improve customer satisfaction. To offer such capabilities, many embedded software providers are attempting unorthodox fixes with row-oriented OLTP databases<\/a>, document stores<\/a>, and Hadoop<\/a> variations that were never designed for heavy analytic workloads at the volume, velocity, and variety of today\u2019s enterprise. Alternatively, some companies are attempting to build their own big data management systems. But such custom database solutions can take thousands of hours of research and development, require specialized support and training, and may not be as adaptable to continuous enhancement as a pure-play analytics platform.\u00a0Both approaches are costly and often outside the core competency of businesses that are looking to bring solutions to market quickly.<\/p>\n

Because it\u2019s specifically designed for analytic workloads, HPE Vertica<\/a> is quite different from other commercial alternatives. Vertica differs from OLTP DBMS and proprietary appliances (which typically embed row-store DBMSs) by grouping data together on disk by column rather than by row (that is, so that the next piece of data read off disk is the next attribute in a column, not the next attribute in a row). This enables Vertica to read only the columns referenced by the query, instead of scanning the whole table as row-oriented databases must do. This speeds up query processing dramatically by reducing disk I\/O.<\/p>\n

You\u2019ll find Vertica as the core analytical engine behind some popular products, including Lancope<\/a>, Empirix<\/a>, Good Data<\/a>, and others as well as many HPE offerings like HPE Operations Analytics<\/a>, HPE Application Defender<\/a>, and HPE App Pulse Mobile<\/a>, and more.<\/p>\n

Q5. How do you make a decision when it is more appropriate to\u00a0\u201cconsume and deploy\u201d\u00a0Big Data\u00a0on premise, in the cloud, on demand and on Hadoop?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>The best part is that you don\u2019t need to choose with HPE. Unlike most emerging data warehouses as a service where your data is trapped in their databases when your priorities or IT policies change, HPE offers the most complete range of deployment and consumption models. If you want to spin up your analytical initiative on the cloud for a proof-of-concept or during the holiday shopping season for e-retailers, you can do that easily with HPE Vertica OnDemand<\/a>.
\nIf your organization finds that due to security or confidentiality or privacy concerns you need to bring your analytical initiative back in house, then you can use HPE Vertica Enterprise on-premises<\/a> without losing any customizations or disruption to your business. Have petabyte volumes of largely unstructured data where the value is unknown? Use HPE Vertica for SQL on Hadoop<\/a>, deployed natively on your Hadoop cluster, regardless of the distribution you have chosen. Each consumption model, available in the cloud, on-premise, on-demand, or using reference architectures for HPE servers, is available to you with that same trusted underlying core.<\/p>\n

Q6. What are the\u00a0new class of infrastructures called \u201ccomposable\u201d? Are they relevant for Big Data?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>HPE believes that a new architecture is needed for Big Data<\/a> \u2013 one that is designed to power innovation and value creation for the new breed of applications while running traditional workloads more efficiently.
\nWe call this new architectural approach Composable Infrastructure. HPE has a well-established track record of infrastructure innovation and success. HPE Converged Infrastructure<\/a>, software-defined management, and hyper-converged systems have consistently proven to reduce costs and increase operational efficiency by eliminating silos and freeing available compute, storage, and networking resources. Building on our converged infrastructure knowledge and experience, we have designed a new architecture that can meet the growing demands for a faster, more open, and continuous infrastructure.<\/p>\n

Q7. What is HPE\u00a0Cloud Strategy?\u00a0<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>Hybrid cloud<\/a> adoption is continuing to grow at a rapid rate and a majority of our customers recognize that they simply can\u2019t achieve the full measure of their business goals by consuming only one kind of cloud.
\nHPE Helion<\/a> not only offers private cloud deployments and managed private cloud services, but we have created the HPE Helion Network<\/a>, a global ecosystem of service providers, ISVs, and VARs dedicated to delivering open standards-based hybrid cloud services to enterprise customers. Through our ecosystem, our customers gain access to an expanded set of cloud services and improve their abilities to meet country-specific data regulations.<\/p>\n

In addition to the private cloud offerings, we have a strategic and close alliance with Microsoft Azure<\/a>, which enables many of our offerings, including Haven OnDemand, in the public cloud.\u00a0We also work closely with Amazon because our strategy is not to limit our customers, but to ensure that they have the choices they need and the services and support they can depend upon.<\/p>\n

Q8. What are the advantages of an offering like Vertica in this space?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>More and more companies are exploring the possibility of moving their data analytics operations to the cloud. We offer HPE Vertica OnDemand<\/a>, our data warehouse as a service, for organizations that need high-performance enterprise class data analytics for all of their data to make better business decisions now. Built by design to drastically improve query performance over traditional relational database systems, HPE Vertica OnDemand is engineered from the same technology that powers the HPE Vertica Analytics Platform. For organizations that want to select Amazon hardware and still maintain the control over the installation, configuration, and overall maintenance of Vertica for ultimate performance and control, we offer Vertica AMI<\/a> (Amazon Machine Image). The Vertica AMI is a bring-your-own-license model that is ideal for organizations that want the same experience as on-premise installations, only without procuring and setting up hardware. Regardless of which deployment model to choose, we have you covered for \u201con demand\u201d or \u201centerprise cloud\u201d options.<\/p>\n

Q9.\u00a0What is HPE Vertica\u00a0Community Edition?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>We have had tens of thousands of downloads of the HPE Vertica Community Edition<\/a>, a freemium edition of HPE Vertica with all of the core features and functionality that you experience with our core enterprise offering. It\u2019s completely free for up to 1 TB of data storage across three nodes. Companies of all sizes prefer the Community Edition to download, install, set-up, and configure Vertica very quickly on x86 hardware or use our Amazon Machine Image (AMI) for a bring-your-own-license approach to the cloud.<\/p>\n

Q10.\u00a0Can you tell us how\u00a0Kiva.org<\/a>, a non-profit organization, uses on-demand cloud analytics to leverage the internet and a worldwide network of microfinance institutions to help fight poverty?\u00a0<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>HPE is a major supporter of Kiva.org, a non-profit organization with a mission to connect people through lending to alleviate poverty. Kiva.org uses the internet and a worldwide network of microfinance institutions to enable individuals lend as little as $25 to help create opportunity around the world. When the opportunity arose to help support Kiva.org with an analytical platform to further the cause, we jumped at the opportunity. Kiva.org relies on Vertica OnDemand to reduce capital costs, leverage the SaaS delivery model to adapt more quickly to changing business requirements, and work with over a million lenders, hundreds of field partners and volunteers, across the world. To see a recorded Webinar with HPE and Kiva.org, see\u00a0here<\/a>.<\/p>\n

Qx Anything else you wish to add?<\/strong><\/p>\n

Colin Mahony:\u00a0<\/strong>We appreciate the opportunity to share the features and benefits of HPE Vertica as well as the bright market outlook for data-driven organizations. However, I always recommend that any organization that is struggling with how to get started with their analytics initiative to speak and meet with peers to learn best practices and avoid potential pitfalls. The best way to do that, in my opinion, is to visit with the more than 1,000 Big Data experts in Boston from August 29 \u2013 September 1st<\/sup>\u00a0at the HPE Big Data Conference.\u00a0Click here<\/a>\u00a0to learn more and join us for 40+ technical deep-dive sessions.<\/p>\n

————-<\/p>\n

Colin Mahony,\u00a0<\/strong>SVP & General Manager, HPE Big Data Platform<\/p>\n

Colin Mahony leads the Hewlett Packard Enterprise Big Data Platform business group, which is responsible for the industry leading Vertica Advanced Analytics portfolio, the IDOL Enterprise software that provides context and analysis of unstructured data, and Haven OnDemand, a platform for developers to leverage APIs and on demand services for their applications.<\/em>
\n In 2011, Colin joined Hewlett Packard as part of the highly successful acquisition of Vertica, and took on the responsibility of VP and General Manager for HP Vertica, where he guided the business to remarkable annual growth and recognized industry leadership. Colin brings a unique combination of technical knowledge, market intelligence, customer relationships, and strategic partnerships to one of the fastest growing and most exciting segments of HP Software.<\/em><\/p>\n

Prior to Vertica, Colin was a Vice President at Bessemer Venture Partners focused on investments primarily in enterprise software, telecommunications, and digital media. He established a great network and reputation for assisting in the creation and ongoing operations of companies through his knowledge of technology, markets and general management in both small startups and larger companies. Prior to Bessemer, Colin worked at Lazard Technology Partners in a similar investor capacity.<\/em><\/p>\n

Prior to his venture capital experience, Colin was a Senior Analyst at the Yankee Group serving as an industry analyst and consultant covering databases, BI, middleware, application servers and ERP systems. Colin helped build the ERP and Internet Computing Strategies practice at Yankee in the late nineties.<\/em><\/p>\n

Colin earned an M.B.A. from Harvard Business School and a bachelor\u2019s degrees in Economics with a minor in Computer Science from Georgetown University. \u00a0He is an active volunteer with Big Brothers Big Sisters of Massachusetts Bay and the Joey Fund for Cystic Fibrosis.<\/em><\/p>\n

Resources<\/strong><\/p>\n

–What\u2019s in store for Big Data analytics in 2016<\/a>,\u00a0Steve Sarsfield, Hewlett Packard\u00a0Enterprise. ODBMS.org,\u00a03 FEB, 2016<\/p>\n

–What’s New in Vertica 7.2?: Apache Kafka Integration!<\/a>, HPE,\u00a0last edited February 2, 2016<\/p>\n

–Gartner Says 6.4 Billion Connected “Things” Will Be in Use in 2016, Up 30 Percent From 2015<\/a>, Press release, November 10, 2015<\/p>\n

–The Benefits of HP Vertica for SQL on Hadoop<\/a>, HPE,\u00a0July 13, 2015<\/p>\n

–Uplevel Big Data Analytics with Graph in Vertica \u2013 Part 5: Putting graph to work for your business<\/a>\u00a0, Walter Maguire,\u00a0Chief Field Technologist,\u00a0HP Big Data Group, ODBMS.org, 2 Nov, 2015<\/p>\n

–HP Distributed R<\/a>\u00a0,ODBMS.org, \u00a019 FEB, 2015.<\/p>\n

–Understanding ROS and WOS: A Hybrid Data Storage Model<\/a>, HPE,\u00a0October 7, 2015<\/p>\n

Related Posts<\/strong><\/p>\n

–On Big Data Analytics. Interview with Shilpa Lawande<\/a>,\u00a0Source: ODBMS Industry Watch,\u00a0<\/span>Published on December 10, 2015<\/span><\/p>\n

–On HP Distributed R. Interview with Walter Maguire and Indrajit Roy<\/a>,\u00a0Source: ODBMS Industry Watch,\u00a0<\/span>Published on\u00a0<\/span>April 9, 2015<\/p>\n

Follow us on Twitter: @odbmsorg<\/a><\/strong><\/p>\n

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“Frankly, manufacturers are terrified to flood their data centers with these unprecedented volumes of sensor and network data.”–\u00a0Colin Mahony I have interviewed Colin Mahony,\u00a0SVP & General Manager, HPE Big Data Platform. Topics of the interview are:\u00a0The challenges of the Internet of Things, the opportunities for Data Analytics, the positioning of HPE Vertica and HPE\u00a0Cloud Strategy. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[27,35,789,66,97,102,903,193,239,904,906,907,286,909,908,406,412,446,544,905],"_links":{"self":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4101"}],"collection":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/comments?post=4101"}],"version-history":[{"count":9,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4101\/revisions"}],"predecessor-version":[{"id":4110,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4101\/revisions\/4110"}],"wp:attachment":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/media?parent=4101"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/categories?post=4101"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/tags?post=4101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}