{"id":22777,"date":"2019-06-11T08:30:26","date_gmt":"2019-06-11T15:30:26","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=22777"},"modified":"2019-06-12T09:12:29","modified_gmt":"2019-06-12T16:12:29","slug":"interview-atif-kureishy-global-vp-emerging-practices-at-teradata","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/06\/11\/interview-atif-kureishy-global-vp-emerging-practices-at-teradata\/","title":{"rendered":"Interview: Atif Kureishy, Global VP, Emerging Practices at Teradata"},"content":{"rendered":"\n
\"\"<\/figure><\/div>\n\n\n\n

I recently caught up with Atif Kureishy, Global VP of Emerging Practices at Teradata<\/a>, during the 2019 edition of the NVIDIA GPU Technology Conference<\/a>, to get a deep dive update for how Teradata is advancing into the fields of AI and deep learning. Based in San Diego, Atif specializes in enabling clients across all major industry verticals, through strategic partnerships to deliver complex analytical solutions built on machine and deep learning. His teams are trusted advisors to the world\u2019s most innovative companies to develop next-generation capabilities for strategic data-driven outcomes in areas of artificial intelligence, deep learning and data science.<\/em><\/p>\n\n\n\n

insideBIGDATA:<\/strong> Can you give us a brief overview of\nyour role with Teradata and the company\u2019s current direction?<\/p>\n\n\n\n

Atif Kureishy:<\/strong> I\u2019ve been at Teradata for two and a\nhalf years now. In that role of promoting AI and deep learning, my teams do\nthree things. Number one, we work with Fortune 500 companies to advance their deep\nlearning capabilities. We also have a separate ML team. My team is focused on\ndeep learning, leveraging a lot of open source, building solutions to solve\nproblems and drive what we call \u201canswers or outcomes.\u201d The second thing we do\nis we take all of that experience, lessons learned, frameworks and toolkits, approaches,\nand IP, and work with our product teams to say, “Look, here’s what we did.\nHere’s what was successful. Here’s what’s not. Here’s what’s hype. Here’s\nwhat’s in the applied research community. Let’s consider how we would bring all\nthat into the technology portfolio of Teradata and shape the roadmaps and shape\nwhere we’re going in the future. As part of that we’re looking to GPU\naccelerate and provide deep learning capabilities into next year. And item\nnumber three is working with media, industry analysts, with the community,\nconferences to help people understand where the hype is, where the reality is,\nand that takes a big portion of our time. We meet with customers to help them\nunderstand what the state-of-the-art is, where the opportunities are, and how\nto get there. <\/p>\n\n\n\n

So that’s\ngenerally me and my team. I have a global remit, so I see all geographies.\nChina, Japan, Europe, North America are probably the areas that we focus on\nmost with Singapore, Australia a little bit, but really, those are the\nterritories that have the most investment and spend of our customers. <\/p>\n\n\n\n

The industry\nthat we focus on most, not surprisingly, is financial services, in particular\nbanking. But we also deal with telecommunications, media\/entertainment, retail,\nand manufacturing. Those are always the early adopters of tech, so that’s what\nwe’re seeing.<\/p>\n\n\n\n

insideBIGDATA: <\/strong>How is Teradata changing their\nmessage in light of changes in the tech industry these days?<\/p>\n\n\n\n

Atif Kureishy:<\/strong> The reason I came to Teradata is\nbecause the company was in the midst of a strategic transformation and I was enamored\nwith that transformation. We’ve done MPP really well, serving the largest\nenterprises. Over time those customers get acclimated and they say, “Well,\nit’s great. We keep the lights on, but I want to do more with this data. You’re\nhelping me with the BI reporting, but increasingly I want to apply more\nadvanced analytics, and I want to get into the predictions.\u201d So Teradata\nrealized it needed to be more of an analytics platform, and enable our\ncustomers to do modern data science and modern analytics.<\/p>\n\n\n\n

To do this\nwe needed to bring in new talent, and also transform the product. It’s more\nthan just a SQL-based engine. It’s appreciating that you have different types\nof analytics and computation that you need to apply on that data, relational\ndata. That’s semi-structured, unstructured, image, voice, etc. Increasingly our\ncustomers want to apply multimodal types of analysis. Teradata is now on this pivot\nto approach this transformation.<\/p>\n\n\n\n

Recognizing\nthat if you’re moving to get out of the SQL data infrastructure game, then the\nbuyers change and the marketing and the go-to market changes. Who has the\ndollars for AI? It’s really the line-of-business that has the dollars for AI\nalong with the AI agenda. In order to engage with the senior executive in the\nbusiness, you try to help that leader with any number of outcomes, like people\nprediction or anomaly detection or yield optimization. You need to speak their\nlanguage, understand their business, but ultimately bring data together and\napply machine and deep learning to those problems. Narrowly, I will say I am\nfocused on specifically deep learning because we focus on new value creation. If\nyou can apply deep learning in the enterprise and solve the problem of AI explainability\nthen you can do things you’ve never been able to do before. For instance, in\nworking with a very large manufacturer, doing high pressure hose fabrication,\ntheir default rates of scrap was off the charts. They had something like 30% of\nwhat they manufactured end up as scrap. When we took a closer look at that, we\nfound they had sensors that were misaligned and as a result they had many false\npositives, and they had a lot of teardown from the quality teams for\nhigh-pressure hoses that were completely fine. So if you can take process data,\nand sensor data, and apply neural networks to increase the accuracy of when you\npredict a defect, the net effect is hundreds of millions of dollars.<\/p>\n\n\n\n

In another\ninstance, you can start to understand customer behaviors when they come into a\nstore by using at computer vision techniques. This is what Amazon is doing with\ntheir cashier-less stores. You have sensors in really high definition cameras,\nand you can start to track how customers traverse through the store, and start\nto appreciate how long they dwell and queue and different things like that. You\ncan optimize the layout of your store. You can put digital signage in the right\nplaces. You can optimize your staff deployment. We’ve been working with the\nlargest retailers for a very long time and many are interested in this\ntechnology. How do we change the game and allow them to do this? It’s all\npredicated on data, but obviously, you need that data, and you need to analyze\nthat data. So that’s why Teradata recommends that they need to get into that\nsort of analytic space and into the predictive space, hence the transformation.<\/p>\n\n\n\n

insideBIGDATA: <\/strong>That’s a pretty big refocus. What was the time frame of this pivot for Teradata?<\/p>\n\n\n\n

Atif Kureishy:<\/strong>  It\u2019s been over the last three years. We’ve\nbeen internally focused on it, but if you’ve seen the sort of rebranding and\nrefresh of our go-to-market, we’re focused on pervasive data intelligence. Let\nme break down those three words. \u201cPervasive\u201d in the sense of you need to be\nable to process all this different types of machine data, log data, structure\ndata, curated data, etc. and process it where it is \u2013 in the cloud, on-prem, in\nobject stores, in relation stores. Increasingly if you do analytics on samples\nof data, you don’t really get the full view. Scale becomes a big issue and Teradata\nhas always been about performance at scale. The second word, \u201cdata,\u201d is our\nlegacy. Finally, \u201cintelligence\u201d is the appreciation of artificial intelligence,\nand the way of prediction and better insights and understanding is on that data,\nat scale, everywhere.<\/p>\n\n\n\n

So in a lot\nof ways, it’s not a dramatic pivot. We’ve been doing distributed algebra and\nanalytics on Teradata forever – the SQL-based capabilities. Now you’re talking\nabout linear algebra, discrete math, calculus, differential equations. You’re\napplying more sophisticated types of math. When you talk about deep learning,\nyou’re applying more sophisticated math on that data. But what everyone\nstruggles with is how you do that at scale. We’ve got the scaling part figured\nout. You need to reach beyond just algebra into geometry, which is what you\nneed – Euclidean geometry in a lot of computer vision problems. But at the end\nof the day, it’s just math at scale on data, and so that’s what we’re talking\nabout.<\/p>\n\n\n\n

insideBIGDATA: <\/strong>And that’s what NVIDIA brings to the\ntable, yes? How are you guys working with NVIDIA?<\/p>\n\n\n\n

Atif Kureishy:<\/strong> Absolutely.<\/p>\n\n\n\n

We’ve been a\npartner with NVIDIA for about one year, part of the Services Delivery Program (SDP).\nIf we engage with the customers and help them solve deep learning problems,\nthat’s going to push computation on the GPUs. So obviously, that’s very\nharmonious. <\/p>\n\n\n\n

Coming up next\nyear, we’re actually putting compute into our Vantage platform. You’re running\nworkload on the Teradata Vantage platform, and that data and computation will\nbe processed on GPUs for training, and serving the inference side. Ultimately,\nyou’re solving answers and problems for our customers. Our 2019 focus is\nVantage. We have all the computation and data, along with Teradata Everywhere,\nAWS, and Azure. But let’s forget about all of that. The idea is if you can\ndeliver this in an \u201cas-a-service\u201d manner which really means in a more\nconsumable way to align a business executive. <\/p>\n\n\n\n

We can do it\nin a much more innovative and creative way using machine and deep learning. But\nwe\u2019re not going to bring all that complexity. We\u2019re going to give you a\nsubscription or some straightforward consumption-based method offering\ndashboards, data pipelines, ML frameworks, data labeling\/annotation schemes, and\nGPU infrastructure. Every enterprise leader in the business wants all of that\nsophistication without all the complexity, so that’s increasingly what we’re\nfocused on.<\/p>\n\n\n\n

insideBIGDATA: <\/strong>What’s the timeframe for these\nsolutions? <\/p>\n\n\n\n

Atif Kureishy:<\/strong> It’s an evolution. You’ll see this\ncarrying along a multi-year strategy. A lot of folks are doing this in the\ncloud, so we embrace those partners where it makes sense. But the Fortune 100, what\nwe call \u201cmegadata\u201d customers because of data gravity, privacy, security, etc. You\nhave to allow them to get to the cloud and that’s a part of our Teradata Everywhere\nstrategy. You also have to allow them to do analytics at scale in that same\nTeradata Everywhere environment. By the way, deep learning is just an evolution\nof ML. ML is just an evolution of some of the other modeling and simulation\ntechniques that we’ve been using. So you have to take customers on that path.<\/p>\n\n\n\n

It’s\navailable, or will be available, on AWS and Azure, and on managed cloud. So\nthose things are available now, so folks can come on board now, and then when the\ndeep learning capabilities come out, they’ll have access to that technology as\nwell. It’ll be part of a first class environment with Vantage. The idea is that\nwe’re going to take them on that journey, and be there for them when they need\nit.<\/p>\n\n\n\n

insideBIGDATA: <\/strong>Can you describe a particularly use\ncase? <\/p>\n\n\n\n

Atif Kureishy:<\/strong> Yes, there were some creative\napplications of deep learning at Danske Bank with a variety of transactions\ninvolving issuing bank, and receiving bank. We decided to extend and add new\nfeatures around everything else we know about the transactions, such as IP\naddresses, Mac addresses, and other derivative information. Then we observed\nthat these transactions occur over time, so we were actually looking at\nsequences of transactions rather than individual transactions. A lot of machine\nlearning approaches today look at a transaction in isolation in order to do comparative\nanalysis and anomaly detection. But we were actually looking at sequences of transactions\nso there’s better signal in that detection.<\/p>\n\n\n\n

So we took\nthe sequences arranged over time and we turn that into a model to emulate pixels\non an image. We literally took those transactional features and then did some\nspatial correlations model techniques and we turned it into image.<\/p>\n\n\n\n

Then we\napplied convolutional neural networks (CCNs) to the image and that became a best-performing\nmethod. We did time-aware LSTMs and other types of recurrent neural networks\n(RNNs). The derivative benefit of this approach was that the auditors and regulators\ncould actually see fraud visually. We showed this kind of pixelization where\nthe intensity of a pixel would actually demonstrate fraud. They got it, and then\napplied some other techniques to recognize attributes that contribute to the classifier\nof false deny or approve. This was enough for us to understand what these black\nbox models are doing. <\/p>\n\n\n\n

In the end,\nthis solution was an ensemble of six different techniques. We had some logistic\nregression approaches, some boosted trees, and some other GLMs. Then we used a deep\nneural network. It was such a dramatic improvement. We worked with them to\nbuild their data science capabilities so that they could support this in the\nfuture, and that’s why it was such a transformational effort.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> Well, this has been great. I appreciate\nthe opportunity to get a Teradata update.<\/p>\n\n\n\n

Atif Kureishy:<\/strong> My pleasure. <\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/06\/Atif-Kureishy.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-5Vn","jetpack-related-posts":[{"id":19129,"url":"https:\/\/insidebigdata.com\/2017\/10\/13\/state-artificial-intelligence-enterprise\/","url_meta":{"origin":22777,"position":0},"title":"State of Artificial Intelligence for the Enterprise","date":"October 13, 2017","format":false,"excerpt":"A survey conducted in July 2017 by Vanson Bourne, the Teradata \u201cState of Artificial Intelligence for Enterprises\u201d report captures the here and now for AI \u2014 how executive decision-makers are investing in AI today, the expected return on investment (ROI), what barriers lie ahead and how businesses craft a solid\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2017\/10\/Teradata_AI_report_fig3.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":17490,"url":"https:\/\/insidebigdata.com\/2017\/03\/29\/teradata-launches-intellicloud-blending-superior-data-analytic-saas-expanded-deployment-choice\/","url_meta":{"origin":22777,"position":1},"title":"Teradata Launches IntelliCloud \u2013 Blending Superior Data and Analytic SaaS with Expanded Deployment Choice","date":"March 29, 2017","format":false,"excerpt":"Teradata (NYSE: TDC), a leading analytics solutions company, announced Teradata IntelliCloudTM, its next generation secure managed cloud offering that provides data and analytic software as a service (SaaS). It is available with new deployment choices including Teradata IntelliFlexTM, the company\u2019s flagship enterprise data warehouse platform that Teradata will deploy and\u2026","rel":"","context":"In "Analytics"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2014\/04\/teradata_logo_mi.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":15158,"url":"https:\/\/insidebigdata.com\/2016\/06\/08\/teradata-aster-connector-for-spark-drives-data-democracy\/","url_meta":{"origin":22777,"position":2},"title":"Teradata Aster Connector for Spark Drives Data Democracy","date":"June 8, 2016","format":false,"excerpt":"Teradata (NYSE: TDC), the big data analytics and marketing applications company, introduced the Teradata\u00ae Aster\u00ae Connector for Spark, an industry-first integration of Apache Sparkanalytics with Teradata Aster Analytics.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2014\/04\/teradata_logo_mi.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":13894,"url":"https:\/\/insidebigdata.com\/2015\/10\/19\/breakthrough-teradata-software-pushes-the-analytic-edge-with-internet-of-things-data\/","url_meta":{"origin":22777,"position":3},"title":"Breakthrough Teradata Software Pushes the Analytic Edge with Internet of Things Data","date":"October 19, 2015","format":false,"excerpt":"Teradata (NYSE: TDC), the big data analytics and marketing applications company, today announced two new breakthrough software capabilities that empower business users to uncover and operationalize the insights hidden within Internet of Things (IoT) data.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2014\/04\/teradata_logo_mi.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":13924,"url":"https:\/\/insidebigdata.com\/2015\/10\/24\/teradata-software-pushes-the-analytic-edge-with-internet-of-things-data\/","url_meta":{"origin":22777,"position":4},"title":"Teradata Software Pushes the Analytic Edge with Internet of Things Data","date":"October 24, 2015","format":false,"excerpt":"Teradata (NYSE: TDC), the big data analytics and marketing applications company, announced two new software capabilities that empower business users to uncover and operationalize the insights hidden within Internet of Things (IoT) data.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2014\/04\/teradata_logo_mi.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":13827,"url":"https:\/\/insidebigdata.com\/2015\/10\/07\/teradata-expands-market-opportunity-for-industry-leading-data-warehouse-on-amazon-web-services\/","url_meta":{"origin":22777,"position":5},"title":"Teradata Expands Market Opportunity for Industry-Leading Data Warehouse on Amazon Web Services","date":"October 7, 2015","format":false,"excerpt":"Teradata Corp. (NYSE: TDC), the big data analytics and marketing applications company, announced today it is making its Teradata Database, a leading data warehousing and analytic solution, available for cloud deployment on AWS to support production workloads.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2014\/04\/teradata_logo_mi.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/22777"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=22777"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/22777\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/22778"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=22777"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=22777"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=22777"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}