Data Analytics, Data Science, Applied Statistics, AI, Big Data, .... whatever you want to call it. Ultimately, it is a range of techniques used to extract insights and information from data. More importantly it needs to be pragmatic and relevant so that its actionable to it's purpose.

Techniques:

Data Visualisation (Reporting and Dashboards with Power BI and Tableau)

Advanced Analytics & AI (Predictive/Propensity modelling, customer Lifetime Value model)

Customer Segmentations

Web Analytics with Google Analytics & Google Tag Manger

Underpinning analysis is the need to gather, merge, manipulate and transform data.

Current Toolsets:

Python (Pandas, Jupyter notebooks, Stats & tensorflow packages )

SAS

SQL

R

Cloud analytics and data processing provides a number of benefits with the agility and flexibility, deployment, and scalability.

Current platforms:

AWS

Google Cloud

An intermediary ETL platform provides the glue between cloud platforms such as CRM and MAs

API development with a central ETL database to provide cleaning, transformation, de duplication and summarisartion of data records between platforms

Solution:

Cloud SQL, with python processes, and cloud storage redundancy