Switching focus back to a series of technical blog posts, over the next 5/6 blog posts (there may be some Web Summit updates intertwined!) I aim to demystify “all things data”, to include reporting – analytics – data science – business intelligence, key difference and dependencies between these terms, explore an introduction to where machine learning fits into your data model in your company. Governance, security and data management will also be covered.
To begin, a short post with 10 perspectives that will get you thinking. (hopefully!)
1: Big Data is just a tool.
2: Analytics is utilized by Data Science and Business Intelligence
3: Data is never clean. You will spend more of your time cleaning and preparing data (up to 90%) than anything else.
4: 90% of tasks do not require deep machine learning
5: More data beats a cleverer algorithm
6: Data Science + Decision Science + Analytics = Business Impact
7: You should embrace the Bayesian approach
8: Academia and Business are two different worlds – know this.
9: Presentation/Visualisation is key (know your audience)
10: There is no fully automated Data Science. You need to get your hands dirty.