10 Must-Read Quotes From Industry Leaders At Cypher 2017

Cypher 2018 is scheduled to take place from 26 to 28 September, and just like its previous editions, we are looking forward to hosting a number of powerful speakers. Reminiscing some of the interesting insights from the speakers from the last edition, we bring you a compilation of 10 interesting quotes from the industry’s very best. For more information on Cypher, visit here.

1| Sandeep Mittal, Managing Director, Cartesian Consulting:

A compelling and purposeful narrative woven from data is what every analytics professional needs to create. Data should be something that is compelling, that people want to consume and it has a purpose. Though it sounds simple, it is not. But that’s what storytelling is to me. Many a times storytelling is confused with visualisation, which is an important part of it, but you can also narrate a great story with data and no visualisation. If you have visualisation, that helps but it is not a necessary ingredient.

2| Atul Jalan, CEO And MD, Manthan Systems:

The over-promise and under-delivery of artificial intelligence is not new. You can see this with most technology trends. We overestimate the power of technology in short-term and highly under appreciate its capability to change things in a long term. I would like to think that technology evolution is very similar to biological evolution — they don’t have a planned and deterministic path but rely on environmental factors, and move ahead by crossovers and mutations. Similarly, various factors have brought AI to a place where it is today.

3| S Vaitheeswaran, Group CEO, Manipal Global Education Services Private Limited:

It may be great to work for the companies and people, but in a country like ours, it is important to understand how big data, analytics and similar technologies can impact our social ecosystem as well. There are companies coming in this space, for instance, using predictive analytics to manage the crowd in Kumbh Mela. It is time that more companies come into this space with an intention of serving the community, to make this industry larger by using complex tools.

4| Ashish Sharma, Co-Founder And COO, BRIDGEi2i:

In today’s scenario, what is really changing is not the business problems or its transformational needs, but it is the data which is changing the shape and size to take digital dimensions. And we are giving the solutions a new name today, AI. But even as AI becomes increasingly pervasive in the future, the fact is, AI is nothing but a part of analytics.

5| Pinky Sahu, General Manager And Head Of Analytics (India), Wipro:

Starting small is the key but the real success is when the solutions can be implemented for long-term solutions. The trick is to operationalise analytics to modify and enhance customer information. It can be done by prioritising tasks, delivering results in pieces, stitching them all together and finally when you have few successes under your belt, that is when you can actually thread them all together and drive operationalisation. It is important to bridge the gap using analytics, otherwise, there will be a set of people still relying on Excel or their gut feeling.  

6| Kapil Malhotra, Head Of Analytics, Pepsico:

The key to setting up analytics business is to take time to embrace changes and not directly jump into designing things. Also, hiring ‘right’, not the ‘best’ matters. There is an excellent talent pool available today but it is important that there is a cultural fitment so that new members fit quite efficiently. They should be go-getters. As we cannot get all the skills in one person, it is important to organise skill sets and hire according to a pyramidal structural, at least for advanced analytics skills.

7| Ashish Singru, Senior Director, Finance And Analytics, eBay:

Every wave of technology is different. Earlier, there was a term called cybernation which was used for computerisation and now it is AI and machine learning that has created a lot of anxiety. Past performance is no predictor of future and you have to be aware of a series of changes that analytics industry is going through. Surviving in this industry is more important for professionals who are in this space than thriving. Organisations are also trying out different models to see where analytics fits into their company. You have to be aware of these changes as you have the ability to convert these evolving technologies into threat or an opportunity.

8| Raghavendra Singh, Research Staff Member, IBM Research:

There has been much talk about the need for creating engagement among the customers on e-commerce sites. There has been a presence of an assistant at a brick and mortar shop to make buyers more at ease. A pair of new image-analysis solutions could open the door for powerful e-commerce and retail applications that can significantly improve shopping experiences or create new marketing opportunities. The advantages of visual product discovery here are many folds. For instance, it can surface a greater proportion of a product catalogue and find products in separate categories that a customer may not have otherwise encountered.

9| Anand S, CEO, Gramener:

The role of analysts is increasingly changing from executing analysis to interpreting analysis. This is made difficult by the fact that we have more black box models that are accurate than white box models. What is helping resolve that is the interaction with these models visually and analytically to help interpret what these models mean and how they work. It is important to play around with models and help people understand it rather than worrying about the model being optimal. The intent here is not to make model smarter, the model is already smart enough and we have ways to optimise it. Focus on communication, it will make a huge difference.

10| Avi Patchava, VP, Data Sciences And Machine Learning, InMobi:

When it comes to AI innovations, Adtech is going to be a driving sector. It has all the ingredients to explore with AI. The reasons that make AI special in Adtech are — availability of diverse data, as there is ample data already flowing in the system. Second is the availability of engineering talent that is required to build AI models. Third is to draw top talents from data science and artificial Intelligence into AdTech. The fact that the mobile advertising global marketplace is closing on a level of complexity and speed matched only by the financial trading system, one can quickly see the scope of impact from analytics, and AI-enabled systems.