Category: Data Science

A Weather Perspective On Problem Solving

The benefit of looking at a problem from a different angle

A Weather Perspective On Problem Solving
 

Incorporating different views or perspectives and combining knowledge from different disciplines can be very beneficial in academic study or in a business setting. It proves to be especially useful when finding ideas or solutions and can bring many new insights. During my internship at AlignAlytics last summer, I encountered several projects that demonstrated this, including a project I contributed to that focused on analysing car breakdown data for an automotive company.

The project specifically aimed to analyse the relationship of breakdowns to weather data. Using weather variables in addition to regularly available data such as; day of the week, holiday periods and reasons for breakdown to build a predictive model of call volume proved to be very successful. Individual variables were weakly predictive of the overall breakdown calls in a specific city, but the combination of several variables in a model gave good results.

When building this kind of model, it is important to think about the variables you are dealing with to try and understand the data. For the call volume model, for example, we explored the use of variables that contained weather data for the previous day. The reasoning for this was that if it snows a lot on one day, this might also affect the call volume on the next day, as perhaps people consequently choose to make their journey on a different day, or cars will have problems starting up the following day.

It is also important to think about the level at which the analysis is to be conducted. In this case, analysis and model building was performed on a city level, as the number of breakdowns and calls are likely to be very related to the situation in a city, such as the infrastructure and population size. The model also needs to be trained on an adequate amount of data, such as a whole year, so that different seasons are included.

Figure 1 shows a comparison between the two predictive models based on weather data and recorded call volume for the city Providence.

Predictive Model Weather Data

Figure 1 – Comparing random forest and multiple regression model for Providence

Taking a weather perspective can be relevant to a great variety of forecasting and modelling projects. In this case the model allows the prediction of car breakdown, which can help with planning the call responses in a city and to organise call centres. There are many other types of businesses that would benefit from modelling and forecasting based on weather conditions, such as businesses focusing on sales, outdoor related activities or the breakdown of machinery. Some interesting research has been conducted on this topic and it has been found that as exposure to sunlight increases, so does consumer spending (Journal of Retailing and Consumer Services).

Combining different disciplines – or ‘practising interdisciplinarity’ – was also a central theme in my Bachelor’s degree at UCL, fittingly called Arts and Sciences. It emphasised the importance of using knowledge, methods or skills from different disciplines when solving a problem or trying to reach a goal. Having experienced this in an academic setting, it was interesting to see this sort of combination in a company working in Data Science. I got an insight into how AlignAlytics achieves goals by trying out new things, combining people’s knowledge and skills and combining different technologies to optimise processes.

An example of this is their adoption of Google Cloud’s big query, which allows storage and querying of data. The Google Cloud Python Notebook gives the possibility to combine SQL queries, to get data from a table, and Python, to analyse this data, for example by using data frames. In the breakdown/weather analysis, the notebook made it possible to query the data for each city, and then train and test different models in Python (multiple linear regression and random forest model were used) to be able to forecast breakdown related calls. The model output could then be visualised in AlignAlytics’ visualisation software ‘Alytic’.

Figure 2 shows an Alytic – visualisation of cities used in the analysis. Their corresponding call volume (shown for 15th of April – it is possible to select specific dates on the Alytic chart) is represented by the size of the circle and the colour of the circle represents the maximum temperature.

Alytic visualisation of cities used in analysis. Weather & Predictive

Figure 2 – Comparing call volume between the cities

Data Science itself is already a combination of disciplines and skills, as it requires knowledge of data analysis and storage methods/technologies and knowledge on the topic or company the analysis focuses on, but many analyses could benefit from changing the way of looking at a problem. So, next time you are trying to solve an issue, or performing analysis, perhaps consider adopting a different perspective and have a look at the weather report.

Posted on January 17, 2018 by Danielle Mosimann

Analytics, Decision Making & Wine

As our society and economy has evolved, we’ve become accustomed to having an abundance of options in just about any decision we must make.  However, it’s the excessive alternatives we are constantly confronted with that often complicate and delay decision making in our personal and professional lives.  For example, I went out to dinner the other night and wanted to have a glass of wine with my meal.  The waiter handed me a book an inch and a half thick containing their vast array of wine selections. Instead of wading through the pages, I quickly came up with a set of criteria to help me focus and determine my selection.

wine-and-food-pairing-chart

To start, white and rosé wines were immediately eliminated. I only drink white wine if I’m eating fish. Since I knew that I wasn’t going to order fish, it was simple for me to eliminate the whites (I ordered a pasta appetizer and beef entrée in case anyone is interested). Rosé isn’t really my thing unless I’m at an outdoor party in the summer and it’s mixed with fruit (à la homemade sangria).

I then narrowed my selection according to the type of taste & texture I wanted to experience on this particular night, I was in the mood for a smooth, even balanced, medium bodied, but not too fruity taste. This criterion narrowed my quest to the great varietals of Pinot Noir and Chianti. Because I had ordered a pasta based appetizer, my search led me to select a glass of Chianti (this also went great with the wood fired Tuscan style bread and homemade olive oil).

Finally, I assessed the value and cost (this is often where most people start every decision, particularly in business). I selected a $13 glass which was about middle of the road for the Chianti price point range.  Boom! I just solved my wine selection problem in less than a minute using simple qualitative analytics and all I had to do was establish a core set of criteria that fit my personal needs.

Businesses should approach decision making in a similar fashion. By establishing a list of factors that matter to your organization today and that will also matter in the future, it will allow you to differentiate yourself amongst competitors and result in continuous growth.  Begin collecting data surrounding these factors, constantly evaluate the outcomes of your decisions and modify/tweak your approaches.  Let’s put this into some context.

Say for instance you’re an executive at a multinational manufacturer and part of your strategy is to strive for continual efficiency through operations.  You may decide to invest in multiple Business Intelligence (BI) tools in order to meet this strategic initiative. The question then becomes who, where, and how should your dollars be invested to maximize the greatest return? Again, an abundance of alternatives exist.

In order to solve this problem, the organization may decide to embark on creating a BI roadmap and assess factors that will determine the analytical capabilities of the current operation and where they should go in the future.  For instance, the manufacturer may want to assess the availability/timeliness of information. This factor will determine if the information is delivered to the users when required in order to do an effective job. Drilling down further, you may then assess the information’s relevancy. Does what I receive even matter in the context of my operating unit? If not, why would I continue to receive such information and what solutions are out there for me to resolve this issue would be typical follow up questions upon further evaluation. Asking simple yes/no questions such as “does the current technology allow me to view information in real-time?” can be just as insightful, particular for a manufacturing production facility.

Decision-making doesn’t have to be challenging or scary. If you take the time to set up a repeatable model, subject to regular evaluation and refinement, which fits your needs you can now begin to solve, simple (i.e. what am I going to eat for dinner tonight?) or complex (i.e. what new markets should we be competing in during the next 1, 3, or 5 years?) issues with greater speed and accuracy.

So, now that you’ve decided that analytical decision making is vital to your personal and professional success, let’s toast over a glass of wine (red preferably)!

Author: Gabe Tribuiani 

Posted on September 4, 2014 by Danielle Mosimann