From high-quality illustrations to engaging panel discussions, Cypher 2017 generated a lot of exceptional content for the highly tech-driven audience. With more than 80 talks and 100 speakers spread across three days and three parallel sessions, there was a lot of food for brain across areas like analytics, artificial intelligence, machine learning and data science, among others. We bring you a compilation of 10 talks from Cypher 2017 that gained accolades from attendees as well as our audience online.
Also, check out these other interesting talks at Cypher 2017 on our YouTube channel.
1| Story Telling With Data By Sandeep Mittal
One of the most engaging talks at Cypher 2017, this session by the managing director of Cartesian Consulting revolved around the idea of telling stories using data. With intriguing visuals and original illustrations, this talk kept the audience engaged throughout the session. He talked about how storytelling with data is not only about good visualisation or presentation skills, but about knowledge of the plot, handling its narrative, and making the audience own it.
Data, as Mittal puts, must have a purpose. While it may sound simple, in reality, it’s not. What’s worth noting in his talk is the repeatedly stated point that “stories have to be derived from the data.”
2| Panel Discussion: From Talent Dearth To Talent Abundance
Sumeet Bansal of AnalytixLabs, Aatash Shah of Edvancer, Charanpreet Singh of Praxis Business School, Narasimhalu Senthil of Rinalytics Advisors and Gaurav Vohra of Jigsaw Academy were the panellists for the session. They talked about how analytics industry is struggling with the right set of talent.
With analytics, data science and big data becoming the next big thing in major industries, finding, using and retaining the right analytics talent is becoming important. The panel discussion chaired by Sumeet Bansal discussed the nuances of analytics hiring.
As the demand for analytics professionals exceed at an assumingly greater rate than the supply, nurturing the right analytics talent has become the need of the hour. But is an easy road? Right from improving their skills, to teaching them industry-specific standards, and inculcating the best of practices in the candidate, there’s a lot that goes into the making of a right candidate for the analytics industry.
3| Automating Analysis By Anand S
In his talk, Anand S shared how a data analyst goes about automating answers to questions with quantifiable patterns. Delivering interesting statistics from cricket, NCERT, banks and others, he spoke about the importance of understanding model to automate analysis.
Statistics evolved as a means of studying data in the mid-1700s — a time when available data was exploding. During most of the history of statistics, our ability to handle large volumes of data was limited, making sampling essential. Our ability to exhaustively test possibilities was also limited, leading to a hypothesis-driven approach. But with a lot of models currently available, it has become increasingly accessible.
4| Setting Up Analytics At An Early-Stage Company By Ankur Sharma
In this highly engaging talk by the head of analytics and user growth at Instamojo, Sharma shares insights about doing analytics the lean way in an early stage company. The talk was aimed at companies that are just starting off or have raised its first couple of rounds of venture funds.
He discusses some of critical metrics and tools to use at different stage, and how to culminate an analytically-driven culture in the company right from day one.
5| Analytics: Decision Support for a New Age Bank By Anand K Sundaram
This talk by the head of analytics at Yes Bank was about how they strongly believed that analytics could drive a change in culture and help algorithm-based decision making to boost the objectives of an organisation.
He presented some interesting use cases across retail and business banking where they have made extensive use of analytics-based complex algorithms, backed by technology-driven execution tools to delight their customers and make a significant contribution in the business. The use cases have been picked from different product and business groups within the banking industry to understand how analytics and technology have changed the culture within banks.
6| Bringing The E In Analytics CoE By Sayandeb Banerjee And Felipe Aragao
This talk by Sayandeb Banerjee of TheMathCompany and Felipe Aragao of Anheuser-Busch InBev focussed on why organisations need to become largely self-sufficient and what can be the challenges on the way. An innovative analytical setup requires characteristics that allow for flexibility and dynamic nature to learn, experiment, produce solutions and create organisational value.
There are tough questions to be addressed – How should analytics be governed within the organisation? What investments should be made? How should problem-solving evolve in the organisation? How to experiment and innovate in analytics? What do you not do to achieve excellence? In this talk, they focused on the art in building centres and pinpointing key success factors.
7| Assuring Business Outcome Through Analytics By Pinky Sahu
In her talk, she talked about the rapidly changing market and technology which is becoming more volatile and less certain. The stresses on the importance of making faster decisions and executing the same.
Today the market and technology are rapidly evolving. The environment is more volatile, less certain or rather disruptive than in the past and because of these disruptions, organisations cannot rely on methods and assumptions that they would have followed about a few years back for business decisions.
Sahu gave an insight on how there is mounting pressure on the CXOs to optimise business outcomes for the best results. Often, they are burdened with faster decision-making on the strategy building.
8| Panel discussion: Analytics – Balancing ‘Advanced’ and ‘Actionable’
Paavan Choudary of Merilytics, Subramanian MS of BigBasket.com, Iqbal Kaur of Zylotech, Arindam Datta of WNS Global Services and Nidhi Pratapneni of Wells Fargo India were the eminent panellists.
Many analytics groups, organisations and professionals tend to correlate advanced analytics with better analytics, while stakeholders are primarily looking for actionable outcomes that generate sustainable value.
Even though this can be achieved through multiple approaches using various analytical methods and techniques, choosing the appropriate approach based on the analytical maturity of the organisation is critical so that the organisation can absorb and ‘operationalise’ the insights. The panel debated and discussed this common dilemma of balancing advanced analytics with actionable insights, that is faced while conducting analytical interventions to solve problems.
9| Analytics Roles In Organisation—Surviving and Thriving Through A Period Of Change By Ashish Singru
As the business applications of analytics have evolved over time, the role of an analytics professional has also steadily specialised along different themes. Broadly divided into the themes of business partnership and business audit, there are further sub-themes pertaining to roles in business operations, business planning etc.
In this talk by Singru, he discussed the roles that exist, and how in each role the analyst has to interact with different levels of hierarchy and functions within the organisations. Additionally, as the wave of automation, AI and predictive modelling hits both businesses and the analytics professionally, how does this classification of roles get impacted? Which types of roles will rise and become more relevant and which ones will fall and be taken over by machines? How should the analytics professional navigate their career in this climate, and be hopeful rather than anxious about their job relevance? And much more.
10| Big Data Vs Data Science: Machine Learning Practices In the Industry By Gopal Malakar
In this session by Malakar, he talked about how big data and data science are different, and how they are confused many times as being similar. He cleared many common doubts that people have regarding these technologies and answered many of these queries in this talk. The talk was about getting rid of the confusions and help understand the practicality of the usage. While there may be different professionals debating on the using technology in a certain way to achieve a result, there can be many different ways of accomplishing it.
He tells how big data is actually a technology stack whereas data science is about applying analytics to solve business problems. He also talked about the need for machine learning in the analytics industry as well as business goals, tools and techniques, and popular ML algorithms during his session.