23-24 November 2019
I had a great time attending Big Data Ldn, I would definitely recommend going if you have any interest in #bigdata and would like to learn more about the solutions available to managing data, deciphering data, and leading with data.
Structure:
The conference was laid out in booth + lectures styling. There were many parallel lectures going on at the same time, so going to all of them was not possible! I recommend grabbing a few friends, and each go to separate streams at the same time, this way you can cover more ground and content.
The booth style was great, there were hundreds of booths to visit, and each had been advertising their involvement in big data. This was a great opportunity to see key players in big data, as well as how other companies are leveraging big data into their own enterprise solution.
What I had enjoyed:
The talks were great and visiting each of the individual booths was incredibly valuable. I gained a wider scope of what is available in the market of AI + ML, data cleaning and data analytics technologies.
I enjoyed browsing around, and honestly I was very well pursuaded by freebies, free hats, free amazing socks, wireless earphones, adapters, etc... It was great! But also, in my treasure hunting, along the way I had many great talks, understood where my company was in their big data journey, and generally learnt more about big data, and its applications.
What did I learn?
Some of my key learnings just involved learning more about some technologies in the big data space. So below I will summarise through a select few techlogies / platforms I was keen on.
Alteryx
ML to make predictive models to discover what customers will be most likely to respond to a campaign. Then with A/B testing create targetted campains, that will drive higher conversion rates.
Big Data, AI, IoT
Expectation that there will be ~ 26 billiion IoT devices by the end of 2020. Big data in the industry: Industry, Mobility, Energy, Smart Home, Smart City...
90% of today's data was created 2-5 years ago.
Preventive -> predictive methodology for errors, aims to improve functions and efficiency. Utilitise big data and ML to create predictive analysis for errors, before they have a chance to ruin you.
Bosch
Building technology to enhance the quality of life. IoT focused, wants to become less of a production company, and more of a service company. They are heavily invested in research + development, manufacturing mobility, aggriculture.
Bosch is also working with insurance companies: the black box. So what has bosch done in this space? Working with driver assistance systems, how can automated systems create less road accidents. + How can bosche inprove their customer expereince: automated warranty diagnostics
Bosch is working towards a more renewable future. The world is moving to electricification, so drivers need to be able to find more accessibile car charge stations.
Another project is their farm eye, deep learning and computer visino for fruit harvesting.
Thoughtspot:
- Focus on the mass
- Search + AI [simple, smart, fast]
- Customer first
Customised consumer experience; the current business world is behind!
Not enough data scientiests for how much big data that we are processing.
Tyranny of averages hides valuable business insights
Search gives everyone fast and easy access to granular insights, fuzzy searching... but for analytics.
Human scale:
- Simple, interactive UI
- 'search speed' response times
- 100% precision, always
Enterprise scale:
- Number of users
- GDPR
Driving adoption - if it is not adopted fully, it is useless.
SpotIQ - churns data to deliver insights behind the scenes. Create instant insights.
Data exists at a granular level - so it should be manipulated in a granular way.
SpotIQ example - 7 insights by analysing ~24.7M rows in 7.44 seconds.
Allows for business users to use the tool:
No coding, pure insights from searching with human text.
Challenge complexity. Do not embrace it
Next steps
I am excited to try out some of the new technologies that I saw, and to see what I will be able to build with them!