This is an excerpt from the white paper available here.
Over the last few years the data industry has been shaken to its core. We have new names, roles, technologies, products coming out on a daily basis. The term “Big Data” has been overused so much that it may be losing some of its meaning.
I am meeting people on a regular basis and the message I receive over and over again is that it’s overwhelming. I am going to try to address this concern in this paper.
The Purpose of Data
The sole purpose of data in an organisation is to support business decisions. Data can do this in several ways.
- By communicating information about the past
- By communication information about the future
- By recommending actions for future success
The first of these has long been addressed by traditional reporting and business intelligence tools so I will not spend too much time here. What I really want to address is the final 2 points:
- Communication information about the future
- Recommending actions for future success
There are several ways that data can help us peek into the future of our organisation. The first and most traditional is the statistician. The statistician is the professional that can look at the data and give you an inference about the future based on the data available. The statistician, especially one that is highly knowledgeable about the business can use his domain expertise and data experience to give extremely valuable insights into the future.
The second way of getting real future value from data is to use Predictive Analytics. Predictive Analytics is also known as Advanced Analytics, Prescriptive Analytics and Machine Learning but I suggest we just call it Predictive Analytics as it clearly summarises the objective of the technology.
Predictive Analytics is about value. It is how you convert your data into real insights about the future. It is not a product, it is not a platform, and it is not Hadoop or any other vendor name. Predictive Analytics is solely the science of predicting the future from historical data.
Predictive Analytics is also not a person. This is an important point because Joe the statistician cannot handle an Excel file of 2GB and ask him to bring in Facebook, Twitter and web-traffic data into his inferences and he’ll probably have a nervous breakdown.
There is only so much data a human head can manage. Computers however do not have this problem; they can handle any vast amounts of data from any variety of sources. They also do not bring any biases to the analysis which has also been a problem in the past.
How to “Predictive Analytics”
Until recently, implementing a Predictive Analytics project has been the domain and capacity of large and sophisticated companies, however, most recently with the emergence of affordable cloud computing, in memory computing analysis, sophisticated modelling tools, combined with the skills of computer programmers, data scientists and analysts, Predictive Analytics is now affordable as a service, by most medium size enterprises.
The solution can be procured as a service i.e. on/off, pay as you go and when you needed it. No longer is huge capital investment required but instead, understanding the need, the challenge, developing proof of concepts and analysing outputs, provide the effective and affordable introduction to the benefits of Predictive Analytics.
Predictive Analytics can predict numerous variables supported by your historical data. The following are some examples:
- Potential success of a marketing campaign
- How best to segment customers
- What marketing mediums have the best ROI
- When will my machine fail
- Why did my machine fail
As long as in the past we have recorded our actions and we also have at a later date recorded the results we can then learn form that data and make predictions about the future.
Predictions can be real time or can be on weekly/monthly/quarterly basis, it all depends on your needs.
There are several ways to get started. You can recruit your very own Data Scientist. Not an easy task considering the high specialisation of these professionals but it is what a lot of companies are doing.
You could also use a service provider. A good service provider will have a team of IT and data people including Data Scientists that have experience in doing these types of projects for customers in your industry.
At PicNet we always recommend that our customers start with a proof of concept. Some of these Predictive Analytics projects can take a long time to implement and integrate back into the processes of the organisation so it’s always best to take small bites and see if there is value to be had. We usually go for a 2-4 week project which usually goes something like this:
- We get potential questions about the future that management would like answered
- We audit all data and its quality in the organisation
- We prepare and clean this data
- We get external data if it can help the prediction (census, web traffic, Facebook, Twitter, weather, etc.)
- Build a simple predictive model, perhaps on a subset of the data
- Provide a report with predictions for the next period (2 months for instance)
This report can then be used by the business to test the value and accuracy of the predictions. If the business approves we then push that system into production. This will mean that you may get weekly, daily or real time predictions as the business requires. The length of these production implementations vary greatly depending on the systems that need to be integrated with and many other factors.
As a manager charged with the responsibility of curating your organisations data resources you should forget vendors and platforms and no sql databases, forget about the 3 Vs (or is it 4?) of big data. As a manager your only concern should be the only V that matters and that is Value. And if you want to get value from your data then consider Predictive Analytics.
For any additional information please feel free to contact me on my details below.