Datamilk
Welcome to Datamilk
About us
Datamilk is an independent Sydney based bespoke data mining consultancy. We specialise in all aspects of data mining and predictive analytics.
Datamilk provides datamining services including programming, predictive modelling and business analytics. Our services span a range of areas including data cleaning, data management, regression models, logistic, ordinal and nominal models, time series and forecasting and more advanced techniques. We also run both public and onsite training courses in datamining techniques.
We specialise in solutions for small to medium sized businesses which typically don't have the in house resources to make the most of their data. We assist companies to identify the business issues they need to address, determine what data will be need to solve the problem, select the correct datamining technique, translate the results into a meaningful business decision and incorporate datamining into the way they do business.
Who's doing the work?
About Ross Farrelly
Owner Ross Farrelly (M. App. Stats., BSc(Hons), M. App. Ethics., Grad. Dip. Ed.) has many years experience in successful consulting, training and programming for clients around Australia and New Zealand. He is a certified advanced SAS programmer and also has experience with both R and Minitab.
See here for Ross's Linkedin profile.
What is datamining?
A Brief Introduction to Data Mining
Data mining is the process of extracting useful information which may be hidden in the data owned by your business - information which can help you make better business decisions.
For example, a retailer may wish to know which of his customers are most likely to take their business elsewhere so he can target them with a intervention to try to retain them. There are data mining techniques (known as cluster analysis) to identify these customers and to test which interventions are most likely to retain customers. A diary farmer may which to know which cows to keep to improve his herd and which to sell. Using data mining techniques known as classification analysis, the calves can be classified as keep or sell using the historical milk production data.
A mining company may need to predict the gold yield they can expect from an ore body. Based on historical records, an equation (or model) can be developed to allow accurate predictions of this type. This is known as regression modelling.
A marketing department might want to measure the effect of an advertising campaign. The customers are randomly divided into two groups: a treatment group which receives a promotional email and a control group which does not. The email are sent out and the responses measures. By comparing the spend of the treatment and control groups, which due to the randomized way in which they were selected are as alike as possible in all respects, we can measure the effect of the campaign.
The data mining process runs as follows. First the problem which needs to be solved must be clarified and then quantified. The data available is then collected, cleaned, checked and assessed to see if it contained the necessary information to solve the problem. The data is then divided into two sets: a training set and a test set. The model is built on the training set and tested for performance on the test set.
For example, if your task is to the number of magazines sold by your retail outlets each week. This is important to know accurately because it cost you if you send too many (the unsold magazines are returned) or if you send too few (you miss out on sales). You collect the data for the previous year showing the demographics of the stores and the number of magazines sold. You build the model on two thirds of the data. You then use the model (or equation) to predict how many magazines will be sold at each store on each day in the remaining one third of your data (which the model has not seen.) Since you know how many magazines were actually sold, you can calculate how good your model is at making these predictions. If it is better than your current methods for deciding on how many magazines to send you could start using the model to make your business more profitable - and you can also calculate how much the model will save you. As conditions change the model will need to be maintained and improved to ensure that it remain as accurate as possible.
These are just a few examples of data mining - there are many more. But the thing they all have in common is the use of data to improve business decision and make your business more profitable.
Case Studies
Datamilk has many examples of consulting and model building including case studies such as:
- predicting the number or offences likely to be committed by young drivers.
- predicting the future value of an unpublished manuscript
- predicting the probability of certain types of customers making an insurance claim
- predicting the yield from a gold processing plant.
For more details on these and other case studies please contact us.
Sandbox
In addition to traditional data mining and advanced predictive analytics, I have expertise in a number of other technologies including:
- Data visualisation using Google maps and Google visualisation api. See here for a beta example of this.
- Text analytics and sentiment mining using python. See here for an example of a simple sentiment miner which searches a web pages for a search phrase and calculates the sentiment in each sentence.
- R Programming. See here for an example of an R program I wrote to automatically create a visual pdf data dictionary of dataset.
- Unsupervised Clustering See here for the R code implementing K means clustering and an animation demonstrating the results.
For more details on these projects and how you can use similar technologies to benefit your business, please contact us.
Contact
To explore the possibilities which can be realised by working with Datamilk, contact Ross Farrelly on:
0433 449 800