Two talks: Knowing your digital customer; Forecasting in R

Hi Coders,
Our first meetup of the year will be hosted on May 3rd at Oliver Wyman Labs’ part of MMC Innovation Centre on Adelaide Road, Dublin 2.

Agenda:
18:30 - Meet and Greet
19:00 - Deborah O’Neill ‘Knowing your digital customer’
19:30 - Shalki Nagpal ‘Data Analysis and Forecasting in R’
20:00 - Q&A, Food & Drinks, Networking

Deborah O’Neill, Partner at Oliver Wyman

Businesses have long been investing in better understanding their customers. Whether it’s focus groups, telephone surveys, or Facebook tracking, it all adds up to a better understanding of who the customer is, what they want, and how can help them get it.
Despite this, there seems to be a lack of understanding of how one digital customer varies from the next in terms of their attitudes to key topics like data privacy or being part of an online trend.
CIOs and their teams should ask themselves: which Digital Families do my customers – online and offline – belong? Which Digital Families do my most profitable customers belong to? And which families do I see being the key to my future strategic successes? This will allow digital and tech teams to look at their goods and services from those perspectives – likely very different to their own world view – and build innovations around those particular needs and preferences.

Shalki Nagpal, BI and Data Analyst at Marsh.

With the boom in data & technologies, demand for the knowledge in statistical programming languages like R and Python have increased. R programming is the one of the easiest and strongest languages out there to resolve all the data conflicts. You can integrate R with almost all the BI tools and the databases (most popular being excel).
Resolving all the data quality conflicts is the toughest job at any point of project development. What are the most common data quality issues that we face in our day to day jobs and how do we resolve those? Dedicating this discussion to integrating of R with various BI tools and covering all the challenges faced with a brief intro on Forecasting (which I’m sure everybody is keen for).
Fully automatic forecasting of time series, based on model fitting and model comparing algorithms selecting the ’best’ model for the data at hand, provides a statistically well founded solution to the forecasting problem and can be of great use to the firm in obtaining accurate predictions for variables like sales, commodities’ input needs and the like, where forecast errors cost money.