Entropy Analytics

Data Analysis,Data Analytics,Data Visualisation,Business Analysis,Data Analysis,Dashboards,R,Tableau,Python,SQL,Data Cleansing,Predictive Analytics,Machine Learning,Data Science,Exploratory Data Analysis,Statistical Analysis, ,

Ask yourself, what problem have you ever solved, ever, that was worth solving? Where you knew all of the given information in advance? Where you didn’t have surplus information and have to filter it out, or you didn’t have insufficient information and have to go and find some?

At Entropy Analytics, we believe that most complex business problems can be broken down into a series repeatable steps using data science methodology, in which the business questions come first and the data follows the question.

DEFINING THE BUSINESS QUESTION Before data analytics work can start on a data set, a business question of interest needs to be established. Whether this be a current business problem, or information, knowledge or insights that are being sought out, the more clearly the business question is defined, the greater the effectiveness of the data analytics work.

DEFINING THE IDEAL DATA SET In order to answer the question, it is necessary to state what the ideal data set is. What can theoretically answer the question may not exist in reality, and may need to be sourced from a variety of data sources, then cleaned and integrated in order to answer your business questions.

OBTAIN THE DATA In order to use the data to answer business questions, the data must be accessible. In reality, it is common that the ideal data set is not readily available. Data often comes in a variety of formats and sources in an organisation, such as relational databases, spreadsheets, text logs or even websites.

CLEAN THE DATA Entropy Analytics understands one of the fundamental aspects of data science is to obtain data from any source, no matter how unstructured or messy the data is, and try to process to prepare it into a clean and usable data set that can be used to answer high priority business questions.

EXPLORATORY DATA ANALYSIS Once the data has been obtained and cleaned, exploratory data analysis can be conducted to get an understanding of the data set. This might include data visualisation or data mining techniques to recognise patterns that were previously unknown in the data set.

DATA ANALYTICS / PREDICTIVE MODELLING / STATISTICAL ANALYSIS Once the data has been obtained, cleaned and fully explored, the data analytics phase to answer the business question can commence. This might take various forms, depending on the business problem, but can range from statistical analysis, predictive modelling, machine learning and data visualisation.

INTERPRETATION OF RESULTS AND DISTRIBUTING INSIGHTS Once the results are obtained, they can be distributed to relevant business stakeholders through reports, presentations or interactive graphics.

CONTACT US If your organisation has a business problem that can be solved with the above data science methodology, please contact us.