Newton’s Laws of Motion and creating project momentum
Friday, 14th July 2017
Webinar: Solving the consolidation conundrum
Thursday, 25th May 2017
Webinar: Infor SunSystems tips and tricks – Query & Analysis – demonstration reports
Friday, 12th May 2017
Webinar: Infor SunSystems v6 tips and tricks – Business Unit administration
Thursday, 11th May 2017
First the history
Jacob Bernoulli, a prominent mathematician, born in 1655 in Basel Switzerland, developed a special form of the Law of Large Numbers (LLN), the original law being developed, but not proved, by Gerolamo Cardano.
According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.
What this means for data modelling and business intelligence?
The implementation of systems may involve the development of a data warehouse. This central repository of information that, once populated, is analysed and assessed enables the consumer of information to glean insights into the workings of the organisation.
By understanding the implications of the Law of Large Numbers, we are able to analyse the data and draw conclusions based on the information held. It is not that we can say the statistical analysis will tell us that future events will occur as they did in the past. Instead the larger the data set, the more confident we can be that the analysis gives us information about what has been happening, and from this we can focus our attention, for example on specific areas to improve organisational performance.
A real life example
Some years ago, I did an analysis of data to identify the lead time between the completion of work and the raising of an invoice to a customer. The purpose was to identify baseline metrics against which an element of a change programme (improving business processes) was to be tracked. By reducing the time to get invoices out to customers, payments could come in quicker thereby having a direct beneficial impact on the organisations cash flow.
On presenting the analysis back, there was much discussion as to whether these could be relied on due to people thinking of isolated single instances where this was not correct. Further probing showed there was a lack of skills in understanding data modelling techniques. For the change programme to be a success, the training strategy was enhanced. The training programme put together covered both how to use the business intelligence software and how to model data from which improved business decisions can then be taken.
At TouchstoneEnergy we recognise that projects are more than just about the technology. On developing the training strategy we need to assess individual skill levels. The application training is likely to be a given. Training on a subject area may also be required to help the individual understand the context they are working in. Without this consideration the return on investment from the programme or project can be significantly diminished.
By John Chapman