{"id":4523,"date":"2017-12-08T08:50:46","date_gmt":"2017-12-08T08:50:46","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=4523"},"modified":"2017-12-08T08:52:18","modified_gmt":"2017-12-08T08:52:18","slug":"on-artificial-intelligence-and-analytics-interview-with-narendra-mulani","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2017\/12\/on-artificial-intelligence-and-analytics-interview-with-narendra-mulani\/","title":{"rendered":"On Artificial Intelligence and Analytics. Interview with Narendra Mulani"},"content":{"rendered":"

“You can\u2019t get good insights from bad data, and AI is playing an instrumental role in the data preparation renaissance.”–Narendra Mulani<\/strong><\/p><\/blockquote>\n

I have interviewed Narendra Mulani<\/strong>, chief analytics officer, Accenture Analytics.<\/em><\/p>\n

RVZ<\/p>\n

Q1. What is the role of Artificial Intelligence in analytics?<\/strong><\/p>\n

Narendra Mulani<\/strong>: Artificial Intelligence<\/a> will be the single greatest change driver of our age. Combined with analytics<\/a>, it\u2019s redefining what\u2019s possible by unlocking new value from data, changing the way we interact with each other and technology, and improving the way we make decisions. It\u2019s giving us wider control and extending our capabilities as businesses and as people.<\/p>\n

AI is also the connector and culmination of many elements of our analytics strategy including data, analytics techniques, platforms and differentiated industry skills.<\/p>\n

You can\u2019t get good insights from bad data, and AI is playing an instrumental role in the data preparation renaissance.
\nAI-powered analytics essentially frees talent to focus on insights rather than data preparation<\/a> which is more daunting with the sheer volume of data available. It helps organizations tap into new unstructured, contextual data sources like social, video and chat, giving clients a more complete view of their customer. Very recently we acquired Search Technologies<\/a> who possess a unique set of technologies that give \u2018context to content\u2019 \u2013 whatever its format \u2013 and make it quickly accessible to our clients.
\nAs a result, we gain more precise insights on the \u201cwhy\u201d behind transactions for our clients and can deliver better customer experiences that drive better business outcomes.<\/p>\n

Overall, AI-powered analytics will go a long way in allowing the enterprise to find the trapped value that exists in data, discover new opportunities and operate with new agility.<\/p>\n

Q2. How can enterprises become \u2018data native\u2019 and digital at the core to help them grow and succeed?<\/strong><\/p>\n

Narendra Mulani<\/strong>: It starts with embracing a new culture which we call \u2018data native\u2019. You can\u2019t be digital to the core if you don\u2019t embed data at the core. Getting there is no mean feat. The rate of change in technology and data science is exponential, while the rate at which humans can adapt to this change is finite. In order to close the gap, businesses need to democratize data and get new intelligence to the point where it is easily understood and adopted across the organization.
\nWith the help of design-led analytics and app-based delivery, analytics becomes a universal language in the organization, helping employees make data-driven decisions, collaborate across teams and collectively focus efforts on driving improved outomes for the business.<\/p>\n

Enterprises today are only using a small fraction of the data available to them as we have moved from the era of big data<\/a> to the era of all data. The comprehensive, real-time view businesses can gain of their operations from connected devices is staggering.<\/p>\n

But businesses have to get a few things right to ensure they go on this journey.<\/p>\n

Understanding and embracing convergence of analytics and artificial intelligence is one of them. You can hardly overstate the impact AI will have on mobilizing and augmenting the value in data, in 2018 and beyond. AI will be the single greatest change driver and will have a lasting effect on how business is conducted.<\/p>\n

Enterprises also need to be ready to seize new opportunities \u2013 and that means using new data science<\/a> to help shape hypotheses, test and optimize proofs-of-concept and scale quickly. This will help you reimagine your core business and uncover additional revenue streams and expansion opportunities.<\/p>\n

All this requires a new level of agility. To help our clients act and respond fast, we support them with our platforms, our people and our partners. Backed by deep analytics expertise, new cloud-based systems and a curated and powerful alliance and delivery network, our priority is architecting the best solution to meet the needs of each client. We offer an as-a-service engagement model and a suite of intelligent industry solutions that enable even greater agility and speed to market.<\/p>\n

Q3. Why is machine learning (ML) such a big deal, where is it driving changes today, and what are the big opportunities for it that have not yet been tapped?<\/strong><\/p>\n

Narendra Mulani<\/strong>: Machine learning<\/a> allows computers to discover hidden or complex patterns in data without explicit programming. The impact this has on the business is tremendous\u2014it accelerates and augments insights discovery, eliminates tedious repetitive tasks, and essentially enables better outcomes. It can be used to do a lot of good for people, from reading a car\u2019s license plate and forcing the driver to slow down, to allowing people to communicate with others regardless of the language they speak, and helping doctors find very early evidence of cancer.<\/p>\n

While the potential we\u2019re seeing for ML and AI in general is vast, businesses are still in the infancy of tapping it. Organizations looking to put AI and ML to use today need to be pragmatic. While it can amplify the quality of insights in many areas, it also increases complexity for organizations, in terms of procuring specialized infrastructure or in identifying and preparing the data to train and use AI, and with validating the results. Identifying the real potential and the challenges involved are areas where most companies today lack the necessary experience and skills and need a trusted advisor or partner.<\/p>\n

Whenever we look at the potential AI and ML have, we should also be looking at the responsibility that comes with it. Explainable AI and AI transparency are top of mind for many computer scientists, mathematicians and legal scholars.
\nThese are critical subjects for an ethical application of AI \u2013 particularly critical in areas such as financial services, healthcare and life sciences \u2013 to ensure that data use is appropriate, and to assess the fairness of derived algorithms.
\nWe need recognize that, while AI is science, and science is limitless, there are always risks in how that science is used by humans, and proactively identify and address issues this might cause for people and society.<\/p>\n

————————————————<\/p>\n

\"Narendra1\"<\/a><\/p>\n

Narendra Mulani<\/strong> is Chief Analytics Officer of Accenture Analytics<\/a>, a practice that his passion and foresight have helped shape since 2012.<\/em><\/p>\n

A connector at the core, Narendra brings machine learning, data science, data engineers and the business closer together across industries and geographies to embed analytics and create new intelligence, democratize data and foster a data native culture.<\/em><\/p>\n

He leads a global team of industry and function-specific analytics professionals, data scientists, data engineers, analytics strategy, design and visualization experts across 56 markets to help clients unlock trapped value and define new ways to disrupt in their markets. As a leader, he believes in creating an environment that is inspiring, exciting and innovative.<\/em><\/p>\n

Narendra takes a thoughtful approach to developing unique analytics strategies and uncovering impactful outcomes. His insight has been shared with business and trade media including Bloomberg, Harvard Business Review, Information Management, CIO magazine, and CIO Insight. Under Narendra’s leadership, Accenture’s commitment and strong momentum in delivering innovative analytics services to clients was recognized in Everest Group’s Analytics Business Process Services PEAK Matrix\u2122 Assessment in 2016.<\/em><\/p>\n

Narendra joined Accenture in 1997. Prior to assuming his role as Chief Analytics Officer, he was the Managing Director \u2013 Products North America, responsible for delivering innovative solutions to clients across industries including consumer goods and services, pharmaceuticals, and automotive. He was also managing director of supply chain for Accenture Management Consulting where he led a global practice responsible for defining and implementing supply chain capabilities at a diverse set of Fortune 500 clients.<\/em><\/p>\n

Narendra graduated with a Bachelor of Commerce degree at Bombay University, where he was introduced to statistics and discovered he understood probability at a fundamental level that propelled him on his destined career path. He went on to receive an MBA in Finance in 1982 as well as a PhD in 1985 focused on Multivariate Statistics, both from the University of Massachusetts. Education remains fundamentally important to him.<\/em><\/p>\n

As one who logs too many frequent flier miles, Narendra is an active proponent of taking time for oneself to recharge and stay at the top of your game. He practices what he preaches through early rising and active mindfulness and meditation to keep his focus and balance at work and at home. Narendra is involved with various activities that support education and the arts, and is a music enthusiast. He lives in Connecticut with his wife Nita and two children, Ravi and Nikhil.<\/em><\/p>\n

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

–Accenture Invests in and Forms Strategic Alliance with Leading Quantum Computing Firm 1QBit<\/a><\/p>\n

-Accenture Forms Alliance with Paxata to Help Clients Build an Intelligent Enterprise by Putting Business Users in Control of Data<\/a><\/p>\n

–Apple & Accenture Partner to Create iOS Business Solutions<\/a><\/p>\n

–Accenture Completes Cloud-Based IT Transformation for Towergate, Helping Insurance Broker Improve Its Operations and Reduce Annual IT Costs by 30 Percent <\/a><\/p>\n

–Accenture Acquires Search Technologies to Expand Its Content Analytics and Enterprise Search Capabilities<\/a><\/p>\n

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

–How Algorithms can untangle Human Questions. Interview with Brian Christian. ODBMS Industry Watch, March 31, 2017<\/a><\/p>\n

–Big Data and The Great A.I. Awakening. Interview with Steve Lohr. ODBMS Industry Watch, December 19, 2016<\/a><\/p>\n

–Machines of Loving Grace. Interview with John Markoff. ODBMS Indutry Watch, August 11, 2016<\/a><\/p>\n

–On Artificial Intelligence and Society. Interview with Oren Etzioni. ODBMS Industry Watch, January 15, 2016<\/a><\/p>\n

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

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“You can\u2019t get good insights from bad data, and AI is playing an instrumental role in the data preparation renaissance.”–Narendra Mulani I have interviewed Narendra Mulani, chief analytics officer, Accenture Analytics. RVZ Q1. What is the role of Artificial Intelligence in analytics? Narendra Mulani: Artificial Intelligence will be the single greatest change driver of our […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[942,35,881,66,97,748,863,1176,1174,350,940,440,1175],"_links":{"self":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4523"}],"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=4523"}],"version-history":[{"count":8,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4523\/revisions"}],"predecessor-version":[{"id":4532,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4523\/revisions\/4532"}],"wp:attachment":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/media?parent=4523"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/categories?post=4523"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/tags?post=4523"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}